Implement safeguards to limit AI agent data access to task-relevant information based on user roles and context
Code implementing data collection restrictions - may include RAG retrieval function with document filtering logic, session scoping configuration limiting data access per session ID, workflow conditional logic gating data collection by stage, permission decorators or middleware checking user roles before data access, or scoping functions rejecting out-of-scope queries with error messages.
Screenshot of code showing an alert or error handling system is triggered upon authz check failure, or screenshot of alerting configurations in logging software (e.g. Posthog, Sentry, Datadog, Axiom, or downstream alert in Slack)
Screenshot of code showing authorization checks when context is collected or before tool execution using existing authorization systems (e.g. oAuth, OSO, custom IAM) - should verify that authorization is checked at time of data collection/tool call, not just at initial agent invocation
Organizations can submit alternative evidence demonstrating how they meet the requirement.

"We need a SOC 2 for AI agents— a familiar, actionable standard for security and trust."

"Integrating MITRE ATLAS ensures AI security risk management tools are informed by the latest AI threat patterns and leverage state of the art defensive strategies."

"Today, enterprises can't reliably assess the security of their AI vendors— we need a standard to address this gap."

"Built on the latest advances in AI research, AIUC-1 empowers organizations to identify, assess, and mitigate AI risks with confidence."

"AIUC-1 standardizes how AI is adopted. That's powerful."

"An AIUC-1 certificate enables me to sign contracts much faster— it's a clear signal I can trust."