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Abbiamoannunciato un rafforzamento della partnership strategica tra Commvault e HPE – one grounded in a shared belief that data protection and cyber resilience needed to evolve alongside modern infrastructure.

And if you were in the room for Antonio Neri’s keynote or watched it online, you might remember Commvault being called out on stage.

At the time, it felt like a strong statement of intent.

Today, heading back to Las Vegas, it feels like something more:

Execution. Momentum. And a real opportunity to build modern, resilient IT for customers..

What’s changed in the past year

In the last twelve months, the conversations we’re having with customers have shifted – but so has the environment they’re operating in.

Yes, data is growing. Yes, AI is accelerating. And yes, you absolutely need to have a resilience plan for AI.

But what’s also changed is the nature of the risk.

We’re now entering what many are calling the age of frontier AI – with advanced models like Mythos fundamentally changing how quickly vulnerabilities are discovered and exploited.

You may have seen that in un recente annuncio di Commvault, we highlighted how these models are compressing what used to be weeks-long exploitation cycles into minutes, dramatically shrinking the window organizations have to respond or recover.

Attacks are becoming more automated, more autonomous, and more immediate.

Which means what you thought you knew might not apply anymore:

  • That you’ll have time to patch before something is exploited
  • That recovery can happen “after the fact”
  • Quella copia di sicurezza è sufficiente

That’s what’s really changed.

It’s why the conversations we’re having today – with customers, with partners, and across the industry – are less about if something happens and more about how quickly you can recover when it does.

And it’s also why the joint innovation with partners like HPE – bringing to market differentiated new solutions that solve real customer challenges and strengthen our cyber resilience portfolio – is so incredibly valuable.

Tre ambiti in cui questa collaborazione si è evoluta

If you step back and look at the past year of this partnership, I’d group our progress with HPE into three clear areas.

#1 – Una maggiore integrazione tecnica proprio dove conta di più

We’ve moved well beyond production and protection across storage infrastructure to run-time platforms. So not only do we enable simplified snapshot management and faster recovery across HPE storage technologies like HPE Alletra Storage MP or HPE StoreOnce, we don’t stop at the storage layer. A great example of that is agentless protection for virtual machines (VMs) managed through HPE Morpheus Software.

Virtualization is in a period of real disruption right now. Customers aren’t just evaluating alternatives – they’re actively migrating. And that introduces risk.

What we’ve focused on is helping to make sure protection doesn’t break and can remain consistent during (and after) those transitions.

Agentless protection adds another layer to simplify that – removing dependencies that can slow down or complicate migrations, while helping keep VMs protected across environments.

This level of integration up the stack means customers can accelerate their VM migration strategy confidently and on their own terms, translating to better operational agility, reduced risk, and greater cost-savings.

#2 – Un maggiore allineamento unificato nella strategia di commercializzazione – e una soluzione di resilienza più completa per i clienti

Il secondo cambiamento ha riguardato il modo in cui ci presentiamo sul mercato insieme – e ciò che offriamo ai clienti come suite di soluzioni integrata. Gran parte di ciò è dovuto al ruolo diHPE Zerto Software from Commvault.

By integrating HPE Zerto more deeply into Commvault Cloud, we’ve strengthened our platform with continuous data protection and workload resiliency and mobility that enables customers to modernize platforms and rapidly recover workloads to keep their business running after operational disruptions.

And more recently, we introduced a game-changer with Commvault Flex built on HPE infrastructure, una soluzione full-stack che si fonda su:

  • HPE Alletra Storage MP X10000: soluzione di storage all-flash ad alte prestazioni per il ripristino accelerato di dati oggetto e file
  • Server HPE ProLiant Compute per un’elaborazione sicura e di livello aziendale
  • And an industry-leading cyber resilience platform that’s flexible and scalable enough to take advantage of that performance.

Flex solves customers’ challenges in protecting data-intensive workloads like multi-petabyte data lakes that power AI and analytics applications. With Commvault Flex built on HPE technology, customers get an integrated solution that accelerates recovery, simplifies deployment, scales easily, and can help them meet their resilience objectives and recovery SLAs for the foundational data that powers their business.

One more area that’s really come into focus for us over the past year is GreenLake by HPE. As customers push harder into AI, one thing that becomes clear pretty quickly is how infrastructure is delivered and consumed matters just as much as what’s powering it under the hood. There’s a growing need for environments that can scale, adapt, and evolve alongside these AI workloads without adding more complexity. That’s where GreenLake becomes such an important part of the conversation. It’s not just a platform – it’s how many customers are starting to think about building AI-ready infrastructure and become an agentic enterprise. For us, that means doubling down on how Commvault shows up in that ecosystem, continuing to invest in tighter integration and an optimized experience. It’s an area we’re really excited about, and one where you’ll continue to see both teams pushing forward together.

#3 – Risultati concreti ottenuti dai clienti a conferma della direzione intrapresa

The third area – and probably the most important – is what we’re seeing in customer environments.

We’re starting to see this architecture land in meaningful ways.

For example:

  • A large European bank leveraged the combined Commvault and HPE solution to strengthen cyber resilience across mission-critical banking systems – while also supporting regulatory requirements like DORA compliance. What made the difference here was the combination of Commvault’s architectural advantages and tight integration with the high-performance HPE Alletra Storage MP X10000, enabling the customer to meet recovery objectives that other solutions couldn’t match.
  • Un’importante organizzazione di gaming online in Sudafrica ha intrapreso un percorso leggermente diverso, concentrandosi sulla disponibilità e sul tempo di attività della propria platform. In questo caso, l’integrazione di HPE Zerto nella più ampia offerta di Commvault ha consentito una replica continua e un ripristino più rapido, supportando un ambiente ad alta disponibilità in cui anche brevi interruzioni hanno un impatto sul business. Il cliente ha ottenuto un’offerta di resilienza più completa, fornita end-to-end da Commvault per un processo di approvvigionamento e assistenza più snello.

Different use cases – but a common theme:

Customers aren’t just buying backup anymore. They’re investing in resilience as part of their production architecture.

Perché l’infrastruttura ibrida è più importante che mai

If you zoom out, the pattern is clear.

AI workloads are amplifying everything. There’s more data, cycles are faster, and there’s less tolerance for disruption.

And increasingly, the limiting factor isn’t compute – it’s data: How quickly it can be accessed, how efficiently it can be moved, and how fast it can be recovered when something goes wrong.

That’s why platforms like the HPE Alletra Storage MP X10000 are playing a bigger role in these conversations – high performance, scale-out storage that can scale to meet extreme capacity and throughput demands. And when it’s integrated in a solution like Commvault Flex, it creates something that’s increasingly important – a protection and recovery layer that can actually keep up with AI.

In vista dell’HPE Discover

Heading into this year’s event, there’s a different energy.

A year ago, we were talking about what we could build together.

Now, we’re seeing:

  • Una maggiore integrazione tecnica
  • Un allineamento più chiaro nella strategia di lancio sul mercato
  • E risultati concreti ottenuti dai clienti che confermano la validità di questo approccio

There’s still a lot of work ahead. But it feels like we’re at one of those points where things start to really take off.

Because the reality is simple:

AI doesn’t wait.

And increasingly, neither can your recovery strategy.

If you’re going to be at HPE Discover 2026, I’d encourage you to stop by and take a look.

Have a conversation with our team at our booth.  Take in a demo. Attend our breakout session. Or setup a meeting with our exec teams for a deeper dive.

I can’t wait to see you there – and to see what all this incredible momentum brings in the coming year.

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There are a lot of conversations happening right now about cyber resilience. Most focus on technology: Detection speed. Recovery architecture. AI-enabled security operations.
All of those things matter. But after sitting down with Dr. Erika Voss, SVP, Global Chief Security & Data Officer at Blue Yonder, and Sam Archey, VP of Trust at Blue Yonder, I kept coming back to something much more fundamental: Trust.
Not trust as a slogan or marketing message, but trust as something operational. Something built deliberately over time and tested in the moments when organizations are under the most pressure.
That distinction can matter because resilience today isn’t just about recovering systems. It’s also about how organizations communicate, how they lead, and how they maintain confidence while uncertainty is still unfolding.
And for a company like Blue Yonder – operating at the center of global supply chains – that challenge becomes even more visible.
Watch the episodio.

Punti chiave: Cosa richiede realmente la fiducia informatica moderna

  • Trust is built through consistency, not perfection. Customers don’t typically expect immediate answers, but they do expect transparency and follow-through.
  • La resilienza è una questione operativa, non teorica. La comunicazione, il coordinamento e i processi decisionali possono essere importanti tanto quanto i controlli tecnici.
  • Le solide relazioni instaurate prima di un incidente possono determinare l’efficacia della risposta dei team nel corso dello stesso.
  • La resilienza della catena di approvvigionamento può aumentare la posta in gioco, poiché le interruzioni si propagano a catena attraverso ecosistemi interconnessi.
  • Organizations are increasingly judged not on whether incidents happen – but on how they respond when they do.

Resilienza e fiducia

One thing became clear very early in this discussion: Erika and Sam don’t think about resilience as a standalone security function. They think about it as a trust function.
Most organizations still separate these ideas:

  • Il reparto sicurezza si occupa della risposta tecnica.
  • Il reparto Comunicazioni gestisce la messaggistica.
  • La leadership interviene quando è necessario intensificare gli sforzi.

But what Blue Yonder has built is much more integrated than that. Their approach recognizes that customer trust is shaped in real time by operational behavior – not just technical outcomes.
And in a supply chain environment, where countless organizations are interconnected, that operational behavior can become incredibly visible. When something breaks inside that ecosystem, the impact rarely stays isolated. 

Il momento in cui la fiducia viene davvero messa alla prova

One of the strongest themes throughout the conversation was how quickly trust can be lost – and how intentional organizations must be to preserve it.
Erika put it bluntly: Customers are no longer evaluating whether companies experience incidents. That’s become table stakes in the modern threat landscape. What they are evaluating is something much more specific: Did they hear it from you first?

That distinction changes how organizations should think about incident response.
For years, the instinct during cyber events was often to hold communication until every detail was verified. But the reality today is that silence can create uncertainty faster than almost anything else.
Customers don’t typically expect complete answers in the first hour. They want acknowledgment. They want presence. They want to know that someone is actively working on the problem and willing to communicate transparently while things are still unfolding.
That’s where operational trust is built. And according to Erika and Sam, those first 60 minutes can matter more than most organizations realize.

Anteprima: la fiducia è fondamentale in una crisi

In this moment from the STRIVE conversation, we discuss how the first 60 minutes of response can determine customer confidence, reduce propagation delays, and shape long-term business relationships.

Creare fiducia prima che se ne abbia bisogno

The trust Blue Yonder has built with customers wasn’t created during a single crisis. It was built through repeated interactions over time – through transparency, responsiveness, and operational discipline long before pressure entered the equation.
The same applies internally.
One thing both Erika and Sam emphasize is the importance of relationships between teams before incidents occur. Security, communications, engineering, operations, and leadership need to know how to work together ahead of time. Otherwise, the first real test of collaboration happens during a crisis, which can be the worst possible moment to establish operational alignment.
That’s why they spend so much time focusing on process maturity, stakeholder engagement, and tabletop exercises.
Not because those activities are theoretical. Because they create familiarity.
And familiarity helps reduce friction when pressure rises. 

Perché le esercitazioni teoriche sono più importanti di quanto la maggior parte delle organizzazioni creda

There was a particularly practical section of the conversation around tabletop exercises that I think a lot of organizations need to hear.
Too often, tabletops become compliance activities. Something organizations run once or twice a year to satisfy requirements and move on from. But the way Blue Yonder approaches them is much more operational.
For them, tabletops are rehearsals for coordination.

  • Chi prende le decisioni?
  • Come avviene l’escalation?
  • Quali partner esterni devono essere coinvolti?
  • In che modo interagiscono i settori legale, della comunicazione e dell’ingegneria?

Those questions become incredibly important during live incidents. And if teams haven’t worked through them ahead of time, response can slow down immediately.
Sam described how teams begin to understand what it may actually feel like to be pulled into an incident under pressure. That experience matters because it helps build muscle memory – not just for technical teams, but for leadership and operational stakeholders as well.
The organizations that recover most effectively are rarely improvising everything in real time. They’ve practiced.

Il lato umano della resilienza

What I appreciated most about this conversation was how grounded it was in the human reality of resilience work.
Cyber resilience often gets framed entirely through technology. But people often still determine outcomes.

  • Come comunicano i leader.
  • Come collaborano i team.
  • Come si comportano le organizzazioni quando le informazioni sono incomplete.

Those factors help shape customer trust just as much as recovery timelines or technical controls do.
And perhaps the most important lesson from Erika and Sam is that trust isn’t earned during easy moments. It’s earned during uncertainty. During ambiguity. During the moments when organizations have to choose transparency over silence and consistency over perfection.

Guarda l’episodio completo

In this discussion, you’ll discover:

  • Come Blue Yonder trasforma la fiducia dei clienti in risultati concreti.
  • Perché la costanza può essere più importante della perfezione immediata.
  • Il ruolo della comunicazione durante gli incidenti informatici.
  • In che modo le esercitazioni teoriche rafforzano la resilienza.
  • Perché gli ambienti della catena di approvvigionamento possono modificare la posta in gioco nel recupero informatico.

Watch now.

Domande frequenti

Q: Why is trust so important in cyber resilience?

A: Because customers increasingly evaluate organizations based on how they respond during incidents, not simply whether incidents occur.

Q: What does “trust as an operating model” mean?

A: It means trust is continuously reinforced through operational behavior, communication consistency, and transparency – not just during crises.

Q: Why do the first 60 minutes of response matter so much?

A: Early communication can shape customer perception, help reduce uncertainty, and help establish credibility during rapidly evolving situations.

Q: How do tabletop exercises improve resilience?

A: They help teams rehearse coordination, escalation, and communication processes before real incidents occur.

Q: What is the biggest lesson from this discussion?

A: That resilience is typically deeply tied to operational trust, and organizations should build that trust before they need it most.

Q: How can organizations help improve customer trust during incidents?

A: By communicating consistently, prioritizing transparency, and building strong internal coordination long before a crisis begins.

Chris Mierzwa is Senior Director, Portfolio Marketing, at Commvault.

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Punti di forza

  • Cyber resilience in MEDITECH environments goes beyond backup and recovery; it focuses on maintaining care delivery and operational continuity during disruptions.
  • Healthcare organizations face significant ransomware risk, making rapid, reliable recovery essential for clinical operations.
  • Traditional data protection approaches often fail to address the complex dependencies between clinical systems, applications, and workflows.
  • Effective recovery strategies must coordinate the restoration of interconnected systems to minimize downtime and operational impact.
  • Commvault’s MEDITECH-focused approach helps combine snapshot-based protection, automated recovery workflows, and recovery visibility to strengthen resilience and preparedness.

When ransomware or operational disruption impacts clinical systems, the effects can ripple quickly across the organization, disrupting workflows, slowing staff productivity, and putting timely care delivery at risk. In these moments, the ability to recover quickly and confidently becomes just as important as preventing the disruption in the first place.

That is why Commvault’s approach to protecting MEDITECH environments is centered on recoverability, resilience, and operational readiness, not just data preservation.

