Ransomware no longer tests backup strategy alone; it tests whether the business can reassemble trusted operations under pressure. AI adds another layer of complexity because more decisions, workflows, and customer experiences now depend on data pipelines, models, prompts, embeddings, and distributed cloud services. The attack surface is expanding while recovery expectations are becoming more precise.

IBM’s 2026 Cost of a Data Breach Report found that 92% of organizations reporting an AI-related breach lacked proper AI access controls, and that ransomware or extortion incidents averaged $5.12 million USD when disclosed by an attacker. Verizon’s 2026 DBIR also reported that ransomware appeared in 48% of breaches, while attacker use of generative AI is affecting targeting, initial access, and malware development.

A resilient organization must be able to identify exposed or compromised data, recover identity services, restore prioritized workloads, and validate that recovered systems are clean enough to support the next business action.

Why cyber resiliency has changed

The shift is also organizational. Ransomware recovery requires coordination across infrastructure, cyber teams, application owners, legal, communications, and business leadership. Technical teams need recovery runbooks that reflect real business priorities, not just infrastructure diagrams. Executives need evidence that impact tolerances are realistic. Operators need an environment where they can test recovery without putting production systems at risk.

That is why modern cyber resiliency depends on continuous validation. It asks organizations to define critical services, protect the data and configurations those services depend on, and rehearse recovery before an incident forces those decisions in real time. In practice, that means using capabilities such as immutable storage, isolated recovery, malware and anomaly detection, and workload-aware recovery orchestration to make recovery more predictable when conditions are least predictable.

How AI changes ransomware readiness

AI changes cyber resiliency by increasing the amount of business-critical data that must be governed, protected, and recoverable. AI applications depend on training data, retrieval-augmented generation sources, vector stores, model outputs, prompts, logs, and API-connected workflows. Each element can create new exposure if access control, classification, and recovery planning do not keep pace.

Attackers are also using AI to increase scale and precision. IBM reported that 25% of breaches involved attacker use of AI, a whopping 56% increase over last year. Most of those attacks employed AI-generated phishing and deepfake impersonation. Verizon’s 2026 DBIR describes threat actors using generative AI in targeting, initial access, vulnerability research, and tool development. The practical takeaway is that recovery planning has to assume faster and more adaptive attack paths.

For defenders, AI is also part of the answer when used with governance. AI-assisted anomaly detection, threat hunting, and recovery intelligence can help teams identify unusual behavior, assess recovery points, isolate compromised files and prevent them from being restored, and prioritize response actions.

But those capabilities are most useful when they work from a clear data foundation. Organizations need to know what sensitive data exists, where it is stored, who or what can access it, and whether AI systems are using it appropriately.

Commvault capabilities such as Risk Analysis can support that foundation by helping teams discover, classify, and assess data risk. For AI-dependent environments, the resilience question becomes very specific: Can the organization recover the data, systems, and access paths required for AI-enabled operations without restoring compromised or overexposed data? Cyber resiliency has to cover that full chain.

What clean recovery requires

Clean recovery starts with the assumption that not every backup is safe to restore. Ransomware actors often try to corrupt, encrypt, delete, or tamper with recovery sources before defenders understand the full scope of compromise. That makes recovery a data trust problem. Teams need a way to help identify viable recovery points, isolate recovery activity, scan for threats, and validate restored workloads before returning them to production.

There are three essential requirements to move forward with clean recovery:

1. Resilient backup storage. Immutable and indelible copies help keep recovery data available within defined retention settings, even if production systems are compromised. Air-gapped architecture adds separation from the affected environment. Capabilities such as Commvault AirGap can help organizations maintain protected backup copies that support ransomware recovery planning.
2. An isolated place to test and stage recovery. Restoring directly into production can increase risk when teams are still investigating the blast radius. Commvault Cleanroom helps support secure, isolated recovery testing, cyber forensics, and recovery staging so teams can validate workloads before broader restoration.
3. Recovery prioritization. After a ransomware incident, the question is not only how fast data can be restored, it is which systems need to come back first to support minimum viable operations. Identity services, communications platforms, customer-facing applications, and core data stores may have to be recovered in a specific sequence. This is why cyber resiliency depends on runbooks, automation, and testing. A clean recovery plan must reflect how the business actually operates.

