AI Cyber Defense / Protecting AI systems used by defenders and the business
AI Cyber Defense: Limit resource consumption in agent loops
Enforce budgets atomically in the orchestrator and stop or escalate at a defined boundary. Keep retries bounded and avoid restarting the entire task after a partial success.
Environment and inputs: Enterprise AI assistants, security copilots, retrieval systems and application-controlled tool executors; framework-independent design.
Scenario and goal
Repeated model and tool calls can exhaust security-platform capacity or generate costly external requests.
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
Track run identifier, active workers, model calls, tool requests, elapsed time and committed resource usage.
Validate with an authorized exercise
Use mocked tools that repeatedly fail and verify the test run stops within its budget, retains completed receipts and does not continue launching background actions.
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.