Healthcare remains one of the sectors most heavily targeted by ransomware. In a MEDITECH environment, downtime can interrupt medication workflows, delay access to diagnostic information, and force staff into manual workarounds that increase both risk and complexity. In this context, a strategy that looks good on paper is not enough. Health systems need confidence that recovery will perform under real-world pressure.

That is where Commvault can make a meaningful difference.

Why Traditional Data Protection Is Not Enough for MEDITECH

Many organizations still rely on data protection approaches built for general IT environments rather than the operational realities of healthcare. MEDITECH recovery requires an understanding of application interdependencies, restoration order, validation checkpoints, and the need to bring clinical systems back online with minimal disruption.

A successful recovery strategy must account for more than simply restoring data. It must support the coordinated recovery of critical systems, applications, and workflows that clinicians depend on every day. Commvault helps organizations navigate that complexity with resilient architecture, streamlined recovery workflows, and greater visibility into recovery readiness.

How Commvault Helps Protect MEDITECH in Practice

Commvault’s approach to MEDITECH protection is designed to align with the operational realities of these environments. Rather than relying on a one-size-fits-all backup model, the solution helps organizations capture application-consistent protection points for critical MEDITECH workloads while helping minimize disruption to production operations.

This architecture provides healthcare organizations with a practical path to faster, more confident recovery. Snapshot-based protection can support rapid restoration for operational resilience, while longer-term backup retention strengthens options for audit, compliance, and broader cyber preparedness. The result is a model that supports both day-to-day recoverability and resilience planning for more severe disruption.

What Cyber Resilience Can Look Like in Practice

When Ransomware Strikes Overnight

Consider a regional hospital facing encryption activity overnight. In that scenario, the ability to recover from immutable backup copies and execute a structured restoration plan can be the difference between prolonged downtime and a controlled recovery. Commvault helps organizations reduce that risk with secure recovery options designed to restore critical systems quickly and cleanly.

When Backup Infrastructure Is Targeted

Attackers increasingly attempt to compromise backup infrastructure before launching ransomware. That makes architectural resilience essential. With immutable protection and isolated recovery options, Commvault helps enable clean recovery points to remain available even when adversaries gain access to production systems.

When Proof of Recovery Is Required

Cyber insurers, auditors, and compliance stakeholders increasingly want proof that recovery capabilities are tested, documented, and operationally sound. Commvault supports that readiness with validation workflows, reporting, and evidence that can help healthcare organizations demonstrate resilience before an incident occurs.

How Commvault Supports MEDITECH Resilience

Commvault helps organizations protect MEDITECH database volumes with application-consistent recovery points, providing a stronger foundation for restoration when clinical systems are impacted.

Recovery Designed Around MEDITECH Dependencies

MEDITECH recovery often involves complex relationships between systems and databases. Commvault’s approach helps support coordinated protection of critical workloads and a recovery model designed to help bring systems back online in the appropriate sequence.

Validated Recovery with Flexible Retention

By combining rapid recovery options with longer-term backup retention, Commvault helps healthcare teams strengthen resilience beyond the initial snapshot window and build a more complete strategy for recovery testing, validation, and preparedness.

Greater Visibility into Recovery Readiness

A strong MEDITECH resilience strategy depends on operational clarity. Commvault helps teams centralize protection workflows, improve visibility into recovery readiness, and simplify the management of critical data protection tasks.

Regulatory and Insurance Readiness

From documented recovery workflows to retention strategies that support audit and compliance discussions, Commvault helps healthcare organizations strengthen compliance documentation and demonstrate a more mature resilience posture.

Why Implementation Matters

Successful resilience in a MEDITECH environment depends on more than selecting the right platform. It also requires alignment with validated deployment requirements, infrastructure compatibility, and a protection design that reflects how MEDITECH systems operate in the real world.

For healthcare organizations, that implementation discipline can be just as important as the recovery technology itself. A well-designed resilience strategy helps enable teams to execute recovery processes as expected when they are needed most.

Why Now

Ransomware threats continue to evolve, and attackers increasingly target backup infrastructure before deploying encryption. At the same time, cyber insurers and compliance stakeholders are asking for evidence of tested recovery capabilities, not just installed tools.

For healthcare organizations running MEDITECH, the need to build resilience before an incident occurs has never been more urgent. Investing in recovery readiness today can help organizations better protect operations, accelerate recovery, and reduce the impact of disruption when it matters most.

In healthcare, recovery readiness ultimately comes down to trust: trust that critical data is protected, trust that systems can be restored in the right order, and trust that resilience has been tested before a crisis occurs.

That is the standard Commvault helps organizations pursue in MEDITECH environments, and the foundation for a stronger, more confident approach to cyber resilience.

Organizations can further strengthen that foundation by working with a Commvault Managed Service Provider that specializes in healthcare. In addition to the technology itself, healthcare teams gain access to expertise that can help align protection strategies to MEDITECH requirements, support implementation with greater confidence, and improve ongoing operational readiness.

For healthcare organizations navigating the complexity of MEDITECH, that combination of resilient technology and healthcare-focused expertise can help accelerate preparedness and improve recovery outcomes when they matter most.

Final Thoughts

Recovery in a MEDITECH environment is about more than restoring systems. It is about restoring the clinical workflows that caregivers rely on to deliver patient care.

As ransomware threats continue to evolve and healthcare organizations face increasing pressure to demonstrate operational resilience, recovery readiness can no longer be treated as a compliance exercise or a backup strategy alone. Organizations that prioritize recoverability, validation, and cyber resilience before an incident occurs are better positioned to reduce disruption, protect patient care, and recover with confidence when it matters most.

Learn how Commvault helps healthcare organizations strengthen MEDITECH resilience, accelerate recovery, and build greater confidence in their ability to withstand cyber disruption. Visit ourMEDITECH documentation site.

Domande frequenti

Q: Why is cyber resilience especially important for MEDITECH environments?

A: MEDITECH environments support critical clinical and operational workflows that directly impact patient care. Cyber resilience helps healthcare organizations recover quickly from disruptions while maintaining essential services and minimizing care delivery interruptions.

Q: How does cyber resilience differ from traditional backup and recovery?

A: Traditional backup and recovery focus primarily on restoring data after an incident. Cyber resilience expands that focus to include operational continuity, rapid recovery, and proactive measures that help reduce the impact of disruptions.

Q: Why are traditional data protection solutions often insufficient for healthcare organizations?

A: Many traditional solutions are designed for general IT environments and may not account for the complex interdependencies between healthcare applications, systems, and workflows. As a result, recovery can be slower and more disruptive.

Q: What challenges do ransomware attacks create for healthcare providers?

A: Ransomware can disrupt access to clinical information, delay care delivery, and increase operational complexity. Healthcare organizations need recovery solutions that enable fast restoration and confidence in recovery outcomes.

Q: How does Commvault support MEDITECH protection and recovery?

A: Commvault’s approach is designed around healthcare operational requirements, providing application-consistent protection, automated recovery processes, and visibility into recovery readiness to help reduce downtime.

Q: What benefits do snapshot-based protection and automated recovery provide?

A: Snapshot-based protection can support faster restoration of critical systems, while automated recovery workflows help streamline recovery efforts. Together, they improve operational resilience and strengthen preparedness for future disruptions.

Chris DiRado is Principal, Product Experience, at Commvault.

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When we talk about cyber resilience, the conversation usually centers on technology: tools, platforms, automation. All of that matters. But when something goes wrong, those aren’t the things that determine how well an organization responds.

People are.

In this episode of STRIVE, I sat down with Dr. Jessica Barker, co-CEO of Cygenta and a leading expert on the human and psychological aspects of cybersecurity. I asked her to discuss a part of resilience that doesn’t always get enough attention – the human side.

What happens when pressure rises, when decisions have to be made quickly, and when teams are forced to work together in ways they may not be used to?

Watch the episodioto find out what she had to say.

Key Takeaways: What the Human Side Reveals

  • Technology doesn’t fail alone – people and processes are always part of the outcome.
  • Confidence under pressure comes from preparation, not instinct.
  • Clear decision ownership helps reduce hesitation during incidents.
  • Trust between teams helps accelerate response and recovery.
  • Culture plays a measurable role in resilience – it’s not just tools or architecture.

When the Plan Meets Reality

Every organization has a plan. It’s documented, reviewed, and often approved at the highest levels. But the real test isn’t how that plan reads – it’s how it holds up when people are under pressure.

Because that’s when things change. Decisions don’t always follow the script. Communication isn’t always clean. Priorities shift in real time. And in those moments, resilience becomes less about process and more about behavior.

Sneak Peek: Cyber Resilience as a Cultural Norm

In this moment from the episode, Dr. Barker highlights the importance of aligning cybersecurity with organizational values. Rather than positioning security as a blocker, resilient organizations embed it into culture – as an enabler of productivity, positivity, and business growth.

The Role of Confidence

One of the most consistent themes in this conversation is confidence. Not confidence in the tools – confidence in the people using them.

Teams that perform well during incidents aren’t guessing. They’ve seen similar scenarios before. They’ve practiced. They understand how to respond, even when conditions aren’t ideal.

That confidence shows up in small ways with potentially faster decisions, clearer communication, and less second-guessing. And over time, those small differences can add up to a significantly stronger response. 

Decision-Making Under Pressure

When something goes wrong, speed matters – but clarity matters more.

  • Who can make decisions?
  • What authority do they have?
  • When should they escalate?

If those answers aren’t clear, teams hesitate. And hesitation creates gaps. One of the most important parts of resilience isn’t just defining processes – it’s defining decision ownership. When people know where they stand, they tend to act faster and with more confidence.

Trust Is the Multiplier

Technology can help enable better and faster responses, but trust can accelerate them. In most organizations, teams operate in their own lanes. Security focuses on threats, infrastructure focuses on systems, and operations focuses on recovery.

That separation works – until an incident forces everyone together. That’s where trust becomes critical. Teams that trust each other:

  • Share information more freely.
  • Collaborate more effectively.
  • Focus on outcomes instead of ownership.

Without that trust, even well-designed processes can break down.

Why Preparation Still Matters

It’s easy to assume that strong individuals can carry a response. But even experienced teams rely on preparation, such as tabletop exercises, simulations, cross-team drills, and so on. They’re what build the muscle memory that teams rely on when real incidents occur. Without that preparation, even the most capable teams are forced to improvise.

Guarda l’episodio completo

In this episode of STRIVE, we explore:

  • How human behavior impacts incident response.
  • Why decision clarity matters under pressure.
  • What separates confident teams from reactive ones.
  • How culture influences recovery outcomes.
  • Where organizations should focus to strengthen resilience.

Guardalo subito.

If you’re thinking about resilience beyond technology, this is a conversation worth your time.

Domande frequenti

Q: Why is the human side of resilience important?

A: Because technology alone doesn’t determine outcomes – people do. Their decisions, communications, and executions under pressure are the keys to success.

Q: What role does preparation play in resilience?

A: Preparation helps build confidence and muscle memory, allowing teams to respond more effectively in real scenarios.

Q: How does trust impact incident response?

A: Trust helps enable faster collaboration, clearer communication, and more efficient decision-making across teams.

Q: Why is decision ownership critical?

A: Without clear ownership, teams hesitate, which can slow response and increase risk.

Q: Can strong tools compensate for weak processes?

A: No. Tools support resilience, but without strong processes and alignment, they can’t deliver effective outcomes.

Q: Where should organizations start improving?

A: Focus on cross-team alignment, clear decision-making structures, and regular scenario-based testing.

Darren Thomsonis Vice President and Chief Technology Officer, EMEA, at Commvault.

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For years, recovery planning followed a familiar pattern. Build the plan, document the steps, and assume it will work when needed. For a long time, that approach held up. Hardware failures, isolated outages, even natural disasters – these were scenarios organizations could anticipate and plan for with some level of confidence.
But the equation has changed.
In this episode of STRIVE, I sat down with Commvault’s Jason Cray, Principal Product Experience, to explore a reality we continue to see across organizations of all sizes: Most don’t fail because they lack a recovery plan. They fail because they’ve never proven that plan will hold up under real pressure.
Watch the episodio.

Key Takeaways: Why Recovery Plans Break Down

  • A documented plan isn’t the same as a proven one. If it hasn’t been tested in realistic conditions, it’s still an assumption.
  • Recovery is a team sport. Security, infrastructure, and operations must align – or recovery slows down.
  • Most investment still happens “left of boom.” Prevention matters, but recovery readiness often gets overlooked.
  • Testing exposes gaps and builds confidence. Without it, organizations default to hope.
  • Resilience is an operational discipline. It requires iteration, communication, and continuous improvement.

The Problem With ‘It Should Work’

On paper, recovery looks straightforward. You define when to recover to, what needs to come back, and where it should be restored. The process appears logical, structured, and manageable.
But as Jason points out, that simplicity rarely survives real-world conditions.
Plans are written in controlled environments, but they’re executed in chaos. When an incident hits, teams aren’t calmly stepping through documentation – they’re reacting, troubleshooting, and trying to align in real time. That’s where the gap emerges. Not between tools and technology, but between expectation and execution.

Sneak Peek: Why Plans Fail Under Pressure

In this moment from the conversation, Jason and I break down why having a plan isn’t enough – and what it actually takes to know a plan will work when it matters.

We’ve Seen This Before

What’s interesting is that this isn’t a new problem; it’s a familiar one, just in a different context.
If you go back to the early days of disaster recovery, organizations followed a similar pattern. Plans existed, but testing was inconsistent at best. Jason shared an example of spending an entire night helping a client pass a disaster recovery test they thought they were ready for. The plan looked solid. The execution told a different story.
Over time, organizations adapted. They tested more frequently, introduced failover exercises and, in some cases even ran production from secondary environments to prove readiness. That shift from assumption to validation is exactly what resilienza informatica now requires.

The First Breakdown: Communication

If there’s one issue that consistently surfaces, it’s communication.
In many organizations, responsibilities are clearly defined – security handles prevention, infrastructure manages systems, and operations owns recovery. Individually, each team may be doing exactly what they’re supposed to do.
But recovery doesn’t happen in isolation. It depends on how well those teams work together when something goes wrong.
As Jason describes, too often it becomes a handoff model: “We’ve done our part, now it’s someone else’s turn.” That approach introduces delays, confusion, and ultimately risk. During a cyber event, coordination matters more than ownership.

The ‘Left of Boom’ Problem

Another pattern we continue to see is the imbalance in where organizations focus their efforts.
There’s significant investment in prevention – security tools, detection platforms, and defensive strategies designed to stop an attack before it happens. That investment is necessary, and it plays a critical role.
But far less attention is given to what happens after the event.
The assumption is that if enough effort is spent on prevention, recovery becomes a secondary concern. In reality, the opposite is true. At some point, something gets through. And when it does, recovery becomes the defining factor in how an organization responds.

From Hope to Evidence

This is where the mindset needs to shift.
It’s not about adding more tools or rewriting documentation. It’s about moving from a model based on hope to one grounded in evidence.
Jason highlights a key observation: The organizations that handle disruption well aren’t the ones that avoid incidents – they’re the ones that experience less impact when those incidents occur. They’ve tested their processes. They’ve validated their assumptions. They understand where their gaps are.
Most importantly, they’ve built confidence – not by believing the plan will work, but by proving it.