How Commvault helps support cyber resiliency

Commvault Cloud helps support cyber resiliency by connecting data protection, data security, identity recovery, and clean recovery capabilities within a unified platform. That matters because ransomware and AI-era disruption do not respect infrastructure boundaries. Critical data may be spread across on-premises systems, cloud workloads, SaaS applications, endpoints, databases, file stores, and AI data environments. Recovery planning has to span those environments without forcing teams into disconnected workflows.

For ransomware readiness, Commvault Cloud can help organizations maintain immutable and indelible backup copies, use air-gapped storage, and stage recovery in isolated environments. Commvault Cleanroom helps teams test recovery plans, conduct recovery validation, and support forensic investigation without relying on production infrastructure. AI-assisted recovery capabilities can help identify cleaner recovery options and improve decision-making during restoration.

For data security, Commvault Risk Analysis helps discover and classify sensitive data so teams can understand where exposure exists before and after an incident. This is increasingly important as AI systems consume and generate more data across business functions.

For identity resilience, Commvault’s Active Directory and Entra ID resilience capabilities help teams recover identity systems that may be required before other critical applications can be accessed and restored.

The broader value is operational. Cyber resiliency improves when teams can define recovery priorities, validate clean recovery paths, and prove that critical services can be restored under realistic conditions. Commvault does not replace the need for disciplined incident response or resilience planning. It offers teams platform capabilities that can help make those plans more measurable, repeatable, and executable across hybrid environments.

Make cyber resiliency measurable before the next attack

AI and ransomware are changing the recovery conversation from “Do we have backups?” to “Can we restore trusted operations in the right order, from trusted data, with evidence that the recovery path works?” That distinction matters because disruption can affect identity, production data, SaaS applications, cloud workloads, AI pipelines, and recovery infrastructure at the same time.

Cyber resiliency depends on data risk visibility, resilient backup copies, identity recovery, clean recovery validation, and practiced workflows tied to business priorities. Commvault Cloud brings together data protection, data security, cyber recovery, and identity resilience capabilities across hybrid environments to help support that model.

It’s critical to make resilience measurable before the next incident tests it. Teams should take steps now to define minimum viable operations, identify what must come back first, validate recovery in isolated environments, and keep recovery plans aligned with changing AI and ransomware risk.

Cyber resilience is increasingly measured by what happens after attackers get in. Organizations have invested heavily in prevention, detection, and response, but ransomware, vulnerability exploitation, credential abuse, cloud misconfigurations, and third-party compromise continue to disrupt operations.

For many teams, the recovery challenge is no longer simply whether backups exist. It is whether those backups are clean, protected, validated, and ready to restore critical services when production systems can no longer be trusted.

That distinction matters because cyberattacks continue to create both data risk and operational disruption. According to the Verizon 2026 Data Breach Investigations Report, ransomware was involved in 48% of breaches, up from 44% the previous year. The report also found that exploitation of vulnerabilities became the most common initial access vector for breaches, rising to 31% while credential abuse fell to 13%.

Cloud backup and recovery efforts need to support the full path from detection to restoration. That starts with identifying suspicious activity before compromised data is restored. It continues with protected, immutable recovery points that give teams usable recovery options when production systems are no longer trusted.

From there, organizations need a way to validate which recovery points are clean and restore critical workloads in the right order. The result is a recovery strategy that helps teams move from incident response to operational restoration with more confidence.

Why is Cloud backup and recovery a cyber resilience strategy?

