What is Data & AI Security?
Data & AI Security is the discipline of discovering, classifying, and governing sensitive data across cloud, hybrid, and on-premises environments—and securing the AI systems that use it. It addresses the full spectrum of enterprise cloud data security: data security posture management (DSPM), data loss prevention (DLP), encryption key management, access governance, and AI-specific threats like model poisoning and training data compromise. Commvault Cloud unifies these capabilities in a single platform—helping organizations reduce exposure, enforce policy, and enable responsible AI across multi-cloud and hybrid environments.
Key Takeaways
Effective cloud data security requires more than perimeter defense. It demands visibility into every sensitive data asset, governance of every access point—human or AI—and protection for the AI pipelines that now power enterprise operations.
Data & AI Security covers five pillars: data discovery, classification, access governance, encryption/DLP, and AI threat defense – across multi-cloud and hybrid environments.
90% of organizations have sensitive cloud data exposed that AI systems can inadvertently surface – making classification and access controls essential before AI adoption.
40% of files uploaded to generative AI tools contain PII or PCI data – creating significant compliance and breach risk without proper data governance in place.
57% of cyber attacks now begin with compromised identities – making data access governance and overly-permissive sharing a critical security gap to close.
AI introduces new attack surfaces: data poisoning, model inversion, adversarial attacks, and prompt injection – requiring security controls designed specifically for AI workloads.
Commvault Cloud unifies data discovery, classification, and AI access governance – covering structured, unstructured, and semi-structured data across on-premises, cloud, and hybrid environments.
Why Data & AI Security Matters
The Attack Surface Has Changed Fundamentally
Traditional security built on network perimeters no longer reflects how enterprises operate. Data is now distributed across dozens of cloud providers, SaaS platforms, and AI systems—and the perimeter has dissolved. Attackers have adapted: 57% of breaches now begin with compromised identities rather than network intrusion. AI adoption is accelerating this risk, with 40% of files uploaded to generative AI tools containing customer or employee PII or PCI data. Organizations that cannot see their sensitive data, control who accesses it, and secure the AI systems that consume it are operating with significant blind spots that attackers are actively exploiting.
Data Protection: Visibility Across Every Environment
Automated data discovery and ML-based classification give security teams continuous visibility into what sensitive data they have, where it lives, and who can access it—across on-premises systems, public clouds (AWS, Azure, Google Cloud), SaaS platforms, and AI pipelines. Without this foundation, protection is applied blindly and compliance reporting is unreliable. Organizations can’t protect what they can’t see — and 90% have sensitive cloud data exposed that AI can inadvertently surface.
Learn moreCybersecurity: Governing Access for Humans and AI
With the network perimeter dissolved, identity and data access have become the new security boundary. Data & AI Access Governance applies classification-based policy controls to restrict access for both human users and AI models—with data masking, redaction, and real-time enforcement across cloud-native and on-premises environments. Powered by Satori, Commvault extends these controls to structured data platforms including Snowflake, Redshift, Databricks, and Microsoft Fabric, with LLM prompt monitoring to help prevent sensitive data from leaking through generative AI responses.
Learn moreCloud Resilience: Securing AI Models and Training Data
AI systems introduce security risks that traditional cloud data security tools were not built to address. Data poisoning, model inversion attacks, adversarial inputs, and prompt injection can corrupt AI behavior, expose training data, or weaponize deployed models. Commvault helps protect against poisoned models, prompt injection, and machine identity attacks, while immutable, air-gapped backup of AI training data and vector databases helps enable the recoverability of AI workloads across hybrid and multi-cloud environments.
Learn moreThe Five Pillars of Cloud Data Security
A Framework for Enterprise Cloud Data Security
Effective enterprise cloud data security requires five interconnected pillars—from discovering what data you have, to classifying it by sensitivity, to governing who and what can access it, to enforcing encryption and DLP controls, to defending AI-specific attack surfaces. In the cloud, responsibility for most of these pillars falls on the customer, not the provider. Commvault Cloud unifies all five across multi-cloud, hybrid, and on-premises environments in a single platform.
Pillar 1: Data Discovery and DSPM
Data Security Posture Management (DSPM) provides continuous visibility into where sensitive data lives, how it is classified, and what risks surround it across multi-cloud environments. Unlike one-time scanning, DSPM continuously monitors data location, access permissions, and security configurations—detecting misplaced data, insecure configurations, and unauthorized access attempts in real time. Commvault Cloud’s DSPM capabilities enable discovery across structured, unstructured, and semi-structured data in live and backup environments.
Pillar 2: Classification and Policy Enforcement
Classification uses machine learning to tag sensitive data by type—PII, PHI, PCI, financial records, API keys, intellectual property—so protection and access controls can be targeted where they matter most. Commvault Cloud’s DSPM capabilities include more than 100 pre-built ML classifiers with support for custom classifications, covering structured, unstructured, and semi-structured formats. Classification also helps prevent sensitive “shadow data” from being unknowingly used in AI training or returned in generative AI responses. Without classification, other tools mechanisms such as DLP and access governance policies cannot be applied accurately.
Pillar 3: Access Governance and DLP
Access Governance applies classification-based DLP controls for both human users and AI models, with real-time enforcement across on-premises and cloud-native environments. The Satori-powered integration extends these controls agentlessly to structured data platforms, monitoring LLM prompts and restricting AI model access to sensitive training data based on defined policies.
Data Loss Prevention (DLP) monitors and controls how sensitive data moves across cloud environments—blocking unauthorized transfers, flagging overly-permissive sharing, and applying masking or redaction to protect privacy.
