Threat scenarios
Actors, assets, unacceptable outcomes and test boundaries.
MLOps & AI security
AI application red teaming
Exercise agreed misuse, data and tool-abuse scenarios against an AI-enabled product before or after launch. Ship and operate model-backed systems with clear data, deployment and monitoring boundaries.
01 / Scope
Work on the system, not around it.
The engagement starts with the architecture and working constraints already in place. We agree the risky paths, make the changes visible and leave ownership with the team running the system.
Evaluation set
Representative prompts, content, tools and user states.
Execution
Manual and automated adversarial testing with impact limits.
Improvement
Mitigation, regression tests, retest and residual risk.
02 / Method
Ship and operate model-backed systems with clear data, deployment and monitoring boundaries.
Scope and access are agreed before the work begins. Decisions, changes and residual risk remain visible throughout the engagement.
Trace data and model flow
Start with evidence, owners and the constraints that shape the safe solution.
Control the release path
Prioritise by practical impact and implement with the people who operate the system.
Observe behaviour in production
Test the changed path, document the decision and hand over a backlog that still makes sense.
03 / What remains
- 01Authorised red-team plan
- 02Scenario and evaluation set
- 03Evidence-backed findings
- 04Regression suite and retest record
Related work
Continue with the adjacent problem.
04 / Talk to Gritt
What is happening in the system?
Send the cloud, workload or deadline. An engineer will reply and tell you whether this is the right scope.
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