Metomic

Applies sensitivity labels to data automatically based on content analysis and context without requiring users to manually classify documents before policy enforcement.

Applies preventative controls automatically such as blocking, encryption, alerting and user justification when sensitive data is detected.

Provides an automated incident response workflow for data loss events.

Discovers and enforces data policies for content stored in or transiting through cloud applications and storage, extending DLP coverage to SaaS environments without endpoint agents.

Detects and controls sensitive data entered into generative AI tools, applying block, redact, or warn actions before data leaves the organization.

Provides granular incident reporting on data loss events.

Ships policy templates for regulated data types such as PII, PHI and payment or financial data.

Ships policy templates for nonregulated sensitive data types such as controlled unclassified information, intellectual property and source code.

Integrates with SIEM platforms for incident response.

Discovers and classifies sensitive data (PII, PHI, PCI data, IP) across cloud object storage, relational and NoSQL databases, data lakes, and SaaS platforms using content inspection and ML classification.

Connects to cloud object storage, data warehouses, on-premises databases, and SaaS platforms for discovery and classification, with coverage depth varying by product.

Maps effective permissions to sensitive data stores across cloud IAM, database roles, and SaaS permissions, identifies over-privileged access and dormant entitlements.

Identifies data flowing into large language models and enforces data access governance and entitlement for generative AI use.

Automatically remediates discovered violations, revoking over-permissioned access, moving misplaced data to compliant storage, encrypting unprotected sensitive files.

Discovers and categorizes the organization's use of third-party AI, whether consumed as a service, installed locally, or embedded inside other applications, building a continuously updated inventory of AI usage including shadow AI.

Assesses and scores the risk of discovered AI services and embedded AI features (data handling, training-use terms, hosting, vendor posture) to drive sanction/block decisions.

Defines organizational AI usage policies and enforces them at the point of use - allowing, blocking, redirecting, or constraining specific AI services, models, and features per user, group, or data context.

Inspects prompts, uploads, and AI-generated responses for sensitive data across modalities, preventing exposure of regulated or proprietary information to third-party AI services.