Governance and posture management guardrails
Continuously reduce your AI × identity × data blast radius.
Maps what each agent can reach against what it has used and what its task requires. The output is a ranked list of removable access, with the usage evidence to remove it safely. Every finding ships with a concrete Relyance AI fix recipe — the exact scope change, the identity to change it on, and the blast radius it closes.
The questions we answer
“What can each agent reach vs. what does it actually need?”
Granted access compared with observed use per agent — the gap is the finding.
“Which agents, MCP tools, permissions, and data paths have drifted?”
Tool access, permissions, and paths tracked as they change — not sampled at audit time.
“Which agents can combine individually safe capabilities into a dangerous execution path?”
Toxic combinations scored across the whole graph, not permission by permission.
“What excess access can be removed without breaking the app?”
Safe-to-remove access computed from observed usage — with the evidence to act on it.
“What sensitive data, whose, where, in what state, and how much? What’s the retention?”
A live inventory with owner, location, state, volume, and retention — current from real flows.
The coverage doesn’t stop here.
Code review for data exposure and remediation
Block dangerous data access paths before a new release.
AI runtime guardrails
Enforce permitted data use. Prevent data exfil.
Governance and posture management guardrails
Continuously reduce your Al x identity x data blast radius.
End-to-end privacy automation
Meet every privacy obligation without growing the privacy team.