Traditional backup strategies were designed to help organizations recover from hardware failures, accidental deletion, and localized outages. Those use cases still matter, but today’s recovery requirements are broader.

Cyberattacks can affect production workloads, identity systems, cloud configurations, SaaS applications, and backup environments at the same time. When that happens, recovery is not just about restoring a copy of data. It is about determining which systems can be trusted, which recovery points remain clean, and which services need to come back first.

That is why cloud backup and recovery has become a critical part of cyber resilience. A modern strategy should help teams detect suspicious activity, protect recovery data, validate backup integrity, and restore critical operations in a controlled sequence. It should also help support regular testing, because a recovery plan that has not been exercised may not perform as expected during a real incident.

This marks a shift from backup as an insurance policy to recoverability as an operational capability. Stored copies still matter, but they are only one part of the recovery equation. Teams also need confidence that recovery data has not been altered, that restoration workflows have been tested, and that the business knows which services must come back first.

Cloud backup and recovery becomes easier to understand when it is viewed as a lifecycle. The five stages below show how organizations can move from early threat detection to validated recovery and long-term resilience improvement.

Stage 1: Detect threats before recovery risk spreads

Recovery starts before systems are restored. In a cyber incident, the first priority is to understand whether suspicious activity has affected production data, backup data, or both.

If teams restore from a compromised recovery point, they may bring corrupted files, malware artifacts, or unauthorized changes back into the environment. That risk makes threat detection an important part of cloud backup and recovery, not just a security operations concern.

Modern recovery strategies should include visibility into abnormal activity across workloads, backup environments, and recovery points. Teams may need to investigate signals such as:

• Unusual encryption behavior
• Sudden deletion spikes
• Unexpected privilege changes
• Abnormal backup patterns
• Malware indicators

These signals can help teams understand where an attack may have spread and which data may require additional review before restoration.

Timing is another essential factor. Microsoft’s 2025 Digital Defense Report found that most attacks investigated by its Detection and Response Team (DART) had short dwell times, which means recovery teams may not have weeks to understand the full scope of compromise before attackers move laterally, access sensitive data, interfere with services, or attempt to affect backup systems. Detection context can help teams avoid treating every recovery point as equally trustworthy.

Threat detection doesn’t eliminate recovery risk on its own. It helps create a more informed recovery process. When suspicious activity is identified early, organizations can isolate affected systems, investigate impacted data, and avoid restoring recovery points that may reintroduce the same threat.

That gives security, IT, and recovery teams a clearer starting point for the next stage: protecting clean recovery points before attackers can alter or remove them.

Stage 2: Protect clean recovery points from attack

In a cyber incident, backups are not just stored copies. They are part of the recovery path, which means attackers may try to disrupt them. If backup data is altered, encrypted, deleted, or made inaccessible, the organization may lose one of its best options for restoring operations without relying on compromised production systems.

That’s why clean recovery points need layered protection. Immutable and indelible backup storage can help preserve data for a defined retention period. Offsite or isolated copies help add separation from the production environment. Encryption, access controls, and role-based permissions help limit who can access or change backup settings. Together, these safeguards make it harder for attackers to interfere with the data teams may need most during recovery.

The goal is to preserve recovery choice. The 2026 Verizon report found that 69% of ransomware victims in its dataset did not pay the ransom, up from 65% the prior year. The report also notes that median ransom payments continued to decline, which it links in part to improved defensive adaptations and increased victim resilience. Teams need clean backups they can actually use, so paying a ransom is not the only path back to business.

The familiar 3-2-1 backup rule still provides a useful foundation: Keep three copies of data, on two different media or platforms, with at least one copy stored offsite or isolated. Modern cloud backup and recovery strategies often extend that model with immutable storage, air-gapped patterns, policy-based retention, and replicated copies across cloud or hybrid environments.

With protected recovery points in place, teams can pare down their restore options and move into validation with a clearer view of what is ready to bring back.