Pillar 4: Encryption and Multi-Cloud Key Management
In multi-cloud environments, encryption key management is a critical and often underestimated customer responsibility. Relying solely on cloud provider–managed keys means the provider can access your data and that a key compromise could be catastrophic across all environments. Commvault Cloud supports Bring Your Own Key (BYOK) and Hold Your Own Key (HYOK) options, with integration into customer- or partner-managed Hardware Security Modules (HSMs). For regulated workloads with data residency requirements, Commvault Geo Shield maintains in-region control of data, operations, and encryption keys—meeting sovereignty requirements across national and regional regulatory frameworks including GDPR, NIS2, and DORA.
Pillar 5: AI Threat Defense
AI introduces attack surfaces that traditional cloud data security tools were not built to defend. The three primary AI threat vectors are: the AI supply chain (poisoned training data and malicious models), the AI model itself (prompt injection, adversarial attacks, and model inversion), and machine identities (AI agents and service accounts with excessive permissions). Commvault Cloud helps protect against all three—with immutable, air-gapped backup of AI training datasets and vector databases; anomaly detection across AI data inputs and outputs; and policy-based governance for access. For training data integrity, Commvault helps maintain version-controlled, validated copies so organizations can recover clean training data if a poisoning attack is detected.
Data & AI Security in Practice
Securing Data Across Every Organization and Use Case
Data & AI Security challenges vary by organizational role, cloud maturity, and AI adoption stage. Commvault Cloud adapts to help enterprise security teams reduce exposure, regulated industries meet compliance requirements, and AI and data teams securely activate data for model development—all from a unified platform.
Reducing Sensitive Data Exposure at Scale
Large enterprises with sprawling data estates across on-premises, multi-cloud, and SaaS environments often lack visibility into where their most sensitive data lives, who has access to it, and how AI systems are consuming it. Commvault Cloud’s DSPM capabilities help security teams identify data hotspots, close overly-permissive sharing gaps, apply consistent DLP and access controls, and surface AI-specific risks—reducing blast radius and helping make sure sensitive data is not exposed when breaches occur or surfaced by AI inadvertently.
Meeting Global Compliance Requirements for Data and AI
Financial services, healthcare, and government organizations must demonstrate control over where personal and sensitive data lives, how it is accessed, and how encryption keys are managed—especially in multi-cloud and cross-border environments. Commvault Cloud’s discovery, classification, BYOK/HYOK key management, and reporting capabilities provide the controls and audit-ready reporting needed to support GDPR, HIPAA, CCPA, PCI DSS v4.0, DORA, NIS2, and the EU AI Act—helping compliance teams move from reactive reporting to proactive governance.
Securing and Activating Training Data for AI
AI and data teams need governed access to historical and operational data to train models and power analytics—but sourcing that data safely is a persistent challenge. Commvault Data Activate provides a governed, self-service workspace to discover, classify, and export trusted backup data in open AI-ready formats (Apache Parquet, Iceberg), with built-in classification, redaction, and audit trails. Immutable, version-controlled backup of AI training datasets helps protect against data poisoning and model drift by giving teams the ability to recover clean, validated training data at any point in time.
Frequently Asked Questions
What is Data & AI Security?
Data & AI Security is the practice of discovering, classifying, and governing sensitive data across an organization’s entire data estate—and extending those controls to the AI models and pipelines that consume it. It gives organizations the visibility and policy enforcement needed to reduce exposure, meet regulatory requirements, and deploy AI responsibly.
What are the core principles of enterprise cloud data security?
Enterprise cloud data security is built on five core principles: continuous visibility into sensitive data (DSPM), accurate classification by sensitivity and risk, policy-driven access governance for humans and AI, robust encryption with customer-controlled key management, and AI-specific threat defense. Because cloud providers secure the infrastructure beneath your data, responsibility for all five principles falls on the customer under the shared responsibility model—not the provider.
How does data security address multi-cloud and key management complexity?
Multi-cloud environments fragment data security across different provider tools, policies, and encryption standards—creating gaps between platforms and making unified visibility and control difficult. Commvault Cloud addresses this with a single platform spanning on-premises, AWS, Azure, Google Cloud, and SaaS. For encryption key management, Commvault supports Bring Your Own Key (BYOK) and Hold Your Own Key (HYOK) options with Hardware Security Module (HSM) integration, helping make sure customers—not providers—retain control of their encryption keys across all environments.
What are the main AI-specific security threats organizations face?
AI introduces three primary threat vectors: the AI supply chain (poisoned training data and malicious models), the AI model itself (prompt injection, adversarial attacks, and model inversion attacks that extract sensitive training data), and machine identities (AI agents and service accounts with excessive permissions). Commvault Cloud helps protect against all three—with immutable backup of AI training datasets, anomaly detection across AI data flows, and policy-based governance for machine identity access.
How does Commvault Cloud approach Data & AI Security?
Commvault Cloud provides a unified Data & AI Security platform covering data discovery, classification, and access governance for both unstructured and structured data. Powered by Satori, it extends real-time, agentless access controls to platforms like Snowflake, Redshift, Databricks, and Microsoft Fabric. Commvault Data Activate bridges protection and AI activation—giving organizations governed, self-service access to trusted data for analytics and AI development.
What is the customer’s responsibility for data security in the cloud?
Under the shared responsibility model, cloud providers (AWS, Azure, Google Cloud) secure the underlying infrastructure—hardware, networking, and physical facilities. Customers are responsible for everything above: the data itself, its classification, access controls, encryption key management, compliance, and governance of AI systems that use it. This means data discovery, DLP, DSPM, identity governance, and AI security are entirely the customer’s responsibility—regardless of which cloud provider is used.
Data Discovery: See Every Sensitive Data Asset
Data & AI Access Governance
