Trusted Retrieval for Agents is a narrow capability package published by AI Security Hub for agentic systems that need fresh, provenance-attached, decision-bearing security context.
Generic search is good at broad discovery. It is weak at safe control flow for action-sensitive security tasks.
This capability is built for the moment when an agent must answer questions like:
The publishable machine-readable bundle is composed of:
The capability is meant to be discoverable by:
The goal is not prettier output. The goal is better next-step control flow.
If trusted retrieval is working as intended, an agent should:
AI Security Hub is the canonical publication home for this capability package. External registries and catalogs should point back here for stable bundle URLs and supporting documentation.
The practical decision is no longer whether AI belongs in security workflows at all. It is where it creates enough leverage to justify real controls, review, and ownership.
AI risk is increasingly a system-design problem, not just a model-safety problem. If an agent can read untrusted content and take action, it needs explicit boundaries.
The useful signal today is concrete: AI risk becomes easier to manage when teams review workflows through permissions, approvals, and data boundaries instead of treating governance as a policy-only exercise.