Stage 3: Validate which backups are ready to restore

Having backups is not the same as being ready to recover. Before teams restore production systems, they need to know which recovery points are usable, which workloads were affected, and what dependencies must come back with them.

A recent backup may contain the latest business data, but it also may include corrupted files, unauthorized changes, or malware artifacts. An older backup may be cleaner, but it may create more data loss. Validation helps teams make that tradeoff with evidence instead of guesswork.

That work starts with scoping the incident. Security and IT teams need to understand when suspicious activity began, which systems were touched, and whether identity services, databases, file shares, SaaS applications, or cloud configurations were affected.

They also need to confirm whether the recovery point supports the application as a whole, not just the data behind it. A database restore, for example, may depend on application servers, permissions, encryption keys, network routes, and identity services all being available in the right state.

Isolated recovery environments can help teams test those conditions before restoring into production. In a controlled environment, teams can safely:

• Scan selected recovery points.
• Review file changes.
• Confirm application startup.
• Test user access.
• Check whether dependent systems behave as expected.

Validation also should feed the recovery sequence. Teams may need to restore identity services first, then core infrastructure, then mission-critical applications, and then supporting workloads.

By testing recovery points before restoration, they can narrow their options and decide which systems are ready to bring back, which need further review, and which should remain isolated until the risk is better understood.

The next stage is where that decision turns into action: restoring the systems, applications, and data the business needs first.

Stage 4: Restore critical operations in the right order

A restore plan starts with the organization’s minimum viable operating state. That means identifying the people, systems, applications, data, and communication channels the business needs to function at a basic level during a disruption.

For some organizations, that may start with identity services and employee communications. For others, it may prioritize customer-facing applications, payment systems, clinical systems, manufacturing operations, or logistics platforms. The order should reflect business impact, not just technical convenience.

Dependencies are where many recovery plans become more complicated. An application may be listed as “critical,” but it still depends on identity, DNS, network connectivity, databases, storage, encryption keys, APIs, and monitoring. If those pieces are not restored in the right state, the application may come back online but remain unusable. That is why recovery teams need dependency mapping before an incident, not during one.

Runbooks and orchestrated workflows help turn those decisions into repeatable steps. They can define who approves the restore, which environment should be used, which checks must happen before production access is restored, and when the next tier of systems can come online. This matters when security, infrastructure, application, cloud, and business teams are all working at the same time.

Restoration also needs checkpoints. After each major workload comes back, teams should confirm that users can authenticate, data is available, integrations are working, and monitoring is in place. Those checks help catch problems before recovery expands to the next tier of systems.

Speed still matters, but control matters just as much. A fast restore can create more work if the wrong data comes back, if access controls are missing, or if an application returns without the systems it needs to run. The stronger approach is to restore in phases, confirm that each critical service is working, and then continue expanding recovery as the environment stabilizes.

Stage 5: Turn recovery lessons into stronger continuity

Once critical services are restored, teams still need to understand what worked, what slowed them down, and where the recovery plan did not match reality. That follow-through is what turns cloud backup and recovery from a response activity into an ongoing resilience practice.

The first step is reviewing the recovery itself. Teams should ask questions such as:

• How quickly did teams detect suspicious activity?
• Were clean recovery points easy to identify?
• Which validation steps took longer than expected?
• Where did restore workflows slow down?
• Were the right people involved at the right time?

These answers can reveal gaps that are not always technical. A recovery may succeed and still expose problems with decision-making, communication, approvals, or handoffs between teams.

Those findings should feed directly into the next version of the recovery plan. If a critical application depended on a system that was not documented, update the dependency map. If access controls slowed restoration, clarify the approval process. If recovery testing missed a key workload, add it to the next exercise. If business leaders lacked visibility into what was restored and what was still offline, improve reporting and escalation paths.

Regular testing is what keeps this work grounded. Tabletop exercises, isolated restores, clean recovery testing, and cross-cloud recovery validation help teams find issues before a real incident forces them to learn under pressure. They also help give leaders better evidence of where the organization is ready and where it still has work to do.

