Capabilities in Depth

AI Security and Governance

Agents, models, RAG pipelines, and MCP servers create paths to customer data that nobody can enumerate by hand. Relyance splits the problem in two: Classification & AI Governance finds and fixes risk before the code ships, and AI Runtime Security & Enforcement keeps AI within bounds in production.

Classification & AI Governance

Relyance classifies data, maps data flows, fixes risky flows and unsafe access, and automates AI governance.

Features	Use cases
Inventory of every AI agent, model, RAG pipeline, and MCP server	Answer, with evidence, which AI paths can reach customer data
Permission reach and data provenance computed per component	Know what each agent can touch before you approve it
Context-aware classification of structured and unstructured data at petabyte scale	Build an accurate picture of the sensitive data you hold
End-to-end lineage from code to cloud to third party	Find and fix risky data flows and unsafe access
Detection at merge in CI/CD	Catch an over-scoped agent before it ships
Impact analysis and fix linked to each finding	Cut a risky path knowing exactly what depends on it

AI Runtime Security & Enforcement

Relyance stops an AI feature from taking an action or leaking data outside the rules set by your company and the app developer.

Features	Use cases
Blocking of unapproved AI actions	Ship AI features that stay inside the rules and policies you set
Runtime blocking of over-scoped AI	Contain an over-scoped agent in production
Monitoring of classified data in motion, with blocking of misuse and leaks	Block, mask, or escalate a sensitive data leak before it leaves your environment

Privacy Automation

Five modules automate privacy operations.

Universal ROPA

Most ROPAs describe systems as they were at the last review; this one regenerates from live flows.

Features	Use cases
Records self-update from live data flows	Survive a 48-hour GDPR Article 30 audit
Multi-jurisdiction coverage	Pass privacy diligence during M&A without a scramble
Lawful basis and retention tracked per activity	Run a current ROPA without a team maintaining it by hand

Data Mapping

Data Mapping builds the map from your systems themselves.

Features	Use cases
Automated mapping	Track data inventory, usage, and flow without manual upkeep
Multidimensional classification	Know your crown-jewel data, who's data it is, what state it is in, where it lives, and how long you retain it
Drift detection	Catch data misuse when a flow changes

DSR Automation

A data subject request is a distributed systems problem: the requester's data sits across SaaS tools, databases, and internal APIs, and the clock starts when the request lands.

Features	Use cases
Unified request portal with identity verification	Verify who is asking before you move any data
Autonomous fulfillment across SaaS, databases, and APIs	Meet GDPR and CCPA deadlines through a spike in requests, without hiring
Audit log per request	Show a regulator exactly what you did, and when

Assessments

Regulators add assessment obligations faster than privacy teams add headcount.

Features	Use cases
Automated DPIAs, PIAs, and AI risk assessments	Finish DPIAs and AI risk reviews before launch
Live obligation-gap detection	Catch obligation gaps as the EU AI Act, NIST RMF, ISO 42001, and new state laws in Texas, Colorado, and Florida take effect
Risk scoring	Cover thousands of assessments with a small team

A correct cookie banner cannot cover trackers you do not know are running.

Features	Use cases
Tracker discovery and governance	Catch a tracker sending health data before it becomes an incident
Consent provenance	Prove what a user consented to, and when
Audit-ready consent vault	Walk into a cookie audit with the evidence already filed

How it fits together

Close an unsafe flow and the graph updates: the ROPA reflects the change, and the related assessment gap closes. For the mechanics, read How Relyance AI Works.