Start Small, Build Momentum

For many teams, the challenge isn’t understanding the problem – it’s knowing where to begin.
The answer isn’t to overhaul everything at once. It’s to start small and build from there.
Focus on one or two critical services. Understand what’s required to recover them. Bring together the teams responsible for those systems and test the process end-to-end. From there, expand the scope and continue refining.
This approach does more than improve recovery – it builds alignment, reinforces communication, and creates the foundation for broader resilience.

The Reality: No Plan Survives First Contact

One of the most honest moments in our discussion was this: Even the best plan won’t work exactly as written.
That’s not a failure – it’s expected.
Jason puts it simply: If you don’t have a plan, you will fail. But even if you do have one, it won’t unfold perfectly in the moment.
What matters is how prepared to adapt your teams are. Testing creates that adaptability. It builds the muscle memory needed to respond effectively when conditions don’t match expectations.

Guarda l’episodio completo

There’s much more we cover in this STRIVE conversation, including:

  • Why recovery plans often fail despite being well-documented.
  • What differentiates organizations that recover effectively.
  • How communication gaps impact execution.
  • Where to start when improving recovery readiness.
  • Why testing is the foundation of resilience.

Guardalo subito.
If you’ve ever questioned whether your recovery plan would actually work, this is a conversation worth your time.

Domande frequenti

Q: Why isn’t having a recovery plan enough?

A: Because most plans are never validated under real-world conditions. Without testing, they remain assumptions rather than proven strategies.

Q: What causes recovery plans to fail?

A: The most common issues in recovery plans are lack of testing, poor cross-team communication, and gaps between documented processes and real execution.

Q: What does “left of boom” mean?

A: Left of boom refers to the focus on preventing incidents before they occur. Many organizations invest heavily here but underinvest in recovery capabilities.

Q: How often should recovery plans be tested?

A: Recovery plans should be tested regularly and under varied conditions. Testing should simulate realistic scenarios, not just controlled exercises.

Q: Where should organizations start?

A: Start with a small set of critical services, align the responsible teams, and test recovery end-to-end before expanding.

Q: What is the key mindset shift?

A: Moving from hope-based planning to evidence-based validation.

Chris Mierzwa is Senior Director, Portfolio Marketing, at Commvault.

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We’ve spent years focusing on identity security in the context of people – who have access, what they can do, and how to control it. That model made sense when most activity in the environment was driven by human users.But that’s no longer the case.Machine identities – applications, services, APIs, and automated workloads – now play a central role in how modern systems operate. They authenticate, communicate, and execute tasks, often without direct oversight. And in many environments, they already outnumber human identities by a wide margin.In this episode of STRIVE, I sit down with Dan Conrad, Principal Technologist and a fellow Field CTO at Commvault. We take a closer look at what that shift means – not just from a security perspective, but also from a governance standpoint. And we explore why so many organizations are still treating this as a secondary concern.Watch the episodio.

Key Takeaways: Where the Risk Is Shifting

  • Machine identities are growing faster than human identities, often by orders of magnitude.
  • Governance models haven’t kept pace, creating blind spots in access and control.
  • Visibility is the core challenge. Many teams don’t fully understand how machine identities behave.
  • Privilege sprawl extends beyond users, with machine identities often holding persistent access.
  • Resilience depends on understanding and managing the remit of these machine identities before they become a problem.

The Identity Model Has Changed

For a long time, identity management was relatively straightforward. You could map users to roles, define access policies, and build controls around predictable behavior. Even with complexity, the model was still anchored in human activity.Machine identities have broken that model.They’re created dynamically, often as part of development or deployment processes. They interact across systems in ways that aren’t always visible or well-documented or audited. And unlike human users, they don’t follow a clean lifecycle – they aren’t onboarded and offboarded in the same structured way.That creates a different kind of challenge. It’s not just about controlling access anymore. It’s about understanding how that access is being used, how it evolves, and how it connects across the environment.

Sneak Peek: You Can’t Phish a Non-Human Identity

In this moment from the STRIVE discussion, Dan describes how attackers aren’t targeting non-human identities directly through phishing – they’re malicious actors using compromised human accounts through social engineering, as a steppingstone to escalate privileges and impersonate powerful machine identities. Once inside, techniques like pass-the-hash and overprivileged service accounts allow attackers to move laterally and vertically, even after passwords are reset.

The Governance Gap

The real issue isn’t that machine identities exist , it’s how they’re governed.In most organizations, there’s a clear process for managing human access:

  • Requests are approved.
  • Permissions are reviewed.
  • Changes are tracked.

There’s a level of discipline that comes from years of focus on user identity. However, machine identities often fall outside of that structure. They’re created quickly to support applications or automation. They’re granted the permissions needed to function, sometimes more than necessary. And over time, those permissions persist. These overprovisioned accesses are rarely audited, reviewed, and more importantly rarely reduced.That’s where the gap forms.It becomes difficult to answer basic questions about access. Not because the information doesn’t exist, but because it hasn’t been organized or managed in a way that makes it usable.

Visibility Before Control

When organizations start to address this problem, the instinct is often to tighten controls.

  • Limit permissions
  • Limitare l’accesso
  • Apply new policies

But control without visibility doesn’t solve much.If you don’t understand how identities are being used, the business context of it in terms of  where they connect, what they interact with, and how they move across systems, then any attempt to restrict them becomes reactive and could result in slowing down business operations.That’s why visibility needs to come first.Once you can see how machine identities behave, patterns start to emerge. You can begin to understand where access is excessive, where dependencies exist, and where risk is concentrated. From there, governance can become more precise and more effective.

A Different Kind of Privilege Problem

Privilege sprawl isn’t new. Most organizations have spent years trying to manage excessive access among human users.Machine identities introduce a similar issue, but with a different dynamic. Their access is often embedded into systems. It’s persistent, automated, and rarely questioned once it’s in place. That makes it harder to detect and easier to overlook.And when something goes wrong, those identities can become a pathway for malicious actors to exploit

Where to Begin

For most organizations, the challenge isn’t awareness, it’s knowing where to start.The first step isn’t a major transformation. It’s building clarity. Understanding how many machine identities exist. Where they’re being created. What permissions they have. How they’re used. And most importantly, confirming that a human user is mapped to a collection of non-human identities for the purposes of auditability and accountability.Those questions sound simple, but they’re often difficult to answer. And that’s exactly why they matter. Because once you can answer them, you’re no longer operating in the dark.

Guarda l’episodio completo

In this installment of STRIVE, we go deeper into how machine identities are changing the way organizations should think about access, governance, and resilience. It’s a practical conversation about what’s happening now – and what needs to change moving forward.Guardalo subito.

Resource

If you’re interested in learning more about this topic, check out this e-book on non-human identities.

Domande frequenti

Q: What is a machine identity?

A: A machine identity is a non-human identity used by applications, services, or systems to authenticate and interact with other resources.

Q: Why are machine identities becoming a bigger risk?

A: Because they are increasing in number, often have persistent access, and are not always governed as strictly as human users.

Q: How are they different from user identities?

A: They operate continuously, are embedded in automated workflows, and often lack structured lifecycle management.

Q: What is the biggest challenge organizations face in governing non-human identities?

A: Visibility. Many teams don’t have a clear understanding of how many machine identities are created, used, or interconnected.

Q: How does this impact resilience?

A: If compromised, machine identities can enable a malicious actor’s rapid movement across systems, making incidents harder to contain and recover from.

Q: Where should organizations start?

A: By identifying machine identities, understanding their permissions, and building governance practices that match their scale and complexity. And most importantly, confirming that a human user is mapped to a collection of non-human identities for the purposes of auditability and accountability

Vidya Shankaran is Field CTO at Commvault.

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Per decenni, le operazioni IT si sono concentrate sul tempo di attività:

  • Garantire il funzionamento dell’infrastruttura.
  • Raggiungi il tuo obiettivo di tempo di ripristino (RTO).
  • Raggiungi il tuo obiettivo di punto di ripristino (RPO).

But modern cyber threats don’t respect infrastructure boundaries – and recovery isn’t just about restoring systems anymore. It’s about restoring clean, trusted data – across teams, under pressure.
In this episode of STRIVE, I sat down with Stephen Foskett, founder and president of the Futurum Group’s Tech Field Day, to discuss an emerging discipline: resilience operations – or ResOps.

And it’s more than a buzzword. It’s a shift in how organizations think about recovery intelligence.
Watch the episodio.

Punti chiave: Cosa cambia con ResOps

  • ResOps moves recovery from infrastructure-focused to business-focused. It’s not just about bringing systems back online – it’s about restoring trusted, usable data.
  • Traditional RTO and RPO metrics aren’t enough anymore. Il Mean Time to Clean Recovery(MTCR) si sta affermando come un indicatore più significativo per misurare la resilienza.
  • Breaking down silos is foundational to cyber readiness. Security, infrastructure, and DevOps must operate in sync – not in parallel.
  • Resilience is an operational discipline, not a tool. Culture, communication, and coordination matter as much as technology.
  • Recovery intelligence is becoming a competitive differentiator. Organizations that recover cleanly and quickly protect revenue, reputation, and trust. 

From IT Ops to ResOps: What’s Changed?

Stephen riflette su un’epoca passata dell’IT in cui i team spesso fornivano assistenza ai sistemi senza comprendere appieno le applicazioni aziendali che questi supportavano. Il ripristino significava ripristinare l’infrastruttura. Oggi quel modello non è più sufficiente. Gli ambienti moderni sono:

  • Distribuito
  • Cloud
  • Basato su DevOps
  • Di importanza critica per la sicurezza
  • Profondamente integrato con i flussi di ricavi

ResOps acknowledges that recovery is no longer an isolated IT function. It’s a cross-functional discipline that helps connect infrastructure, software development, and security with real business outcomes.

Why Traditional Metrics Don’t Tell the Whole Story

RTO. RPO. These metrics have guided disaster recovery planning for years. But as Stephen explains, restoring quickly isn’t enough if the data you restore isn’t clean.
Enter a more meaningful metric: MTCR. It’s not just how fast you recover; it’s how fast you can recover to a verified, trusted state.
In a ransomware event, that difference matters enormously. Restoring compromised data can restart an attack cycle. ResOps focuses on restoring operational integrity – not just functionality.

Anteprima: Perché è importante un recupero sano

In this moment from STRIVE, Stephen explains why traditional recovery metrics miss the mark – and why recovery is a cross-functional discipline.

La vera barriera: i silos organizzativi

Technology isn’t usually the biggest blocker to resilience. Structure is. Security teams often report to one executive. Infrastructure teams to another. Application teams to yet another. Each with different priorities, different incentives, and different definitions of success.
ResOps challenges that fragmentation.
Stephen discusses how collaborative workshops and cross-functional alignment are helping break down those silos. Because during a cyber event, organizational misalignment slows recovery more than tooling gaps ever will.

Perché Commvault sta puntando su questo tema

STRIVE isn’t about product features. It’s about how recovery thinking is evolving. ResOpsè perfettamente in linea con ciò che osserviamo sul campo:

  • Clienti che hanno difficoltà a coordinarsi durante gli incidenti.
  • Organizzazioni che stanno ripristinando le infrastrutture ma mettono in dubbio l’integrità dei dati.
  • I vertici aziendali chiedono indicatori che riflettano il reale impatto sul business.

The concept of MTCR reframes recovery intelligence around business trust – and that’s where the industry is heading. Recovery is no longer a back-office process. It’s an executive concern.

Il futuro dell’intelligenza applicata al recupero

Looking ahead, ResOps is likely to mature rapidly. Over the next 12–18 months, organizations are expected to:

  • Integrare in modo più stretto i flussi di lavoro relativi alla sicurezza e al ripristino.
  • Adottare nuovi indicatori incentrati sulla ripresa.
  • Rendere operativa la resilienza in una fase più precoce del ciclo di vita delle applicazioni.
  • Investite in soluzioni intelligenti in grado di distinguere i dati integri da quelli compromessi.

Cyber threats are accelerating. Recovery strategies must evolve at the same pace. ResOps helps provide a framework for doing that.

Guarda l’episodio completo

In questa puntata parleremo di:

  • In che modo ResOps si differenzia dalle operazioni IT tradizionali.
  • Perché l’MTCR sta contribuendo a ridefinire gli indicatori di ripresa.
  • Come si concretizza, nella pratica, l’allineamento organizzativo.
  • In che modo la cultura DevOps influisce sulla resilienza.
  • Qual è il futuro previsto per l’intelligenza applicata al recupero?

Guardalo subito.
If you’re responsible for cyber readiness, continuity, or recovery strategy, this is a must-watch discussion.

Domande frequenti

Q: What is ResOps?

A: ResOps (Resilience Operations) is an emerging discipline that integrates IT operations, security, DevOps, and business stakeholders to help improve recovery intelligence and organizational resilience.

Q: How is ResOps different from traditional IT operations?

A: Traditional IT ops focuses primarily on infrastructure uptime. ResOps expands that focus to include clean data recovery, cross-functional coordination, and business alignment.

Q: What is Il Mean Time to Clean Recovery (MTCR)?

A: MTCR measures how quickly an organization can restore verified, clean data and resume safe operations after a cyber event – not just how quickly systems are brought back online.

Q: Why are metrics like RTO and RPO insufficient in modern environments?

A: They measure speed and data currency, but not data integrity. In ransomware scenarios, restoring compromised data can extend disruption.

Q: How can organizations start implementing ResOps?

A: Begin by:

    • Allineamento dei team di sicurezza, infrastruttura e DevOps.
    • Valutazione degli indicatori di ripristino oltre a RTO/RPO.
    • Verifica dei processi di ripristino in modalità pulita.
    • Abbattere i silos operativi.
    • Integrare l’approccio basato sulla resilienza nelle prime fasi della progettazione dei sistemi.

Q: Why is recovery intelligence becoming more important?

A: As cyber threats grow more sophisticated, the ability to recover cleanly, quickly, and confidently directly impacts revenue, customer trust, and regulatory posture.

Darren Thomsonis a Field CTO at Commvault.

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Punti di forza

  • Frontier AI is collapsing vulnerability remediation windows,prevention alone can no longer guarantee security.
  • The question that boards,regulators,and insurers are now asking is not “Do we have backups?” but “Can we prove we can recover cleanly?”
  • Backups are not recovery: a copy tells you data exists,not whether it is clean or restorable.
  • tempo medio per il ripristino completo (MTCR) must become a board-level,continuously measured number – not a theoretical estimate.
  • An Isolated Recovery Environment – air-gapped,immutable,hardened,and identity-isolated – is the baseline,not an advanced capability.
  • What counts as “clean” will keep changing as AI models grow more capable of finding compromises humans cannot anticipate.

I have spent a large part of my career running production systems. I know backup environments from the inside,the ones customers actually trust. I know recovery plans as the things that only reveal their weaknesses when something has already gone wrong. That experience changes how you think about cyber resilience.
From a distance,backup and recovery sounds manageable. Protect the data,store copies,document the runbook,test when you can,restore when you need to. But anyone who has run these environments at scale knows the harder truth: Recovery is where assumptions go to be tested. And right now,too many organizations are operating on assumptions that no longer fit.
For years,security operated on a familiar sequence: Find the vulnerability,patch it,harden the environment,monitor for activity. That model still matters. But the window it depends on is collapsing.
Frontier AI has changed the velocity of vulnerability discovery,attack path chaining,and exploit generation. Models like Claude Mythos and GPT-5.5-Cyber have already demonstrated what this looks like,so far in controlled,early-access testing that still relied on human expertise and carried meaningful false-positive rates,but the trajectory is unmistakable. As access widens,the same capability moves into attackers’ hands.
In a single month,Palo Alto Networksdisclosed 26 CVEs,representing 75 underlying issues,after adopting frontier AI models for code scanning,compared with its typical volume of fewer than five CVEs per month.
Researchers also are warning that AI-assisted discovery is collapsing remediation windows,with someexploits now emerging within minutes of disclosure. When the patch window disappears,the remediation math stops working. Prevention cannot carry the full weight of readiness.
Prevention still matters,but it no longer defines readiness. The customers I talk to are not asking whether they need more controls. They already know they do. They are asking whether their business can recover cleanly when those controls fail,when attackers move faster than remediation cycles,or when compromise has been present longer than anyone realized.
That question is now what boards,regulators,and insurers are forcing. They have moved past “Do we have backups?” and toward something more consequential: “Can we prove we can recover cleanly?”