Over time, the goal is a recovery program that gets sharper after every test and every incident. Teams are better prepared, recovery steps are better understood, and the organization has a clearer path for keeping essential operations running through disruption.

Turning Cloud recovery into business resilience

 Cloud backup and recovery now plays a larger role than traditional data protection alone. It is the connected process of detecting recovery risk, protecting backup data, validating clean restore options, and restoring critical services when production environments can no longer be trusted.

In a cyber incident, those activities cannot operate as separate handoffs. Threat context should inform which backups are reviewed. Backup protection should preserve the recovery options teams may need. Validation should determine what is ready to restore. Restoration should bring back the services the business depends on in a controlled order.

A backup that cannot be trusted, tested, or restored at the right time may not give the business the outcome it needs. A restore process that ignores identity, application dependencies, or business priorities can leave systems technically recovered but operationally incomplete.

The larger opportunity is to treat recovery as an ongoing resilience practice. That means testing plans before an incident, updating dependency maps as environments change, and using each exercise or recovery event to improve the next response.

Organizations that recover faster are not necessarily those with the most copies of data. It is imperative to know which data is usable, which services matter most, and how to restore them under pressure.

The challenge is to make recovery readiness as operational as detection and response. Cloud backup and recovery provides a practical foundation for that work when it is treated as a continuous path from risk detection to business restoration.

Organizations should build that muscle to help be better positioned to restore clean data, recover critical services, and keep the business moving when disruption hits.

Why is sensitive data exposure the biggest security gap?

Data is the invaluable fuel that propels modern businesses. So, organizations have made it a priority to heavily invest in sophisticated security tools.

However, according to Varonis’ 2025 State of Data Security Report, 90% of organizations still have exposed sensitive cloud data. Similarly, 88% of organizations have stale but enabled ghost users.

But that’s not all. According to IBM’s Cost of a Data Breach Report 2025, 53% of breached organizations reported compromised customer PII. These statistics paint a vivid image: though data is central to businesses, visibility and overall security remain critical issues. 

Data is no longer confined to structured databases. It exists across files, emails, cloud platforms, SaaS applications, and endpoints. Much of it is unstructured, duplicated, or unmanaged, making it difficult to track and protect.

AI is amplifying this problem. About 40% of files uploaded to generative AI tools contains sensitive information, often without governance or oversight. As AI adoption grows, so does the number of systems and identities interacting with data.

Without visibility into what data exists and where it resides, organizations cannot effectively secure it. This lack of visibility is the root of the modern data security problem.

What are the pillars of data and AI security?

To address the challenge of data exposure, organizations need a structured approach that brings consistency and control to how data is managed. Data and AI security is built on three core pillars: Data Discovery, Data Classification, and Data & AI Access Governance.

Each pillar addresses a prominent gap:

• Discovery provides visibility into where data resides across environments. This includes structured systems such as databases, as well as semi-structured and unstructured sources that are often overlooked.
• Classification adds context by identifying the type and sensitivity of data. It enables organizations to distinguish between operational data, sensitive personal information, financial records, intellectual property, and other high-risk categories.
• Access governance enables organizations to verify that data is used appropriately. It defines who or what can access data, under what conditions, and with what level of control.

These three pillars do not exist independently. They create a connected system that fully covers data and AI security. Discovery identifies the complete data landscape, classification defines the appropriate sensitivity, and access governance enforces control based on that context.

This model even extends beyond human users to include machine identities such as AI models. In modern environments, these non-human identities often represent a significant portion of data access activity. Bringing these pillars together can help organizations move from fragmented security controls to a unified, policy-driven approach.

How can organizations discover and classify sensitive data?

Discovery and classification are foundational to a successful data security model. Yet, they are often the most difficult to implement effectively.

