Agent Security Risk
Diagnostic

We maintain a live, heavily governed registry of all AI agents deployed across the enterprise, tracking their purpose, owner, and active status.

Agents operate using short-lived credentials that dynamically inherit only the strict permissions of the invoking user, preventing "super-user" exploitation.

Hard limits are coded into the API gateway to prevent rogue or looping agents from executing thousands of queries and draining cloud compute budgets.

Agents are programmatically restricted from cross-referencing distinct data domains (e.g., mixing HR payroll data with marketing data) without explicit secondary authorization.

Sensitive customer and employee data is automatically redacted or tokenized before entering the context window of any external language model.

Agent outputs are scanned by a deterministic rules engine for sensitive information before being transmitted to external systems, clients, or untrusted endpoints.

The underlying architecture strictly separates system instructions from user-provided data inputs to mitigate the risk of indirect prompt overrides.

Agent commands (e.g., executing a SQL query or initiating a bank transfer) must pass through a strict validation filter that ensures syntax safety before the action executes.

A centralized breaker exists in the Control Plane to instantly isolate an agent if it exhibits anomalous API call volumes or begins hallucinating actions.

Every autonomous action is logged immutably, capturing the exact prompt, weights, and data state at the time of execution for regulatory audit purposes.

Workflows carrying high financial, legal, or reputational risk are hardcoded to pause and require explicit human authorization before final execution.

Internal AI initiatives are formally mapped against the risk categories of authoritative standards like the EU AI Act or the NIST AI RMF prior to deployment.