That proof starts with one distinction most organizations still get wrong: Backups are not recovery.
A backup tells you a copy exists. It does not tell you whether the data is clean,whether application dependencies are intact,whether identity services can be safely restored,or whether the recovery sequence still reflects the current environment.
I have reviewed plans that looked complete until someone tried to execute them. The runbook was there,but outdated. The restore worked but took three times longer than the estimate. The system came back,but downstream applications could not connect. None of that is unusual. It is exactly what real testing is supposed to surface. The problem is most organizations discover these gaps during an actual incident.
The metric that matters most when something goes wrong is how quickly you can return to a known-good state. That is why tempo medio per il ripristino completo (MTCR)needs to become a board-level number,not a theoretical estimate in a plan,but a measured,validated time.

The Moving Target: What’s Clean Today May Not Be Clean Tomorrow

With Frontier AI models,the honest answer is this: you cannot guarantee that every vulnerability will be found and remediated in time. Attackers leveraging the same models are discovering and chaining exploits faster than any remediation program can realistically keep pace with. That is not a failure of your security team. It is the new physics of the threat landscape.
What you can control is your ability to recover. That means an Isolated Recovery Environment – backups air-gapped from the internet,unreachable from the production network,and protected from the lateral movement that defines a sophisticated breach. It means immutability and compliance lock,so no credential,however privileged,can shorten retention or delete data outside an authorized process. And it means ResOps in practice: not just backing up data,but continuously testing recovery,automating integrity validation,and measuring your MTCR – the validated time to return to a known-good state.
But here is the part most organizations are not yet accounting for: what counts as “clean” is not a fixed line. As AI models grow more capable,they will increasingly find vulnerabilities that the human mind simply cannot anticipate,novel attack paths,dormant implants,subtle corruptions embedded long before detection. A recovery point that is clean by today’s standards may carry compromise that tomorrow’s AI-assisted forensics will surface. That means your definition of clean must evolve continuously. MTCR is not a number you set once. It is a discipline you maintain,revisiting what clean means,updating your validation criteria,and treating resilience as a living standard rather than a certification you pass once.
So what is a good MTCR? Based on what I have seen work in practice,the target for your entire “minimum viable company – the smallest set of systems that lets you keep operating,which I define precisely below,should be under six hours. Six hours is achievable with the right architecture: an IRE ready to run,a pre-validated recovery sequence,and runbooks that are executable rather than readable. If your current MTCR is measured in days,the gap is almost always one of those three.

Four Steps to Stay Resilient in the Frontier AI Era

Accepting that prevention alone is not enough is the starting point. From there,the work gets specific. Here is where I tell organizations to focus.

1. Evaluate your actual recovery risks.

Most recovery risk assessments ask the wrong questions. “Do backups exist?” is not the same as “Can we recover cleanly?” The harder questions are: Can critical systems be restored without reintroducing the threat? Are recovery environments isolated from compromised production systems? Are recovery plans mapped to current dependencies – not the architecture from two years ago?

In a fast-moving vulnerability environment,the gap between “we have backups” and “we can recover” is where organizations get hurt. Assessing that gap honestly,before an incident forces the issue,is where resilience planning must start. That assessment needs to include a business impact analysis: which systems have a recovery window measured in minutes,which in hours,and which can wait a day. Without that tiering,every system looks equally urgent during an incident,and nothing gets restored fast enough.

2. Make isolated recovery and air gapping the baseline – not the exception.

If you are still treatingair-gapped,immutable copies as an advanced capability rather than a standard requirement,that assumption no longer holds. When exploitation timelines compress to minutes,you need fallback options that are structurally separated from production identity,network,and management planes – logically or physically isolated,immutable,and with no live path back to production that an attacker can follow.
The goal is not just protection from the current threat,but maintaining clean recovery options when a vulnerability you have not patched yet gets exploited. That happens now. Plan for it.
Isolation only holds if the infrastructure around it is hardened. That means backup infrastructure on hardened operating systems,not generic images,and ideally on physical servers that survive a hypervisor-layer attack. It means encryption keys stored outside the backup platform,in an external vault with just-in-time access and no dependency on production Active Directory. And it means treating your backup domain as a separate identity boundary: no trust to production AD,mandatory MFA,and multi-person authorization for destructive operations. None of this is exotic,it is the baseline for your environment to recover into an uncompromised space.
Equally important is the question of what you are recovering from. Industry incident-response data consistently puts median breach dwell time in the range of weeks,not days. That means your recovery copies need to reach back far enough to find a genuinely clean point,not just yesterday’s backup. Critical systems warrant multiple geographically separated copies,including at least one immutable copy and one that is fully offline. Retention policy is not a storage cost decision. It is a security decision.

3. Know which systems the business cannot operate without – and recover those first.

Most organizations discover their recovery sequence during an incident. That’s why the first 24–48 hours aren’t spent restoring systems,they’re spent deciding what matters.
Organizations know they have to recover identity platforms,billing systems,operational databases,and core infrastructure. What they often have not mapped is the order,the dependencies between those systems,and the downstream applications that cannot function until specific services are back.
This gets more complex as AI becomes embedded in business operations. Data pipelines,model repositories,vector databases,agentic workflows – these are now operational dependencies,not just technical infrastructure. If your recovery sequencing does not account for them,your recovery time estimates are probably wrong.
Defining what it means to operate as a “minimum viable company (the smallest set of systems required to keep the business running) and building recovery around that definition is not a theoretical exercise. It is the practical answer to the question every executive team will ask during an incident: What do we bring back first?

In my experience helping customers through active incidents,the first 12 hours answer that question whether you have planned for it or not – what gets recovered in that window becomes your MVC by default. The organizations that come through fastest decided in advance: they knew exactly which systems had to be back within 12 hours and had validated they could do it. If your MVC does not fit in 12 hours,it is not your MVC,it is a wish list. The work is to keep trimming until what remains can realistically be restored in that window,then test it until you can prove it.

4. Automate resilience and test continuously – not on a calendar schedule.

A recovery plan that lives in a document and is reviewed annually is not a recovery capability. It is a hypothesis that has never been tested against reality.
The problem with calendar-based testing is what it misses between cycles. Environments change constantly: new workloads,updated dependencies,infrastructure that has drifted from what the runbook describes. By the time the annual test runs,it is validating a snapshot of an environment that no longer exists. In a threat landscape where exploitation can happen within minutes of disclosure,that lag is not acceptable.Threat scanning,clean recovery point identification,dependency-aware restoration,and recovery orchestration all need to be automated and running continuously. Not because automation is a best practice,but because the manual alternative cannot keep pace with how fast things now move.
Continuous testing also depends on continuous detection. You cannot select a clean recovery point if you do not know when the compromise began. That is why threat detection,anomaly scanning of backup data,and recovery-point analysis have to feed each other: detection tells you which copies predate the intrusion,and that determination drives which point you actually recover from. Without that link,you are restoring to a date you hope is clean rather than one you have verified,and in a Frontier AI threat landscape,hope is not a recovery strategy.
What continuous testing surfaces is different from what annual tests find. Calendar tests tend to confirm the plan works under controlled conditions. Continuous testing finds the dependency that changed last month,the recovery sequence that breaks when a specific workload is added,the identity service that takes twice as long to restore as the estimate assumed.
Those are the gaps that matter during a real event,and the only way to find them before an incident does is to be testing all the time.
Testing also needs to happen in the right environment. A recovery test that runs against production infrastructure does not tell you whether you can recover when production is compromised. Cleanroom testing – validating restoration in a fully isolated environment with no connectivity back to production – is how you confirm your backup copies are genuinely usable under incident conditions. That includes recovering identity services,external key management,and Tier 0 applications in isolation,with dedicated break-glass accounts that exist outside your normal directory.
What makes daily testing viable is validate restore,a recovery type that exercises the full restore path for every critical asset without touching production. Your backup platform needs to support this natively; if it cannot run an automated,non-disruptive recoverability test across your MVC every day,you do not actually know whether your backups work. In Commvault,this restores against your critical-asset groups,with automated reporting on the recovery status of every protected system.
The same applies to your runbooks. A runbook that lives in a Word document or PDF is a reference manual,not an operational tool – it assumes someone has the time,clarity,and access to read it under pressure. Real runbooks are digital scripts that execute the recovery sequence and validate each step,confirming the application actually works before moving on: not the service started” but “the application responded correctly to a synthetic transaction.” Commvault’s Cleanroom Runbooks are built for this – executable workflows that drive an end-to-end recovery in an isolated environment without a human interpreting a document at every step.
One final point that rarely makes it into recovery plans until it is too late: during a serious incident,your corporate communications infrastructure may itself be compromised or unavailable. Email,Teams,and Slack run on the same infrastructure attackers target. Know in advance which out-of-band channels your team will use to coordinate,and make sure those channels are tested alongside your technical recovery procedures.
Hear more from Commvault’s Chief Security Officer Bill O’Connell on the four critical steps for resiliency in the AI era.

Resilience Is an Operating Discipline,Not a Project

The organizations that will hold up under frontier AI-accelerated threats are the ones that treat resilience as anoperating discipline — measured MTCR,continuous validation,and a recovery capability they have proven,not assumed.
The problem isn’t that attacks are getting faster. It’s that recovery hasn’t caught up,and until it does,the math doesn’t work.


Domande frequenti

Q: What is tempo medio per il ripristino completo (MTCR) and why does it matter?
A: MTCR measures how quickly an organization can return to a verified,known-good state after a cyberattack – not just restore data,but confirm it is clean and that application dependencies are intact. It should be a board-level metric with a measured,validated time,not a theoretical estimate buried in a recovery plan. The target for a well-architected MVC – covering all identity systems,critical applications,and isolated environment readiness – is under six hours.
Q: What is an Isolated Recovery Environment and how is it different from a standard backup?
A: An Isolated Recovery Environment is a fully air-gapped,immutable copy of critical data that is structurally separated from production networks,identity systems,and management planes. A standard backup tells you a copy exists. An IRE tells you that copy is protected from the same attack that hit your production environment.
Q: How do we know whether we can actually recover today?
A: The only honest answer comes from testing,not documentation. If you cannot point to a recent,validated recovery of your “minimum viable company – ideally a daily automated test – then you do not know,you are assuming. A defensible answer to the board is a measured MTCR backed by continuous validation,not a recovery plan that looks complete on paper.
Q: What do regulators and cyber insurers now expect?
A: The bar has moved from “Do you have backups?” to “Can you prove you can recover cleanly,and how fast?” Regulators increasingly expect demonstrable recovery capability and tested resilience; insurers increasingly price coverage – and pay claims – based on evidence of isolated,immutable backups and validated recovery times. A measured MTCR and a documented testing cadence are becoming table stakes for both.
Rajiv Kottomtharayil is Chief Product Officer at Commvault.

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Proteggere i carichi di lavoro legati all’IA: in che modo le organizzazioni possono garantire la resilienza nell’era dell’IA?

La resilienza dell’IA contribuisce a garantire la protezione, il ripristino e la governance dei carichi di lavoro, dei dati e dei modelli di IA, combinando il rilevamento delle minacce, il ripristino completo e l’accesso controllato ai dati.

Domande frequenti

Che cos’è la resilienza dell’IA?

AI resilience is the ability to protect, recover, and govern AI systems across their full lifecycle. Commvault’s Protect and Leverage AI capabilities help verify that data, models, and pipelines remain secure, recoverable, and trustworthy — even when disrupted by cyber threats, failures, or operational complexity in hybrid and multi-cloud environments.

Perché è importante proteggere i carichi di lavoro dell’IA?

I carichi di lavoro di IA si basano su dati, modelli e infrastrutture distribuiti, il che li rende vulnerabili a minacce quali l’avvelenamento dei dati e la corruzione dei modelli. Proteggerli contribuisce a salvaguardare l’integrità dei dati, a ridurre il rischio operativo e a mantenere la fiducia nei processi aziendali basati sull’IA. Commvault aiuta ad affrontare queste sfide con Metallic AI, unificando il rilevamento basato sul machine learning, il ripristino guidato e l’automazione su Commvault Cloud.

Cosa comprende la protezione completa dello stack di IA?

Full AI stack protection safeguards data pipelines, vector databases, models, metadata, configurations, and compute infrastructure. Commvault Cloud Unity covers this breadth — including unified data platforms like Amazon Redshift and Google BigQuery, vector retrieval systems, and compute infrastructure — enabling complete and consistent recovery of AI workloads across hybrid and multi-cloud environments.

Perché il recupero pulito è importante negli ambienti di intelligenza artificiale?

Clean recovery confirms that restored data is free from corruption, malware, or inconsistencies. In AI systems, compromised data leads to inaccurate outputs and biased decisions. Commvault Synthetic Recovery addresses this by analyzing multiple backup versions to assemble a validated recovery point — so restored AI workloads produce trusted, accurate outputs.

In che modo l’intelligenza artificiale migliora la protezione dei dati e le operazioni?

Commvault embeds AI across the protection lifecycle — automating threat detection, optimizing backup scheduling, and predicting storage needs through ML-enabled capabilities. Arlie, Commvault’s AI assistant, improves user experience through natural language interactions, guided workflows, and intelligent insights, helping security and IT teams manage complex AI environments more efficiently.

Che cos’è l’IA responsabile nel contesto della protezione dei dati?

L’IA responsabile consente ai sistemi di operare all’insegna della trasparenza, della governance e del controllo. Commvault sostiene questo approccio attraversoAttiva dati — a governed workspace that applies encryption, immutability, and role-based access controls to curate and extend trusted data to AI and analytics platforms, helping prevent misuse and maintain compliance while enabling innovation.


Punti di forza

  • Mythos accelererà probabilmente l’individuazione delle vulnerabilità, raggiungendo una portata e una velocità tali da superare i tradizionali flussi di lavoro di correzione basati sull’intervento umano.
  • Gli elementi fondamentali della sicurezza, come l’applicazione delle patch, i backup in ambiente isolato e una gestione rigorosa delle vulnerabilità, rimangono essenziali ma potrebbero non essere più sufficienti da soli.
  • La sfida principale consiste nel passare dalla semplice individuazione alla capacità di intervenire, dato che il volume delle vulnerabilità sta aumentando a ritmi tali da superare gli attuali limiti operativi.
  • AI resilience depends on the ability to recover coherent systems – not just data – across models, pipelines, and permissions.
  • Le organizzazioni che si adegueranno in modo proattivo in questa fase iniziale avranno probabilmente un vantaggio competitivo molto maggiore rispetto a quelle che rimanderanno l’adozione di misure concrete.

