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Commvault® Cloud Data Posture 360

Commvault Data & AI Security

Commvault Data & AI Security enables enterprises to discover and classify sensitive data across structured and unstructured environments — then enforce access governance to control what human users and AI systems can access, query, or use.

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100,000+ customers worldwide
15x Leader in Gartner® Magic Quadrant
SOC 2, ISO, FedRAMP Certified

99%

of enterprises have exposed sensitive data that can be easily surfaced by AI

_{2025 Varonis State of Data Security Report}

53%

of breaches involve customer PII

_{2025 IBM Cost of a Data Breach Report. Pg. 8}

$4.99M

average cost of a data breach (2026)

_{https://www.ibm.com/reports/data-breach}

Key Capabilities

Discover, classify, and protect your critical data

Discover and classify nearly any data type

Scan, analyze, and classify data across structured, semi-structured, and unstructured data sources.

Out-of-the-box and custom classifiers

Use pre-built sensitive data classifiers or build your own that suit your data classification schema or types of data you care about.

Enable access controls and governance

Help keep sensitive data secure by controlling who or what applications or AI models have access to specific data sets.

Enable responsible data use by AI models

Confidently use and protect sensitive or regulated data by instituting access controls that permit access to data only by those individuals or AI models with appropriate permission.

Protect privacy

Apply controls to safeguard data and AI outputs based on a user (or model’s) access levels. Mask or redact information that is beyond their permissions.

Simple Access Governance Integrations

Unified access governance to data wherever you keep it with dozens of integrations directly into your workflows and databases, including:

Amazon S3

Amazon Redshift

Amazon RDS

MongoDB

Snowflake

Databricks

Frequently Asked Questions

What is data discovery and why is it important?

Data discovery is the process of scanning and identifying data across your organization to understand what exists, where it lives, and whether it contains sensitive information.

It matters because most organizations lack visibility into their data. Without discovery, sensitive information like PII, financial data, or intellectual property can remain exposed, increasing the risk of breaches, compliance violations, and misuse—especially with AI tools.

How does data classification help reduce risk?

Data classification organizes data based on sensitivity and business value—such as identifying PII, PCI, or confidential data.

Once classified, organizations can apply targeted policies to protect that data, including access restrictions, masking, retention rules, or deletion. This allows security teams to prioritize what matters most and reduce unnecessary exposure.

How does access governance apply to AI systems?

Access governance extends beyond human users to include AI models and applications.

Organizations need to control:

What data AI models can access for training
What data can be included in prompts
What outputs can be generated and shared

By applying policies to AI interactions, organizations can prevent sensitive data leakage and support responsible AI usage.

How do data discovery and access governance support compliance?

Together, they provide the foundation for meeting regulatory requirements like GDPR, HIPAA, and PCI DSS.

Data discovery identifies where sensitive data resides
Classification defines how it should be protected
Access governance enforces who can use it and how

This combination helps organizations demonstrate control, reduce risk, and maintain audit readiness.

Why is data visibility important for securing AI and analytics workflows?

Data visibility gives organizations a clear understanding of where their data lives, how it’s used, and who has access to it. In AI and analytics workflows, data often spans cloud, on-premises, and SaaS environments, making it harder to track. Without strong visibility, sensitive data can be unintentionally exposed or misused. By continuously discovering and mapping data, organizations can identify risks early, enforce appropriate protections, and maintain control over how data is used in AI models and pipelines.

How can organizations reduce the risk of sensitive data exposure in AI pipelines?

Reducing risk in AI pipelines starts with knowing what data is being used and applying controls at every stage. Organizations should discover and classify data before it enters pipelines, enforce strict access governance, and monitor usage in real time. Masking or anonymizing sensitive data, along with maintaining audit trails, further limits exposure. These practices help keep AI workflows secure while still enabling teams to innovate with data.