Code review for data exposure and remediation
Block dangerous data access paths before a new release.
Each PR is diffed against the live Data Journeys™ graph. If a change opens a new path to sensitive data, the finding shows the path, the identity that can walk it, and the fix. No new path, no noise.
The questions we answer
"What sensitive data becomes reachable due to an AI code change?"
The diff is resolved against the graph: which fields become reachable, through which service, from which change.
"Which human, agent, or service identity will exercise that access?"
Each new path is tied to the identities and permission sets that can exercise it.
"Did the change create a path from untrusted input to a privileged tool or dataset?"
Untrusted-input-to-privileged-tool paths are flagged in review, with the full chain shown.
“Is it sanctioned or shadow AI?”
New models, agents, RAG pipelines, and MCP servers are inventoried as they appear in code — sanctioned or not.
"Does the new path violate residency, purpose, or customer commitments?"
New paths are checked against residency rules, declared purpose, and customer commitments.
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.