A few weeks ago, I was in a room with a group of CIOs and CISOs when the conversation turned to Mythos and Project Glasswing. The energy was immediate – these are people who have lived through a lot of hype cycles, and this commanded their attention.

The reactions landed in two camps. One: The threat categories aren’t new – organizations with solid vulnerability management and trusted air-gapped backups will be better positioned than those without. Two: The velocity is different – not just what Mythos can find, but how fast, how fast bad actors could leverage AI for machine-speed attacks, and what that does to the math that most vulnerability management programs are built on.

Both were right. That’s what made the conversation worth writing about.

What Mythos Changes – And What It Doesn’t

Mythos is Anthropic’s AI model for autonomous vulnerability discovery. It can find and chain critical exploits across major operating systems at a success rate that is believed to have no real precedent in this domain.

Project Glasswing – the consortium of companies brought in to test and harden their systems before Mythos or similar capabilities reach adversaries – is the signal that this is real, it is here, and the window for getting ahead of it is short.

The fundamentals-first view holds: patching matters, virtually air-gapped backups matter, vulnerability management discipline matters. None of that changes with Mythos. What changes is the production rate on the other side of those programs.

The question after Glasswing isn’t whether you have a vulnerability management program. It’s whether it was built for findings that arrive in a trickle – or a tsunami.

Most programs were built for the trickle. Periodic assessments, CVSS-based prioritization queues, patch and testing cycles measured in weeks. That cadence made sense when the pace of discovery matched the pace of human-led processes. Mythos-class capability breaks that assumption – the volume of exploitable findings may exceed what most organizations can process through the workflows they have today.

The issue isn’t detection. It’s capacity to act – and what happens when the gap between discovery and remediation widens faster than you can close it.

Quando la prevenzione viene messa in secondo piano, la resilienza prende il sopravvento

When prevention timelines are compressed, the resilience question moves to the front of the line. If you can’t guarantee you’ll patch everything before something is exploited – and increasingly, you can’t – the questions that matter shift: How fast do you detect? How do you contain? And when you recover, what exactly are you recovering to?

That last question is harder than it sounds, especially for organizations with progressive agentic interactions. An AI system isn’t just data. It’s a model version, a training pipeline, a vector database, a set of agent identities and permissions – all of which need to reflect the same operational state to constitute something you can actually trust.

Most organizations can restore individual components. Very few can prove that what they’ve restored is coherent.

Recovering an AI system isn’t a data restoration problem. It’s a coherence problem – and the gap between those two things is where most enterprises are currently exposed.

This is the thread that connects Mythos to the broader AI resilience conversation. It isn’t that Mythos introduces a new type of risk that requires a new framework.

It’s that Mythos compresses the timeline in a way that surfaces existing gaps faster, with less runway to close them before something goes wrong, thereby increasing the change that something will go wrong before an organization can properly remediate vulnerabilities.

The Window Is Open. It Won’t Stay That Way.

Glasswing was designed to give defenders a head start. The organizations that use this window deliberately – stress-testing their vulnerability programs for volume, getting AI resilience infrastructure to a state they can defend, and treating recovery as something that has to be provable before an incident, not assembled during one – will be in a materially better position than those that wait.

The fundamentals still apply. The urgency is new.

“L’impresa agentica: perché la resilienza dell’IA richiede un sistema di registrazione – Commvault’s latest Readiness Report – examines the AI resilience infrastructure gaps that determine whether organizations can answer the hard recovery questions when the pace of threats demands it.

Domande frequenti

Q: What is Mythos and why is it significant?

A: Mythos is an AI model designed for autonomous vulnerability discovery, capable of identifying and chaining exploits across systems at unprecedented speed. Its significance lies in how it compresses the timeline between vulnerability discovery and potential exploitation, raising the stakes for defenders.

Q: Does Mythos change the fundamentals of cybersecurity?

A: No, core practices like patching, backups, and vulnerability management still matter. What is changing is the volume and velocity of threats, which puts pressure on existing processes that were designed for slower, more predictable workflows.

Q: Why may current vulnerability management programs struggle?

A: Many programs were built for a steady flow of findings, not the surge enabled by AI-driven discovery. As a result, organizations face a growing gap between identifying vulnerabilities and actually remediating them.

Q: What does “resilience” mean in the context of AI systems?

A: Resilience goes beyond restoring data – it involves recovering an entire AI system in a coherent, trustworthy state. This includes models, training pipelines, vector databases, and access controls all aligning correctly.

Q: Why is recovery becoming more important than prevention?

A: As prevention timelines shrink due to faster exploitation, it is becoming unrealistic to patch everything in time. This shifts focus to how quickly organizations can detect, contain, and recover from incidents.

Q: How can organizations start preparing?

A: Organizations can stress-test their vulnerability management processes, modernize resilience infrastructure, and validate recovery capabilities. Acting during this early window provides a meaningful strategic advantage.

Tim Zonca is Vice President, Portfolio Management, at Commvault.

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Punti di forza

  • L’IA agentica comporta nuovi rischi per la sicurezza perché pianifica, memorizza e agisce su più sistemi, anziché limitarsi a un singolo ciclo di richiesta-risposta.
  • I dati di addestramento “avvelenati” possono influenzare in modo impercettibile il comportamento del modello su larga scala, anche quando il modello sembra funzionare normalmente nei test standard.
  • I database dei vettori compromessi possono influenzare le decisioni degli agenti alterando il contesto su cui si basa il modello, facendo apparire legittimo un comportamento scorretto.
  • L’identità degli agenti non controllati crea un problema di controllo degli accessi alla velocità delle macchine che i tradizionali sistemi di identità incentrati sull’uomo non sono in grado di gestire.
  • Le decisioni a cascata basate su uno stato errato possono diffondere errori in più agenti e flussi di lavoro, rendendo molto più difficili il rollback e il ripristino.

The tools, controls, and governance policies most enterprises have in place were designed for systems that answer questions – retrieval tools, copilots, generative assistants. Systems that respond to a prompt and stop. When something went wrong, the failure was discrete. Fix the prompt, adjust the configuration, move on.

Agentic AI doesn’t work that way. These systems plan, remember, and execute across the enterprise without step-by-step human instruction. They maintain state. They coordinate with other agents. They act on production systems – writing to databases, triggering workflows, making decisions at machine speed.

That architectural shift introduces four threat vectors that existing security frameworks were never designed to address. If your AI governance strategy doesn’t account for them, you likely have exposure you probably can’t see.

1. Dati di addestramento contaminati

An AI system is only as trustworthy as the data it was trained on. That statement has always been true. What’s changed is the attack surface.

In agentic AI deployments, training pipelines are larger, more complex, and frequently assembled from multiple sources – internal data, third-party feeds, vendor-provided datasets. Each dependency in that chain is a potential injection point. An adversarial actor who can influence training data – through supply chain compromise, insider access, or contamination of a shared data source – can shape model behavior at scale.

What makes this particularly dangerous is that poisoned models often perform normally on standard benchmarks. The manipulation may be surgical: designed to produce specific outputs in specific contexts while behaving correctly everywhere else.

By the time the effect surfaces in production, the model has been in use for weeks or months, and tracing the contamination back to its source requires exactly the kind of relational data provenance most organizations don’t have.

The question to ask: Can you produce a complete, verifiable record of what data your models were trained on – at a specific point in time?

2. Banche dati vettoriali compromesse

Vector databases are the memory layer of agentic systems. Before an agent acts, it queries a vector store to retrieve relevant context – past interactions, domain knowledge, reference data – that shapes what it does next.

Most security teams aren’t thinking about vector databases the way they think about other sensitive data stores. They should be.

A compromised vector database doesn’t just return wrong answers. It shapes the decisions that follow. Injected embeddings – malicious content inserted into the vector store – can redirect agent behavior in ways that appear completely legitimate from the outside.

An agent asked to approve a transaction retrieves context that subtly reframes the approval criteria. An agent managing customer communications pulls context that steers responses in an attacker’s preferred direction. The action looks correct. The reasoning looks sound. But the underlying context has been manipulated.

This attack vector is particularly hard to detect because it operates below the model layer. Standard model monitoring won’t catch it. The model is behaving exactly as trained – it’s the context it’s reasoning from that’s been corrupted.

The question to ask: Is your vector database treated as a sensitive, governed data asset – with access controls, integrity monitoring, and audit logging comparable to your most critical production databases?

3. Identità degli agenti non governati

In a multi-agent architecture, agents don’t just interact with data – they interact with each other. They spawn subagents, delegate tasks, request outputs, and synthesize results from agents they’ve never been explicitly connected to. To do this, they authenticate, present credentials, and establish trust.

Agent identity is the access control layer for the autonomous enterprise – and it’s a gap that identity security vendors and identity providers (IDPs) don’t close. Their governance frameworks are built for human identity.

Agent identities created within those same rules appear completely legitimate: They were provisioned correctly, they followed policy. The IDP isn’t failing – it simply has no framework for determining whether an agent is acting outside the context it was created for, has been quietly escalated, or is coordinating where it shouldn’t be.

The exposure is qualitatively different from traditional credential compromise. When a human user’s credentials are stolen, the attacker operates within that user’s permissions, at human speed.

When an agent’s identity is compromised, the attacker gains access to the autonomous decision-making layer – the ability to trigger workflows, approve actions, coordinate with other agents, and exfiltrate data at machine speed, at scale, through channels that appear entirely normal.

Identity-layer failures are also among the hardest to detect after the fact. Agent actions taken under a compromised identity don’t look anomalous – they look like legitimate agent behavior. And because they’re generated by a system rather than a human, the volume can be enormous before anyone notices.

Recovery compounds the problem. Most AI recovery playbooks focus on restoring data: training sets, model weights, pipeline configurations. Identity is rarely on the list. A system recovered with clean data but misaligned identity configurations isn’t actually recovered. It’s a clean system with a poisoned access layer.

The question to ask: Is agent identity managed with the same rigor as human identity – with lifecycle management, least-privilege access, and inclusion in recovery playbooks?

4. Decisioni a cascata basate su una situazione errata

The first three attack vectors are discrete. This one is systemic – and in many ways it can be the most difficult to contain.

Multi-agent architectures are designed for coordination. Agents share context, pass outputs to one another, and build on each other’s work. That coordination is what makes them powerful. It’s also what makes failures propagate.

An agent operating on corrupted memory doesn’t fail cleanly. It produces outputs – decisions, actions, data – that other agents consume. Those agents produce their own outputs. By the time the original corruption surfaces as something observable, bad state may have touched dozens of downstream processes, across multiple agents, with no clean rollback path.

This is what makes the context gap so significant. At any given moment, your AI system consists of a model version, a set of training data, an artifact store, a pipeline configuration, and a set of active agent interactions – all of which need to reflect the same operational state to constitute a trustworthy, recoverable system. When they don’t, you don’t just have an error. You have a system that is coherent in pieces and incoherent as a whole.

Point tools can each confirm their own slice. None can confirm the pieces belong together. That’s not a monitoring problem you can solve by adding another tool. It’s a structural gap – and the only way to close it is with a system that captures AI state relationally: what was running, against what data, with what configuration, at what moment.

The question to ask: If your AI infrastructure were compromised today, could you identify exactly what state every component was in before the incident – and prove it?

Cosa significa questo per la vostra strategia di sicurezza

Each of these four vectors requires a different defensive response. But they share a common implication: The governance and resilience frameworks designed for the previous era of AI don’t cover the failure modes of the agentic era.

Securing agentic AI requires extending your framework in three directions:

  • Deeper, into the data and identity layers that sit below the model.
  • Broader, to cover agent-to-agent interactions that existing monitoring doesn’t observe.
  • Relationally, to capture not just the state of individual components, but how they fit together at any point in time.

That last requirement is the one most organizations haven’t yet confronted. And it’s the one that will determine whether, when something goes wrong, you have a recoverable system or a collection of accurate-looking reports describing something that no longer exists.

Read “L’angolo cieco dell’IA agentica: perché la resilienza dell’IA richiede un sistema di registrazione”per scoprire perché è necessario un SOR per garantire la coerenza e l’accuratezza dei tuoi dati di IA.

Domande frequenti

Q: Why are agentic AI systems riskier than traditional generative AI tools?

A: Agentic systems do more than answer prompts. They maintain state, coordinate with other agents, and take actions in production environments, which expands the attack surface far beyond simple prompt manipulation.

Q: What makes poisoned training data so difficult to detect?

A: The manipulation can be highly targeted, affecting only specific situations while leaving normal benchmarks intact. That means a model may look healthy until the poisoned behavior appears in real use.

Q: How can a vector database become a security problem?

A: A vector database shapes the context an agent uses before acting. If that context is altered, the agent may make decisions that seem reasonable on the surface but are really being guided by malicious data.

Q: Why is agent identity different from human identity?

A: Agent identity is tied to autonomous actions, delegation, and machine-speed execution. Traditional identity governance is designed for people, so it often misses whether an agent is acting outside its intended context.

Q: Why is cascading bad state such a serious issue in multi-agent systems?

A: Once one agent consumes corrupted output, that error can spread to downstream agents and workflows. The result is not just one bad decision, but a chain of connected failures.

Q: How can organizations improve AI security?

A: Extend governance deeper into data and identity layers, monitor agent-to-agent interactions, and track AI state relationally to enable them to reconstruct what happened during an incident.

Michael Thelander is Senior Director, Product Marketing, at Commvault.

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Punti di forza

  • I sistemi di IA agenziale sono sensibili allo stato e operano in modo continuo, rendendo insufficienti i modelli di ripristino tradizionali.
  • Il livello di memoria (database vettoriali e archiviazione del contesto) rappresenta una superficie di attacco critica ma poco monitorata.
  • I flussi di lavoro relativi al processo decisionale in tempo reale possono essere modificati senza attivare i tradizionali avvisi di sicurezza.
  • Le lacune nell’osservabilità delle interazioni tra agenti impediscono alla maggior parte delle organizzazioni di avere una visione completa dei rischi.
  • Una vera e propria ripristinazione richiede una registrazione unificata e sincronizzata nel tempo di tutti i livelli del sistema per ripristinare uno stato affidabile.

Most enterprises entering the agentic AI era are managing resilience with the wrong mental model – and the data backs it up: solo 1 azienda su 5 dispone di un modello maturo per la governance degli agenti IA autonomi. They’re thinking about AI the way they think about applications: discrete, stateless, recoverable by restoring clean data to a clean environment.

Agentic AI doesn’t work that way. These systems are stateful, continuously operating, and architecturally layered in ways that create failure modes most security and resilience frameworks weren’t designed to address. The gap isn’t in tooling. It’s in understanding what’s actually running – and what “recovery” has to mean for systems built this way.

There are four architectural layers that define the problem. Each one is distinct. Each one is underprotected. And together, they explain why an agentic AI system can appear recoverable while remaining fundamentally compromised.

Layer 1: Agent Memory – The Attack Surface You’re Not Watching

Traditional enterprise applications don’t remember anything between sessions. Agentic AI does. The memory layer – primarily vector databases storing embeddings, but also session state and retrieved context – is what gives agents continuity across interactions. It’s what allows an agent to pick up where it left off, to draw on prior context, to build a coherent picture of a complex workflow over time.

It is also one of the most consequential attack surfaces in the modern enterprise stack – and one of the least monitored.

