AI Cyber Defense / Protecting AI systems used by defenders and the business
AI Cyber Defense: Separate AI confidence from incident severity
Set severity with documented operational criteria and allow an explicit unknown finding. Use model scoring only as supplementary context until calibrated on representative outcomes.
Environment and inputs: Enterprise AI assistants, security copilots, retrieval systems and application-controlled tool executors; framework-independent design.
Scenario and goal
A model's confident language can encourage escalation even when evidence is weak.
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
Record evidence completeness, corroboration, business impact and the model's proposed conclusion as separate fields.
Validate with an authorized exercise
Compare two synthetic cases with identical evidence and different generated confidence wording; verify severity remains consistent and uncertainty is preserved.
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.