This is because modern data environments are highly fragmented. Sensitive information is spread across multiple cloud platforms, on-prem systems, SaaS applications, and endpoints. A significant portion of this data is unstructured, making it harder to identify and categorize.

Some of the most notable challenges include:

• Shadow data that exists without knowledge, approval, or security oversight.
• Inconsistent formats across structured and unstructured data.
• Rapid data growth due to AI adoption that outpaces manual classification efforts.

To address this, organizations need scalable discovery capabilities and classification frameworks. Proper classification can assign meaning to the vast amounts of existing data. This typically includes categories such as PII, protected health information (PHI), PCI, intellectual property, and keys and secrets.

The value of classification comes from how it is used. Once data is classified, organizations can effectively apply retention and deletion policies, restrict or monitor access, and enable masking or redaction for sensitive fields.

At scale, a mature discovery and classification approach does not just fulfill coverage but also helps produce meaningful outcomes. This can include reduced exposure, improved policy enforcement, and measurable risk reduction.

What are the major risks of overpermissive access?

According to research by ReliaQuest, 99% of cloud identities are over-privileged. On a similar note, a 2025 study by the Ponemon Institute highlights that 61% of US firms have suffered from insider data breaches in the past two years, with the average cost of such incidents being a staggering $2.7 million.

This proves that even when organizations understand their data, access remains one of the weakest points in security.

Overpermissive access occurs when users, applications, or service accounts have more access to data than they need. This issue is widespread because access controls are often granted broadly for convenience and rarely revisited.

The impact is significant. Excessive access increases the likelihood of accidental exposure, insider risk, and exploitation during a breach.

To address this, organizations must first carefully inspect access patterns. This includes finding out who is accessing sensitive data, what systems or identities are involved, and whether that access aligns with business needs.

Particular attention must be given to privileged accounts and service identities. These often have extensive permissions and can access large volumes of sensitive data across systems.

In this landscape, effective access governance is key. This requires:

• Aligning access policies with data classification.
• Continuously monitoring usage patterns.
• Identifying and remediating access drift over time.

By reducing unnecessary access, organizations help limit their attack surface and improve overall data protection.

How should organizations govern data used by AI systems?

The adoption of AI is rapidly spreading across every facet of modern businesses. This introduces a new layer of complexity in how data is accessed, processed, and exposed.

Training datasets often include large volumes of data sourced from across the organization. Without proper classification and governance, these datasets may contain sensitive or regulated information.

This creates risk at multiple stages:

• During data preparation and training.
• When models interact with live data.
• Through outputs that may unintentionally expose sensitive information.

So, classification must precede model training. This means validating and classifying all data used in datasets and removing sensitive information when necessary.

Likewise, after deployment of AI tools, data teams must continuously assess how models use and expose data. They also should apply appropriate control mechanisms such as masking or redaction where needed.

AI systems should not be treated as separate from data security. They are an extension of how data is used and must be governed accordingly. By integrating such data and AI security capabilities into the broader AI development lifecycle, organizations can help reduce risk while still enabling innovation.

How does data classification power regulatory compliance?

Regulatory compliance depends on the ability to identify and control sensitive data. Frameworks such as GDPR, HIPAA, and PCI DSS define specific requirements for how data must be handled. However, these requirements cannot be enforced without first understanding where regulated data exists.

This is why compliance programs fail without a proper data foundation.

In such cases, data classification acts as the backbone for compliance, mapping data to regulatory categories. It allows organizations to apply targeted controls based on data sensitivity and enforce critical data lifecycle policies.

This opens up a myriad of essential capabilities:

• Enforcement of retention and deletion policies
• Restriction of access to regulated data
• Implementation of crucial privacy controls

It also simplifies audit processes. Organizations can demonstrate where sensitive data resides, how it is protected, and who has access to it. Access governance further strengthens compliance by ensuring that only authorized identities can interact with regulated data.

Together, data classification and access controls reshape compliance for the modern, AI-enabled era.

