Data flow
Prompts, retrieval sources, context, outputs, logs and retention.
MLOps & AI security
LLM application security assessment
Review data exposure, tool use, retrieval, prompt boundaries and abuse paths in LLM-backed applications. 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.
Tool boundary
Actions, identities, arguments, approvals and result handling.
Abuse testing
Injection, data extraction, cross-tenant access and unsafe automation.
Controls
Isolation, validation, monitoring, fallback and incident response.
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
- 01LLM system and trust-boundary map
- 02Validated abuse cases with evidence
- 03Control and architecture remediation
- 04Retest and monitoring plan
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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