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AI abuse-case matrix for launch reviews

AI launch reviews work best when product, engineering, security, and leadership can see the same risk map. An abuse-case matrix gives the team a practical way to discuss what can go wrong, what controls exist, and what launch decision remains.

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Use abuse cases to make launch risk concrete

AI risk conversations can become vague quickly. The abuse-case matrix keeps the team focused on the actual workflow: what the model can see, what tools it can call, what data it can expose, what humans review, and what evidence leadership needs before release.

This is not a prompt recipe or exploit guide. It is a decision artifact for launch readiness.

AI abuse-case matrix outline

Abuse area Review question Decision output
Instruction conflict Can user, retrieved, or tool-provided text steer the workflow away from intended rules? Required guardrails, test notes, and residual-risk owner.
Data exposure What sensitive, regulated, customer, or internal data can the workflow access or reveal? Data boundary, logging expectation, and approval criteria.
Tool action What can the system do on behalf of a user, and where is human confirmation required? Action limits, escalation points, and rollback path.
Identity and access Whose authority does the workflow use, and how are permissions checked? Access-control gaps and launch blockers.
Monitoring and recovery How will the team detect, investigate, and correct unsafe behavior after release? Telemetry plan, response owner, and review cadence.

What a launch review should answer

  1. What is the workflow allowed to decide or do? The answer should be specific enough that engineering and leadership agree on the boundary.
  2. What data and tools does it touch? Data flow and tool-use review should happen before the launch decision, not after a surprising behavior report.
  3. What controls are already in place? Include access control, logging, human review, output constraints, rollback paths, and escalation.
  4. What residual risk is acceptable? Someone needs to own the tradeoff. The matrix should make that owner and rationale visible.

Where the lab work fits

Evening Star AI keeps Purple Team close to AI decision-support and anomaly-intelligence questions. The consulting output stays practical: launch criteria, abuse-case review, guardrail recommendations, and a plain-English risk summary.