Relyance AI Capabilities
Relyance AI has two capabilities. This page covers what each does. Capabilities in Depth has the features and use cases.
| Capability | What you get |
|---|---|
| AI Security and Governance | AI inventory, permission reach, context-aware classification, guardrails at code merge and at runtime |
| Privacy Automation | ROPAs and data maps from live flows; DSR, assessment, and consent automation |
AI Security and Governance
You ship AI features with confidence without guessing what they can reach, how they can be attacked, and a leak becomes a blocked event rather than an investigation.
The first half is Classification & AI Governance: Relyance classifies sensitive data in context, maps data flows, and inventories every AI agent, model, RAG pipeline, and MCP server. For each component, it computes permission reach and provenance: which data it can touch, through which human or non-human identities and grants, and by what path. Relyance runs checks at merge in CI/CD, so an over-scoped agent surfaces while one revert still fixes it.
The second half is AI Runtime Security & Enforcement: in production, Relyance blocks data misuse, prevents data leaks, and allows only the AI actions your company and the app developer approve.
Privacy Automation
Relyance generates ROPAs and data maps from live data flows, so they stay current with no manual upkeep. It automates DSRs, assessments (DPIAs, PIAs, and AI risk), and consent on the same graph the security capability uses. When a data flow changes, the records that describe it change with it. The observed flows are your evidence.
One graph underneath
Both capabilities come from the Data Journeys. The graph Relyance builds from your connected systems. To see how, read How Relyance AI Works.