The attack vector is subtle enough to evade most conventional security tooling. An adversary who can influence what gets written to a vector database can shape what the agent believes to be true. Injected or manipulated embeddings don’t need to look malicious – they need to look authoritative.

A compromised memory store can redirect agent behavior, exfiltrate data through agent actions, or cause an agent to make decisions that appear legitimate but serve an attacker’s objectives. None of this requires touching the model itself.

The detection problem is compounded by the volume and velocity of vector database writes in active agentic deployments. Anomaly detection tools built for structured data don’t translate well to embedding space. The signal is there – but most organizations aren’t equipped to read it.

What resilience requires here: continuous integrity monitoring of vector databases, not just backup. Version-controlled embeddings with a provable chain of custody. The ability to identify, at any point in time, exactly what the memory layer contained – and to restore to a verified clean state, not just a recent one.

Layer 2: Runtime Control – When the Workflow Is the Threat

Agentic AI doesn’t execute fixed scripts. It plans. At runtime, an agent receives a goal, determines the steps required to achieve it, selects the tools it needs, and executes – often spawning subagents to handle parallel workstreams. The workflow is dynamic, constructed in the moment, and frequently long-running.

This is what makes agentic AI genuinely useful. It’s also what makes it genuinely difficult to protect.

In a conventional automation environment, a compromised workflow is bounded. It does what it was configured to do, and it stops. A compromised agentic workflow is different: It adapts.

If an attacker can influence the planning layer – through a poisoned prompt, a manipulated tool response, or a corrupted planning model – the agent will pursue the attacker’s objective using whatever legitimate tools and access it has. It will look like normal operation. The logs, to the extent they exist, will show authorized tool calls.

Consider a procurement agent tasked with validating vendor invoices against contract terms. Under normal operation, it checks invoice amounts, cross-references approval thresholds, and flags exceptions for human review.

An attacker who can influence the planning layer – through a manipulated tool response from the contract database – doesn’t need to touch the approval logic directly. They simply give the agent a contract record with altered thresholds.

The agent plans correctly against corrupted inputs. Every tool call it makes is legitimate. Every decision it reaches is wrong. By the time the anomaly surfaces in a finance reconciliation, the workflow has processed weeks of invoices and the audit trail shows nothing but authorized actions.

The window between compromise and detection in these scenarios is not measured in seconds. Agentic workflows operate continuously. By the time anomalous outcomes surface, the workflow may have touched dozens of systems, made hundreds of decisions, and left changes across production environments that are difficult to enumerate and harder to reverse.

What resilience requires here: runtime monitoring that watches what agents are deciding, not just what they’re doing. Intervention mechanisms that can halt a running workflow cleanly without cascading failures. Recovery playbooks built for long-running agentic processes – not just for discrete transactions.

Layer 3: Agentic Observability – The Logging Gap at Machine Speed

Enterprise logging infrastructure was built for human-scale operations. It captures what systems do, at a granularity and latency designed for human review. Agentic AI operates at a different speed entirely.

In an active multi-agent deployment, agents are spawning subagents, passing context between one another, making tool calls, and synthesizing outputs – continuously, in parallel, faster than conventional logging pipelines were designed to capture.

The interactions that matter most for security – agent-to-agent communications, context handoffs, tool invocations that cross trust boundaries – are exactly the interactions that existing monitoring frameworks leave most underobserved.

Today, solo il 17% delle aziende continuously monitor agent-to-agent interactions. The other 83% are governing agentic AI based on a partial picture – one that captures what individual agents do in isolation but misses the interaction layer where the most consequential security events occur.

This isn’t a gap that more logging volume solves. The problem isn’t the quantity of data being captured – it’s that the data structures and latency requirements of agentic interactions don’t fit well into observability frameworks designed for slower, more structured systems. Closing this gap requires purpose-built agentic observability tooling, or significant adaptation of existing infrastructure.

What resilience requires here: end-to-end visibility into agent-to-agent interactions, not just individual agent outputs. Logging architectures that can operate at agentic speed without dropping events. The ability to reconstruct, after the fact, the full sequence of agent decisions and interactions for any given workflow.

Layer 4: Multi-Agent Coordination – Where Emergent Failures Hide

The most architecturally novel risk in agentic AI doesn’t come from any single compromised agent. It comes from how agents depend on one another – and how failures propagate across those dependencies before anyone realizes something is wrong.

In a multi-agent architecture, agents share context. An orchestrator agent passes a task brief to a subagent; the subagent returns a result that the orchestrator incorporates into its next decision.

If the subagent’s output is corrupted – through a compromised memory layer, a manipulated tool response, or a poisoned planning model – the orchestrator has no native way to detect it. It treats the output as authoritative. It incorporates it. It acts on it. And it passes its own now-compromised output downstream.

This is the emergent failure mode: a corruption that originates in one layer, propagates through agent interactions, and surfaces as an anomalous outcome in a system several steps removed from the original compromise. By the time it’s visible, the causal chain is long and the blast radius is significant.

Consider a threat intelligence pipeline where a data-gathering agent ingests feeds from external sources, a classification agent categorizes and scores them, and an orchestrator incorporates the scored intelligence into security posture recommendations pushed to downstream teams.

If the data-gathering agent’s memory layer is compromised – subtly, through injected embeddings that cause it to weight certain threat actors as low-risk – the classification agent receives inputs it has no reason to question. It classifies accurately against what it’s given.

The orchestrator incorporates the results confidently. Security teams downstream deprioritize the relevant threat category based on what looks like a coherent, multi-source consensus. The failure originated in Layer 1. It expressed itself in Layer 4. Nothing in between flagged an anomaly because nothing in between had visibility across the full chain.

The governance frameworks most enterprises apply to AI were designed for model outputs – what the AI says. Multi-agent coordination failures are not model output failures. They are systems failures, arising from the interaction layer between models, and they require a different kind of governance: one that monitors and controls not just individual agent behavior but the trust relationships between agents, the integrity of context as it passes between them, and the access rights that govern what any agent can request of any other.

What resilience requires here: agent identity management that treats inter-agent trust as a first-class security concern. Integrity verification for context as it moves across agent boundaries. Governance policies that cover autonomous agent behavior – not just the outputs of individual models.

Il problema relazionale che accomuna tutti e quattro

These four layers are distinct in their failure modes, but they share a common vulnerability: none of them has a shared record of how they relate to each other at a specific point in time.

The model registry knows what version is running. The vector database knows what’s in memory. The orchestration layer knows what workflow is active. The identity system knows what agents have what access. Each can confirm its own slice of the picture. None can confirm whether those slices belong together – whether they reflect the same operational state, the same moment, the same trustworthy configuration.

That’s the context gap. And it’s why recovery from an agentic AI compromise isn’t a data restoration problem. It’s a coherence problem – one that requires a unified record of the relationships between layers, not just the components themselves.

Close this gap before an incident, or spend an incident trying to close it.

The architecture challenges covered here are only part of what security and resilience leaders need to understand about agentic AI risk. “L’angolo cieco dell’IA agentica: perché la resilienza dell’IA richiede un sistema di registrazione” goes further, examining where most enterprises actually stand on AI resilience readiness, what the governance gaps look like in practice, and what it takes to make “our AI is trustworthy” a provable claim, not just an assertion.

Domande frequenti

Q: Why doesn’t traditional disaster recovery work for agentic AI?

A: Traditional recovery assumes systems are stateless and can be restored from clean backups. Agentic AI systems retain memory, evolve over time, and depend on layered interactions, making simple restoration insufficient to regain trust.

Q: What makes the memory layer in agentic AI vulnerable?

A: The memory layer stores embeddings and contextual data that influence agent decisions. If compromised, attackers can subtly manipulate what the agent “believes,” leading to incorrect but seemingly legitimate actions.

Q: How can attackers exploit runtime workflows in agentic AI?

A: Attackers can influence planning inputs, prompts, or tool responses, causing agents to execute harmful actions using legitimate processes. These actions often appear normal in logs, making detection difficult.

Q: Why is observability a challenge in multi-agent systems?

A: Agentic systems operate at machine speed with continuous interactions between agents. Traditional logging systems are not designed to capture or process this level of dynamic, high-frequency activity.

Q: What are emergent failures in multi-agent environments?

A: Emergent failures occur when a small compromise in one agent or layer propagates across interconnected agents, resulting in large-scale issues that are difficult to trace back to the original source.

Q: What does effective recovery look like for agentic AI?

A: Effective recovery requires more than restoring data – it demands a coherent snapshot of all system layers, including memory, workflows, identities, and interactions, aligned to a verified trustworthy state.

Tim Zonca is Vice President, Portfolio Management, at Commvault.

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Scaling a data-driven company is hard. Scaling one while meeting GDPR requirements, managing thousands of customers, enabling analytics teams, and standing up new infrastructure in under two weeks? That’s a different level of complexity.
In a recent episode of STRIVE, I sat down with Asif Dromi of monday.com and Ben Herzberg of Commvault to unpack what it really takes to operationalize data security at scale – not in theory, but in practice. This isn’t a high-level conversation about best practices. It’s a real-world look at how security, compliance, automation, and infrastructure decisions intersect when the clock is ticking.
Watch the episodio.
If you’re a CISO, data leader, architect, or compliance owner, this episode gives you something more valuable than theory. It shows how:

  • . Se sei un CISO, un responsabile dei dati, un architetto o un responsabile della conformità, questa puntata ti offre qualcosa di più prezioso della semplice teoria. Ti mostra come:
  • Un’azienda in rapida crescita ha gestito le sfide poste dal GDPR senza frenare l’innovazione.
  • L’approccio “Infrastructure as Code” può semplificare gli audit.
  • Security and business agility don’t have to compete.

It’s rare to hear directly from operators who’ve done this under real constraints. That’s what makes this STRIVE conversation different.

È raro poter ascoltare direttamente gli operatori che hanno affrontato questa sfida in condizioni di reale difficoltà. È proprio questo che rende speciale questa conversazione nell’ambito di STRIVE.

  • Compliance and growth don’t have to compete. Monday.com demonstrates how GDPR requirements and rapid expansion can coexist when security is built into architecture from the start.
  • Manual permissions don’t scale. Automation does. Infrastructure as code and API-driven access controls can turn governance from a bottleneck into a force multiplier.
  • Role-based access must evolve with data usage. As more teams depend on analytics, visibility and fine-grained controls become important to help prevent permission sprawl.
  • Operationalized security means visibility. It’s not just about setting policies – it’s about monitoring, auditing, and adapting controls dynamically as environments change.
  • Speed is possible when architecture is intentional. A compliant European data warehouse stood up in under two weeks because governance, automation, and tooling were designed to scale.
  • Security maturity enables innovation. When permissions, infrastructure, and compliance are programmable, organizations can move faster.

Quando le autorizzazioni, l’infrastruttura e la conformità sono programmabili, le organizzazioni possono agire con maggiore rapidità.

For monday.com, the challenge wasn’t just storing European data in Europe. It was:

  • Per monday.com, la sfida non consisteva semplicemente nell’archiviare i dati europei in Europa. Si trattava piuttosto di:
  • Garantire la conformità al GDPR e la residenza dei dati a livello regionale.
  • Garantire che i dipendenti avessero accesso solo ai dati pertinenti.
  • Garantire la visibilità e la verificabilità.
  • A supporto di analisti e sviluppatori che necessitavano di un accesso rapido.

As Asif explains in the episode, becoming a data-driven organization means internal access expands rapidly. The more teams rely on analytics, the more complex permissions become.
And that’s where many organizations hit a wall. Security becomes manual, permissions become fragile, and compliance becomes reactive. That’s not operationalized security. That’s a house of cards.

Designing Security into the Architecture from Day One 

Uno degli aspetti più interessanti dell’episodio è il modo in cui monday.com ha affrontato il problema dal punto di vista architettonico. Anziché adeguare il sistema a posteriori per renderlo conforme, ha realizzato:

  • Un data warehouse europeo dedicato.
  • Controlli di accesso chiari basati sui ruoli.
  • Modelli di autorizzazione dettagliati.
  • Livelli di governance automatizzati.

Ben descrive ciò che accade in molte grandi organizzazioni: con il passare del tempo, le autorizzazioni si accumulano a strati, spesso senza una visibilità centrale. Alla fine, nessuno è più sicuro di chi possa accedere a cosa. Rendere operativa la sicurezza significa evitare questa deriva. Significa costruire sistemi in cui la governance si adatta automaticamente man mano che l’utilizzo cresce.

L’automazione è un moltiplicatore di forza

If there’s one theme that runs through this episode, it’s automation. Instead of treating permissions as tickets and manual updates, monday.com wrapped their infrastructure in code. Databases, roles, and access policies could be created and modified programmatically.
The result? A compliant, scalable environment stood up in less than two weeks. That’s not luck. That’s architecture. And it’s a powerful reminder that security doesn’t slow you down when it’s built correctly. It enables speed.

Cosa significa davvero rendere operativa la sicurezza dei dati

“Operationalizing” gets used a lot. In this episode, it’s defined as:

  • Visibilità costante sui dati sensibili.
  • Gestione centralizzata e automatizzata delle autorizzazioni.
  • Monitoraggio degli accessi.
  • Integrazione con gli strumenti di collaborazione.
  • Politiche che si adattano man mano che il numero di utenti e il volume dei dati aumentano.

Static controls don’t scale. Manual workflows don’t scale. Security must become dynamic – part of the operating fabric of the organization. And that shift is where many enterprises struggle today.

Guarda l’episodio completo di STRIVE

In the discussion, you’ll hear more about:

  • Come monday.com ha strutturato il proprio data warehouse europeo.
  • Le lezioni più importanti apprese durante l’implementazione rapida.
  • Perché l’automazione era imprescindibile.
  • Ciò che le aziende spesso sottovalutano riguardo alla proliferazione delle autorizzazioni.
  • Come affrontare la questione dell’attuazione della governance prima che le iniziative relative all’intelligenza artificiale prendano piede.

Guardalo subito.

FAQs 

Q: How can small teams implement scalable data security?

A: Start with a clear permissions model and infrastructure-as-code tools. Automate permission management early to help avoid manual bottlenecks as you grow.

Q: What role does automation play in compliance?

A: Automation helps enable consistency, reduce errors, and simplify audits. Using APIs and scripts, you can monitor and adjust permissions dynamically.

Q: How long does it typically take to set up a compliant, scalable data environment?

A: With the right planning and tools, organizations like monday.com have achieved this in less than two weeks. Speed depends on scope and existing infrastructure.

Q: What are best practices for operationalizing data security?

A: Implement role-based access controls, automate permission management, monitor access logs regularly, and integrate security tools with collaboration platforms for real-time oversight.

Chris Mierzwa is Senior Director, Portfolio Marketing, at Commvault.

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Punti di forza

  • I quadri normativi in materia di conformità codificano gli insegnamenti tratti dai fallimenti verificatisi nella realtà e aiutano le organizzazioni a rafforzare la resilienza, la governance e la stabilità operativa.
  • Le organizzazioni che considerano la conformità come un’iniziativa volta a rafforzare la fiducia possono contribuire a consolidare la fiducia dei clienti, i rapporti con i partner e la credibilità del marchio.
  • L’allineamento normativo e solidi controlli dei rischi possono contribuire a migliorare i risultati nel settore assicurativo, dimostrando un approccio alla sicurezza maturo e resiliente.
  • Mappare i requisiti di conformità a risultati aziendali misurabili consente alle organizzazioni di collegare direttamente gli investimenti nella resilienza alla protezione dei ricavi e alla continuità operativa.
  • Le capacità di resilienza informatica, quali i backup immutabili, il ripristino rapido e i quadri di governance, aiutano le organizzazioni a trasformare la conformità in un vantaggio competitivo.

