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 Model Context Protocol (MCP) servers in use with user and device attribution and enforces data policies on AI agent tool traffic through an inline gateway that redacts or blocks sensitive data in prompts and responses.

Discovers and classifies sensitive data (PII, PHI, payment data, IP, secrets) across structured and unstructured stores by combining deterministic techniques such as patterns, keywords, and validators with AI/ML techniques such as unsupervised clustering and small language models. Breadth of the technique blend, and whether classification extends to prompts, model outputs, and vector databases, are the primary differentiators; products that rely on pattern matching alone sit at the low end.

Discovers and classifies sensitive data across a heterogeneous cloud estate in one inventory: object storage, managed data warehouses and lakes, cloud database services, and SaaS applications, including sources that are not supported out of the box through custom connectors. Breadth of supported sources and depth per source vary; on-premises and mainframe estates are covered under On-Premises and Mainframe Data Discovery.

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

Identifies sensitive data flowing into large language models and AI assistants such as Microsoft Copilot and ChatGPT, and enforces which generative AI services may use it, in which geographic region, and under which entitlements, reporting unsanctioned AI use. Right-sizing entitlements to stop oversharing before an AI assistant is rolled out is the most common form; blocking is usually delegated to DLP.

Acts on discovered data risks either natively or by orchestrating third-party DLP, IAM, EDRM, and ticketing controls: revoking over-permissioned access, quarantining or moving misplaced data, encrypting or masking unprotected files, and applying protection labels. Whether actions execute natively or only through integrated tools, and the breadth of available actions, are the primary differentiators; many DSPM products still leave enforcement to the integrated control.

Extends access analysis to non-human AI identities, mapping which AI agents, copilots, and stand-alone models can reach which sensitive data stores and flagging over-broad or unsanctioned model access before it is exploited. Coverage of agent frameworks and model identities, and whether findings feed entitlement right-sizing before an AI rollout, vary across products.

Improves classification precision over time through administrator false-positive flagging, classifier threshold and rule tuning, custom classifier authoring, and workflows that route uncertain results to data owners for validation or exception handling. Whether stakeholder feedback retrains the classifiers, or only suppresses individual findings, is the primary differentiator.

Produces audit trails and regulation-mapped reports such as GDPR, HIPAA, and PCI DSS data inventories from discovery and access findings, with alerts on policy violations, so that evidence of data-handling practices can be handed to auditors without manual assembly. Custom and stakeholder-specific reporting is a common weak spot across products.

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.

Automatically detects and anonymizes sensitive fields (names, addresses, contact details) inside prompts or pasted content before submission to an AI service, allowing the interaction to proceed with redacted data instead of blocking it outright.

Discovers MCP servers and AI agent integrations in use, routes agent tool calls through a governed gateway or proxy, and enforces access and data policies on agent-to-tool traffic, extending AI usage control from human prompts to autonomous agent workflows.