How Does Commvault Cloud Help You Discover and Govern Workload Protection?

Effective data protection depends on knowing what you have before an incident occurs – not discovering gaps during recovery. Commvault Cloud is designed to help organizations continuously discover, classify, and apply backup policies across hybrid and multi-cloud assets, so coverage stays current as environments change.

Commvault Cloud begins with AI-enabled discovery –automatically inventorying cloud-native and hybrid assets, identifying resources without policy coverage, and bringing workloads under centralized governance in Command Center. Policies can be applied consistently across accounts, regions, and clouds, with real-time visibility into protected status and cost. Because environments change continuously – new workloads deployed, configurations updated, cloud resources spun up – discovery is designed to run as an ongoing process rather than a one-time assessment, helping teams maintain accurate coverage without manual audits.

• AI-enabled discovery: Continuously inventories cloud-native and hybrid assets, identifies coverage gaps, and brings new workloads under centralized policies.
• Centralized policy engine: Command Center applies consistent retention schedules, backup frequency, and copy policies across multi-cloud, hybrid, and on-premises workloads from a single interface.
• Commvault Threat Scan: Continuously monitors backup data for anomalies, encryption activity, and malware indicators so security teams can act before recovery begins.
• Cross-region and cross-cloud copies: Backup copies can be created across regions and cloud providers to support compliance, data residency requirements, and resilience posture.

Why Do Fragmented Tools Fail at Recovery Time?

89% of organizations operate in environments environments with more than one cloud, including hybrid cloud and multi-cloud set ups, yet most recovery failures don’t stem from a lack of backup jobs – they stem from protection that wasn’t built for the environment being recovered. Workloads spread across cloud databases, SaaS platforms, Kubernetes clusters, and on-premises systems each have different backup requirements, and point tools designed for one environment rarely translate cleanly to another.Workloads spread across cloud databases, SaaS platforms, Kubernetes clusters, and on-premises systems each have different backup requirements, and point tools designed for one environment rarely translate cleanly to another.

Recovery failures surface the gap between a backup that ran and a service that actually restarts. Crash-consistent snapshots may restore raw data while leaving transaction logs, service dependencies, and cluster configurations in an inconsistent state – meaning the application cannot start even when the data is present. Unified data protection can help address this by ensuring workloads are protected in a way that reflects how they operate, and by validating recovery readiness before an incident forces the question.

Commvault Cloud supports security leaders who require audit-ready recoverability, IT teams managing hybrid and multi-cloud environments, and cloud and compliance stakeholders responsible for protecting and validating critical workloads. Commvault was recognized in the IDC MarketScape: Worldwide Cyber-Recovery 2025 Vendor Assessment for strengths in cyber recovery architecture, security ecosystem integration, and workload breadth.

• Continuous backup monitoring: Threat Scan monitors backup data for malware indicators, encryption activity, and anomalous behavior –with alerts integrated into SIEM and SOC tools for coordinated incident response.
• Commvault Cleanroom Recovery: Designed to stage restoration in an isolated environment so teams can validate data integrity and confirm systems are threat-free before returning to production – reducing reinfection risk.
• Cleanpoint Identification: Designed to help pinpoint when data may have become compromised, providing more precise selection of a verified recovery point and supporting minimization of data loss.
• Orchestrated service recovery: Command Center workflows can restore dependent services in sequence – helping reduce the manual coordination burden during high-pressure recovery events.
• Scalable on-premises protection: HyperScale supports on-premises protection for hybrid environments, with streamlined onboarding and management through Command Center.

Before unified data protection, the most dangerous moment in incident response was often the restore itself – when teams discovered coverage gaps they didn’t know existed. With Commvault Cloud, teams can move from reactive gap discovery to proactive governance: understanding which workloads are protected, at what policy level, and whether recovery points have been validated. That shift – from hoping a backup worked to demonstrating that it can – can make the difference between a measured recovery and an extended outage.