In boardrooms across Europe and beyond, compliance has become a loaded word. It conjures images of endless documentation, mounting regulatory pressure, and the looming threat of fines.
GDPR. NIS2. DORA. The acronyms keep coming, and for many organizations, it can feel like they are choking on regulation.
But what if we’ve been looking at compliance the wrong way? What if compliance isn’t just about avoiding penalties – but about building a better, stronger, more resilient business?

L’analogia con le assicurazioni: regole che esistono per un motivo

Tqui’s a useful parallel between compliance and insurance.
When you insure your car, the insurer sets certain conditions. Your brakes must work. Your tires shouldn’t be bald. An alarm system might be required. You can argue about the inconvenience, or the cost – but fundamentally, those rules exist because they help reduce risk. They help make accidents less likely. They help protect both you and others.
And qui’s the key point: Those requirements are usually a good idea, whether you buy the insurance or not.
Regulation works in much the same way. Governments and regulators don’t create frameworks because they enjoy it. Regulations are responses to real-world failures – data breaches, operational disruptions, systemic risk. They codify lessons learned the hard way.
You may object to the burden. You may find it frustrating. But when you look closely at what these frameworks require, it’s hard to argue that the core principles are unsound.

  • Proteggere i dati dei clienti.
  • Garantire la resilienza operativa.
  • Conosci i rischi della tua catena di approvvigionamento.
  • Essere in grado di riprendersi dagli incidenti informatici.
  • Dimostrare capacità di gestione e responsabilità.

Nessuna di queste è una cattiva idea.

Da come evitare le multe a come instaurare un clima di fiducia

Too often, compliance is framed defensively: “Do this so you don’t get fined.” “Do this so you don’t go to jail.”

That’s a low bar. And it’s a missed opportunity. When we shift the perspective, compliance becomes something much more powerful. It becomes a driver of trust.
Take GDPR as an example. At its heart, it’s about protecting personal data. If your organization implements strong data protection practices – not just to tick a box, but because your systems genuinely safeguard customer information – that builds trust. Customers are more confident doing business with you. Partners are more willing to integrate with you. Regulators view you as lower risk.
Trust is not a regulatory outcome. It’s a commercial advantage.
The same applies to the Digital Operational Resilience Act. It’s not just about reporting incidents; it’s about being able to withstand and recover from disruption. In a world wqui cyberattacks are inevitable, resilience is not optional. It’s foundational to continuity, reputation, and long-term value.
When compliance drives resilience, resilience drives business stability – and stability drives growth.

Regolamentazione e assicurazioni: un circolo virtuoso

Tqui’s also a natural alignment between regulation and insurance markets. When regulators mandate certain standards, insurers quickly follow. Organizations that demonstrate compliance and strong risk controls are more attractive to underwriters. They may benefit from better terms, broader coverage, or more favorable premiums.
This creates a reinforcing cycle:

  • Il regolamento stabilisce gli standard minimi.
  • Le organizzazioni rafforzano i propri controlli.
  • Le compagnie assicurative premiano chi adotta un approccio più prudente nei confronti del rischio.
  • I mercati diventano più stabili e resilienti.

In questo contesto, la conformità diventa un segnale per il mercato: prendiamo sul serio il rischio.

L’anello mancante: collegare la conformità ai risultati aziendali

One of the most important opportunities for organizations – particularly technology providers – is to make the “line of sight” between compliance and business value explicit.
For example:

  • Se un prodotto crea backup immutabili, ciò contribuisce a soddisfare i requisiti normativi in materia di integrità dei dati.
  • Se ciò consente un rapido ripristino a seguito di incidenti informatici, contribuisce al rispetto dei requisiti in materia di resilienza operativa.
  • Se garantisce una tracciabilità chiara delle operazioni e una rendicontazione adeguata, ciò contribuisce a soddisfare i requisiti di governance e supervisione.

But it shouldn’t stop tqui. The next step is to articulate the business benefit:

  • Immutable backups help reduce the impact of ransomware – and protect revenue.
  • Faster recovery helps minimize downtime – and preserves customer confidence.
  • Strong governance helps reduce regulatory scrutiny – and enhances brand credibility.

This mapping is critical. Compliance is not the end goal; it’s the mechanism that enables the outcomes that businesses care about: continuity, reputation, customer trust, and competitive differentiation.

La conformità come innovazione, non come obbligo

Tqui’s a tendency to treat compliance as a “get-it-done” exercise. A cost center. A necessary evil.
But if we look at history, many best practices that are now considered fundamental to modern IT and security originated in regulatory or insurance requirements. Over time, they became embedded in how well-run organizations operate.
Encryption. Access controls. Incident response planning. Business continuity testing. Third-party risk management.
At one time, these may have been viewed as regulatory burdens. Today, they are table stakes for any serious enterprise.
The organizations that treat compliance as an innovation catalyst – rather than a checkbox exercise – are often the ones that pull ahead. They embed resilience into their architecture. They design with governance in mind. They turn regulatory requirements into product capabilities and customer value propositions.

Resilienza informatica: dove la conformità e la strategia si incontrano

This is wqui cyber resilience becomes central.
Modern regulations increasingly recognize a simple truth: Prevention is not enough. Incidents will happen. The differentiator is how well an organization can respond and recover.
Cyber resilience – the ability to withstand, recover from, and adapt to cyber disruption – is no longer just a security concern. It’s a strategic imperative. It supports regulatory compliance, yes. But more importantly, it underpins operational continuity and business confidence.
When organizations invest in resilient architectures, immutable data, rapid recovery capabilities, and robust governance frameworks, they are not merely satisfying regulators. They are building durable enterprises.

Una prospettiva diversa sulla conformità

Perhaps it’s time to change the narrative.
Instead of asking, “What’s the minimum we need to do to comply?” we should be asking:

  • In che modo questo regolamento ci rende più forti?
  • Quale buona pratica viene qui codificata?
  • Come possiamo sfruttare questo aspetto per rafforzare la fiducia dei clienti e dei partner?
  • In che modo ciò determina un vantaggio competitivo?

Compliance done well is not about fear. It’s about foresight.
It reflects lessons learned across industries. It embeds best practice into everyday operations. And when connected clearly to product capabilities and business outcomes, it becomes a powerful commercial story.
Yes, regulation can feel burdensome. Yes, the acronyms keep coming. But underneath the paperwork lies something far more valuable: a framework for running a better business.
Compliance isn’t just about avoiding penalties. It’s about enabling resilience. And resilience, ultimately, is what drives sustainable success. Learn more about how Commvault enables data protection to help your organization meet compliance requirements qui.

Domande frequenti

Q: Why should organizations view compliance as more than a regulatory obligation?

A: Compliance frameworks often reflect best practices developed in response to real-world cyber incidents, operational failures, and governance challenges. Organizations that embrace compliance strategically can help strengthen resilience, improve trust, and create long-term business value.

Q: How does compliance contribute to customer trust?

A: Strong compliance practices demonstrate that an organization takes data protection, governance, and operational continuity seriously. This can help increase customer confidence, strengthen partner relationships, and position the organization as a lower-risk business.

Q: What is the connection between compliance and cyber resilience?

A: Modern regulations increasingly focus on an organization’s ability to recover from disruptions rather than solely preventing them. Investments in resilient infrastructure, immutable backups, and rapid recovery capabilities can help organizations maintain continuity during cyber incidents.

Q: How can compliance positively impact insurance and risk management?

A: Organizations with mature compliance programs and strong security controls are often viewed more favorably by insurers. This can lead to better coverage options, improved policy terms, and potentially lower premiums.

Q: Why is it important to connect compliance initiatives to business outcomes?

A: Compliance efforts are most effective when organizations clearly demonstrate how controls support broader goals such as protecting revenue, reducing downtime, and preserving customer trust. This helps leadership view compliance as a strategic investment rather than a cost center.

Q6: How can organizations turn compliance into a competitive advantage?

A: Businesses that embed resilience, governance, and security into their products and operations can differentiate themselves in the market. By proactively aligning with regulatory expectations, organizations can strengthen their reputation and create greater confidence among customers and stakeholders.

Darren Thomson is Field CTO at Commvault.

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Punti di forza

  • La sovranità operativa verte su chi può accedere ai sistemi e sotto quali giurisdizioni questi operano.
  • L’accesso dei fornitori, i flussi di telemetria e i canali di assistenza possono creare lacune nascoste in materia di sovranità.
  • La sovranità operativa è più difficile da certificare perché richiede visibilità e controlli continui.
  • Le organizzazioni devono essere in grado di dimostrare e documentare ogni percorso di accesso agli ambienti sovrani.

Ask most organizations where their sovereignty program is strongest, and the answer is usually some version of the same two things: data locality and encryption. They know where their primary data lives. They’ve implemented bring-your-own-key or hold-your-own-key arrangements. They can point to certifications.

Ask them who accessed their sovereign environment in the last ninety days, from which countries, and under which legal jurisdictions – and the confidence tends to evaporate.

Operational sovereignty is the hardest pillar to audit, the most likely to be underestimated, and the most common place where a sovereignty posture that looks solid on paper breaks down in practice. The Digital Sovereignty Readiness Report names it as one of the four pillars – this post goes further.

The question most organizations can’t answer: ‘Who accessed your sovereign environment in the last 90 days, from which countries, and under which legal jurisdictions?’

Cosa significa realmente “sovranità operativa”

Operational sovereignty is not about where data lives. It’s about who runs the environment – and who can reach it. It covers three things that most sovereignty programs treat as implementation details rather than first-class concerns:

  • Personnel access and jurisdiction. Every person who can access your sovereign environment – for support, maintenance, monitoring, or incident response – operates under a defined legal jurisdiction. If a support engineer in a country subject to a foreign data access law can reach your systems, the sovereignty of your infrastructure is only as strong as that engineer’s legal exposure.

Most organizations, when they audit this for the first time, find at least one support pathway that crosses a jurisdiction boundary they hadn’t mapped.

  • Third-party and vendor access. Your sovereignty boundary extends to every vendor, managed service provider, and software platform with access to your sovereign environment. ITSM platforms, monitoring tools, SIEM systems – if these sit outside your sovereignty boundary but have access to data or metadata within it, you have a gap that data locality controls cannot close.
  • Telemetry, billing, and control-plane traffic. Data sovereignty programs focus on primary data. Operational sovereignty requires mapping where everything else goes: the telemetry your infrastructure generates, the metadata your monitoring systems collect, the billing data your provider processes. These flows can cross jurisdiction boundaries even when primary data doesn’t – and they are rarely mapped.

Why This Pillar Is Harder To Certify – and Why That Matters

Data locality is relatively straightforward to document. You can point to a storage region, a data residency agreement, a third-party audit. Operational sovereignty doesn’t have the same paper trail. There is no certification that guarantees the jurisdictional status of every support engineer who might access your environment.

This is precisely what makes it both the hardest pillar to audit and the most important to get right. It also connects directly to the minimum viable sovereignty challenge: applying the right operational controls to the right workloads requires knowing what those controls are – and operational sovereignty is where that knowledge is most commonly absent.

La dimensione della catena di approvvigionamento

Il regolamento NIS2, che estende gli obblighi in materia di sicurezza informatica ai settori dell’energia, dei trasporti, della sanità e delle infrastrutture digitali, impone ora alle organizzazioni di valutare le pratiche di sicurezza informatica dei propri fornitori di tecnologia. Per i programmi di sovranità, ciò ha un’implicazione diretta: l’approccio dei fornitori in materia di sovranità non è più un semplice elemento di cortesia negli appalti, ma un requisito verificabile.

Ciò significa porre nuove domande a ogni fornitore all’interno del proprio perimetro di sovranità: Dove si trova il vostro personale di supporto? Sotto quale giurisdizione legale opera? Cosa succede all’accesso di cui dispone al mio ambiente se la vostra azienda venisse acquisita da un’entità non UE?

Come si presenta una buona soluzione

Un ambiente operativamente sovrano presenta quattro caratteristiche che possono essere dimostrate, non solo documentate:

  • Every access pathway into the sovereign environment is mapped – not just primary access, but vendor access, support access, and monitoring system access.
  • Lo stato giuridico di ogni persona o sistema che dispone di tale accesso viene documentato e sottoposto a verifica a intervalli prestabiliti.
  • I flussi di traffico relativi alla telemetria, ai metadati e al piano di controllo vengono inventariati e, a seconda dei casi, sono contenuti entro i confini della sovranità oppure vengono esplicitamente valutati e accettati come fuori dall’ambito di applicazione.
  • The organization can answer the ninety-day access question – precisely, with evidence.

One more thing: Operational sovereignty doesn’t end at access control. If recovery requires personnel who operate outside your sovereignty boundary, the posture fails at the moment of an incident. That’s the subject of the fourth post in this series. IlDigital Sovereignty Readiness Reportdigitale include una domanda di valutazione diretta sulla sovranità operativa.

Domande frequenti

Q: What is operational sovereignty?

A: Operational sovereignty addresses who manages and accesses an environment, including personnel, vendors, and support systems. It extends beyond where data is stored.

Q: Why is operational sovereignty commonly overlooked?

A: Many organizations focus primarily on data location and encryption. Access pathways, support personnel, and telemetry flows are often not fully audited.

Q: How do vendors impact sovereignty posture?

A: Vendors and managed service providers may have access to sensitive systems or metadata. Their legal jurisdictions and operational practices can affect overall sovereignty compliance.

Q: Why are telemetry and metadata important?

A: Even if primary data remains local, telemetry and metadata may cross jurisdictional boundaries. These flows can create compliance risks if left unmanaged.

Q: What does a strong operational sovereignty model include?

A: It includes mapped access pathways, documented jurisdictional controls, audited vendor access, and visibility into all telemetry and metadata flows.

Alex Zinin is VP/GM, Managed Service Providers, at Commvault.

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Punti di forza

  • Le architetture sovrane spesso danno priorità agli audit e ai controlli di accesso rispetto alla prontezza al ripristino.
  • Il personale addetto al ripristino, i sistemi di backup e i modelli di custodia delle chiavi possono creare lacune nella sovranità durante gli incidenti.
  • È fondamentale garantire controlli coerenti sia nell’ambiente primario che in quello di ripristino.
  • Una resilienza a prova di sovranità richiede procedure di ripristino collaudate in condizioni realistiche.

Picture the moment. The attack has already happened. The incident response team is assembling. Someone must decide which systems come back first, in what order, using the correct recovery points.

And then someone realizes: The personnel with recovery system access are based in a different country. Worse, the recovery environment itself (hosted in a cloud region, a partner datacenter, or a secondary site) was never subject to the same sovereignty controls as the primary data.

The practice wasn’t subject to the same sovereignty controls as the primary data. The regulator is asking for status. The clock is running.

This is the scenario most sovereign architectures were not designed for – and the one the Digital Sovereignty Readiness Report calls out directly: most sovereign applications are designed for the audit, not the incident.

Most sovereign applications are designed for the audit, not the incident. The difference becomes visible at the worst possible moment.

