AI Cyber Defense / Protecting AI systems used by defenders and the business
AI Cyber Defense: Constrain AI assistant network egress
Route fetch and send operations through an egress-enforcing service with narrow destinations and scoped identity. Restrict redirects and internal addresses for external fetch tools.
Environment and inputs: Enterprise AI assistants, security copilots, retrieval systems and application-controlled tool executors; framework-independent design.
Scenario and goal
A manipulated assistant may attempt to send retrieved information to an unapproved destination.
Why it matters
This path concerns attacks against AI-enabled systems used by the defender or business. It complements defenses against attackers who use AI as a tool. Treat model inputs, retrieved evidence and generated plans as untrusted proposals at execution boundaries. A model may assist analysis, but the application remains responsible for tenant isolation, authorization, resource budgets and the integrity of approvals and receipts.
Evidence to collect
Observe tool-layer outbound requests, destination policy, data classification and the authenticated task owner.
Validate with an authorized exercise
Use a synthetic document and approved test endpoint to verify intended retrieval; confirm an unapproved outbound destination is denied before any document content is transmitted.
Rollout and checks
Use synthetic evidence and mocked write tools for adversarial evaluation. Version the model, prompts, tool contracts and retrieval configuration together. Keep high-impact permissions outside the model and retain an operator takeover path. When an evaluation fails, correct the enforceable boundary and rerun the same fixture rather than relying only on a stronger wording of the prompt.
Scope and limits
This guide proposes a defensive test; it is not a tested production design. Check platform support and local requirements. References explain the mechanisms but do not certify the design. Behavior alone does not prove AI involvement.