Il punto cieco della ripresa nell’architettura sovrana

Sovereignty programs are built around access control – who can reach the data, under what authority, through what pathway. That architecture is necessary. It is not sufficient. And it connects directly to the operational sovereignty gaps esaminate nel terzo articolo di questa serie: If the people who run your environment operate outside your sovereignty boundary, that problem doesn’t disappear during an incident. It becomes the problem.

What access control leaves unanswered is the harder question: What happens after an incident, when recovery is not just a technical operation but a legally constrained one?

A ransomware attack on a regulated European organization doesn’t simply create a recovery problem. It creates a recovery problem that must be solved within a jurisdiction, using personnel with appropriate authorizations, against recovery points that can be demonstrated to be clean and uncompromised.

The sovereign architecture designed to protect the data can make recovery harder if resilience wasn’t built into the original design.

Le modalità specifiche di guasto

The ways sovereign recovery architectures fail are predictable – and common:

  • Recovery personnel outside the sovereignty boundary. The engineers who know the recovery systems may operate in a different jurisdiction. Under pressure, using them is the path of least resistance. It is also a sovereignty violation at the moment it is least convenient to have one.
  • Backup infrastructure without matching controls. Primary sovereign environments are carefully controlled. Backup infrastructure – particularly older or secondary environments – is frequently not subject to the same sovereignty requirements. If recovery points are stored or processed outside the boundary, compliant recovery is not available from compliant infrastructure.
  • Key custody under crisis conditions. Hold-your-own-key arrangements are designed for normal operations. Under crisis conditions – with primary systems compromised and time pressure acute – the key custody model that works in a routine maintenance window may become an obstacle to recovery. If this hasn’t been tested, it’s an assumption, not a control.
  • Cross-environment governance gaps. Organizations operating across multiple sovereign tiers – which is most of them – often have strong controls in primary environments and weaker controls in secondary environments that are also part of the recovery path. Consistency across the full estate is what auditors will look for. Gaps in secondary environments become visible exactly when consistency matters most.

Perché i controlli sulla sovranità possono complicare la ripresa

The same controls that make a sovereign environment defensible to an auditor can make it harder to recover from. Data movement restrictions that prevent unauthorized exfiltration also constrain recovery orchestration. Key custody arrangements that ensure no provider can access your data without authorization also add friction when you need to restore quickly.

None of this means these controls are wrong. It means they have to be designed with recovery in mind from the start – not added to an architecture where recovery was an afterthought. This is the core of the minimum viable sovereignty principle: Calibrating controls to actual requirements includes recovery requirements, not just access control requirements.

Cosa richiede una resilienza orientata alla sovranità

  • Clean recovery validation. Proving that recovery points are free from compromise before restoring to production – not just recent, but uncompromised. In a ransomware scenario, a recent backup may itself be compromised. The ability to identify and restore from a known-clean recovery point, validated before it’s needed, is a sovereignty requirement, not just a disaster recovery requirement.
  • Cross-environment governance. Consistent sovereignty controls and audit evidence across the full estate – not just the primary sovereign deployment. Every environment in the recovery path must meet the same requirements as the primary environment.
  • Tested under realistic conditions. Regular exercises that validate recovery under the conditions that will actually exist during an incident: the legal constraints that apply, the personnel who are available, the recovery points that are clean. An annual disaster recovery test that doesn’t account for sovereignty constraints is not a sovereignty-ready exercise.

La domanda da aggiungere alla tua analisi sulla sovranità

There is a direct way to assess whether your recovery architecture meets the same sovereignty requirements as your primary data environment: Ask it as a question and require an honest answer.

Can you recover your sovereign data, cleanly, within defined tolerances, using personnel operating within your sovereignty boundary, right now – under real conditions, not a controlled exercise?

For most organizations, the honest answer reveals a gap. The organizations that find it now – before the incident – will be best prepared with evidence when the regulator asks for it. The ones that don’t will be building it under pressure, in front of the people they least want to disappoint.

The Digital Sovereignty Readiness Reportinclude una domanda relativa alla valutazione dell’architettura di ripristino diretto.

Domande frequenti

Q: Why is recovery important to digital sovereignty?

A: Sovereignty is incomplete if organizations cannot recover data within the same legal and operational boundaries used to protect it.

Q: What are common sovereign recovery failures?

A: Common failures include recovery personnel operating outside the sovereignty boundary, backup infrastructure lacking matching controls, and inconsistent governance across environments.

Q: How can key custody complicate recovery?

A: Hold-your-own-key models strengthen security during normal operations, but they can slow recovery efforts during incidents if not properly tested.

Q: What is clean recovery validation?

A: Clean recovery validation confirms that recovery points are free from compromise before systems are restored. This is especially important in ransomware scenarios.

Q: How should organizations test sovereignty-ready resilience?

A: They should conduct realistic exercises that account for legal constraints, operational availability, and validated recovery points – not just standard disaster recovery testing.

Alex Zinin is VP/GM, Managed Service Providers, at Commvault.

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Punti di forza

  • La sovranità minima necessaria (MVS) si concentra sull’applicazione del giusto livello di controllo ai carichi di lavoro appropriati.
  • Trattare tutti i carichi di lavoro allo stesso modo può comportare complessità e costi superflui o una protezione insufficiente.
  • Le organizzazioni rientrano in genere in tre profili di sovranità: sovranità totale, impresa regolamentata e multi-cloud ibrido.
  • Una governance coerente in ambienti misti rappresenta una delle maggiori sfide operative.

There is a version of the digital sovereignty conversation that leads organizations somewhere expensive, operationally burdensome, and – if they’re being honest – further than their actual obligations require. Maximum sovereignty sounds responsible. In practice, it’s often a miscalibration.

There is an equally common version that leads somewhere dangerously thin – controls that satisfy a checklist but wouldn’t survive an audit, an incident, or a regulator who has stopped accepting documented intent as proof of demonstrated control.

The organizations that get sovereignty right tend to do something more rigorous and more practical than either extreme: They ask what they actually owe, to whom, and for what. Then they build to that standard – no more, no less.

This is the discipline of MVS, introduced in the Digital Sovereignty Readiness Report  and developed in full here.

MVS isn’t a shortcut. It’s a recognition that the goal is the right level of control, applied consistently, across every workload that requires it.

Non tutti i carichi di lavoro sono uguali

The starting point for an MVS approach is workload classification – and most organizations skip it entirely.

A trading system processing regulated financial data carries fundamentally different sovereignty obligations than an internal HR collaboration tool. A database holding personal data of EU citizens is subject to a different legal and regulatory regime than a development environment running anonymized test data.

Treating all of these identically – either by applying maximum sovereign controls across the board or by assuming a single deployment model covers everything – is how organizations end up either over-engineered or under-protected.

The right question before any deployment decision: What does this workload require across each of the four sovereignty pillars? The Readiness Report includes a self-assessment structured around exactly that question.

The Three Profiles – and What They Actually Need

Le imprese soggette a regolamentazione rientrano in tre profili ben definiti, ciascuno con motivazioni principali e priorità di investimento diverse.

  • The True Sovereign. Government agencies, defense contractors, and critical national infrastructure operators. For these organizations, sovereignty is not a compliance requirement – it is an operational mandate. Maximum control over every dimension of the technology stack is often legally required, and the cost tradeoffs are accepted because the alternative is not.
  • The Regulated Organization. Financial services firms, healthcare organizations, energy companies. These organizations face binding requirements from DORA, NIS2, GDPR, and sector-specific frameworks. Compliance obligations may also map to EU certification schemes – including EUCS, EUCC, BSI C5, and SecNumCloud – depending on sector and deployment context.

on-negotiable in certain areas – particularly around data residency, operational access controls, and recovery within jurisdictional boundaries. But not every workload carries the same obligation.

  • The Hybrid Multi-Cloud Organization. Organizations with existing hyperscaler investments facing increasing sovereignty pressure from customers, regulators, or procurement requirements. Their challenge is not wholesale migration – it’s layering sovereign controls onto a mixed estate and maintaining consistent governance across it.

Il costo di una calibrazione errata

Over-engineering sovereignty creates its own operational risks. Organizations that apply maximum sovereign controls to workloads that don’t require them absorb cost and complexity that serves no regulatory or business purpose.

Under-engineering is the more common failure mode, and the more dangerous one. It typically doesn’t show up until the audit arrives – or, more seriously, until an incident occurs and recovery becomes a legally constrained problem. (That failure mode is the subject of the quarto articolo di questa serie.)

Un punto di partenza pratico

Un approccio MVS prevede tre passaggi:

  1. Classify workloads by their actual sovereignty requirements across each pillar – don’t start with deployment models.
  2. Mappare ciascuna classe di carico di lavoro al livello di implementazione che soddisfa tali requisiti, nell’intero spettro che va dalle regioni degli hyperscaler pubblici al cloud pubblico sovrano fino agli ambienti gestiti on-premise.
  3. Govern the resulting mixed estate consistently – controls, audit evidence, and recovery capabilities must be demonstrable across the full environment, not just the most-sovereign tier.

The third step is where most programs struggle. Maintaining consistent sovereignty controls across a mixed estate is an operational governance challenge – and specifically the domain of Operational Sovereignty – argomento del terzo articolo di questa serie, il pilastro che la maggior parte delle strategie considera come un aspetto secondario.

Utilizzate l’autovalutazione contenuta nelDigital Sovereignty Readiness Reportper individuare la vostra posizione attuale rispetto a tutti e quattro i pilastri.

Domande frequenti

Q: What is minimum viable sovereignty (MVS)?

A: MVS is the practice of applying sovereignty controls based on actual business and regulatory needs. It is intended to help avoid both over-engineering and under-protection.

Q: Why is workload classification important?

A: Different workloads carry different regulatory and operational obligations. Classifying workloads helps organizations apply the appropriate level of sovereignty controls.

Q: What are the three common sovereignty profiles?

A: The three profiles are true sovereign organizations, regulated organizations, and hybrid multi-cloud organizations. Each has distinct operational and compliance requirements.

Q: What risks come from over-engineering sovereignty?

A: Excessive controls can increase operational complexity and costs without delivering meaningful compliance or business value.

Q: Why do mixed environments create governance challenges?

A: Organizations often operate across multiple cloud and infrastructure models. Maintaining consistent controls, audit evidence, and recovery standards across all environments is difficult.

Ruben Renders is Solutions Director, MSP, at Commvault.

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Punti di forza

  • La residenza dei dati indica il luogo in cui questi sono archiviati, ma la sovranità digitale richiede anche il controllo sull’accesso, sulle operazioni e una corretta comprensione delle implicazioni giurisdizionali.
  • La sovranità operativa è spesso l’aspetto più debole e meno sottoposto a verifica della maggior parte dei programmi di sovranità.
  • Una posizione di sovranità completa si basa su quattro pilastri: la località dei dati, la sovranità tecnologica, la sovranità operativa e la sovranità giurisdizionale.
  • La sovranità non è una questione binaria; le organizzazioni devono definire una strategia in linea con i propri obblighi normativi e operativi.

Here is a question worth sitting with: When your organization made its sovereignty decision, what exactly did it decide?

For most, the answer is some version of the same thing. Pick a region. Move the workloads. Choose a cloud provider with data centers in-country. Check the box. The question of where data lives was answered, and the sovereignty conversation was considered closed.

But it wasn’t closed. It had barely started.

Data residency answers one question: Where? Digital sovereignty asks three more – who, how, and under what conditions?

The conflation of residency with sovereignty is understandable. Hyperscalers have made region selection feel like a sovereignty decision. Compliance checklists ask where data is stored. Regulatory guidance, at least in its earlier iterations, focused heavily on geography.

Choosing a sovereign cloud region is a real thing – it matters, it has operational implications, and it’s a necessary first step. But it is only a first step. And most organizations stopped there.

What Residency Doesn’t Answer

Think of it this way: Choosing a sovereign cloud region is like buying a safe. It tells you where your valuables are stored. It says nothing about who has a copy of the combination, who manufactured the safe, which country’s laws govern the manufacturer, or whether you can open it if compelled to.

Region selection answers one question. Three more remain entirely open – and these are the questions regulators, procurement committees, and auditors are now asking with increasing precision:

  • Who can operate your environment, and from where? Whether your cloud provider’s support personnel are subject to foreign jurisdiction is a sovereignty question that data residency cannot resolve. A routine maintenance window performed by a support engineer in a different legal jurisdiction is an access pathway your residency policy doesn’t cover. This is the domain of Operational Sovereignty – the hardest pillar to audit and the most commonly overlooked.
  • Under what legal regime can your data be accessed? A foreign technology provider operating infrastructure in-country does not automatically remove the reach of their home jurisdiction’s law. The extraterritorial reach of foreign legal regimes is a risk that geography alone cannot eliminate.
  • Can you recover your data if something goes wrong? Most sovereignty programs are built around access control. Very few address recovery – whether your data can be restored cleanly, within defined tolerances, by personnel who operate within your sovereignty boundary. That gap is where sovereignty postures most commonly fail under real conditions.

Il quadro di riferimento che colma il divario

A complete sovereignty posture spans four interdependent pillars. The Digital Sovereignty Readiness Report – available at readiverse.com – walks through each in full. In brief:

  • Data locality addresses where data and metadata actually travel.
  • Technological sovereignty covers control over encryption, key custody, and architecture portability.
  • Operational sovereignty covers who runs the environment and from where.
  • Jurisdictional sovereignty establishes the legal framework governing and affecting all of the above.

No single pillar is sufficient. A strong data locality posture with weak operational controls is not sovereignty – it is residency with unexamined risk.

What makes the framework useful is not its complexity. It’s the questions it generates. When an organization maps its current posture against all four pillars for the first time, it almost always finds gaps it didn’t know were there – not because the controls are absent, but because the questions were never asked.

La sovranità è una scala mobile

One more thing worth naming: Sovereignty is not a binary state. There is no certification that grants it and no single deployment model that guarantees it. It is a posture – a set of deliberate, auditable decisions. And the right level of that posture varies by organization, by workload, and by what you actually owe regulators and customers.

That calibration is what minimum viable sovereignty is about – the subject of the secondo articolo di questa serie.

Regulatory confidence is built long before the audit itself – through clearly defined requirements, not assumptions tied to geography.

Download the Digital Sovereignty Readiness Report for the four-pillar framework and a practical self-assessment tool.

Domande frequenti

Q: What is the difference between data residency and digital sovereignty?

A: Data residency focuses on where data is physically stored. Digital sovereignty goes further by addressing who can access the data, how systems are operated, and exposure to which jurisdictions may create legal risk.

Q: Why is region selection not enough for sovereignty?

A: Choosing a cloud region only addresses geography. It does not resolve issues related to operational access, legal risks exposure, or recovery capabilities.

Q: What are the four pillars of digital sovereignty?

A: The four pillars are data locality, technological sovereignty, operational sovereignty, and jurisdictional sovereignty. Together, they create, what we believe, is a more complete framework for assessing sovereign readiness.

Q: Why is operational sovereignty difficult to manage?

A: Operational sovereignty involves monitoring who can access systems, where they operate from, and under which legal regime. These controls are harder to audit than simple data location requirements.

Q: Is digital sovereignty a fixed certification?

A: No. Sovereignty is an ongoing posture based on deliberate, auditable decisions that vary by organization, workload, and regulatory environment.

Ruben Renders is Solutions Director, MSP, at Commvault.

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