Most LLM deployments are trust-based: you trust the prompt, trust the retrieval, trust the output parser. When something goes wrong, there's often no reproducible record of what actually happened. A safety audit replaces some of that trust with verification.
A common gap: outputs are consumed without provenance, refusal behavior is untested against adversarial input, and there's no defined safety envelope for what the system is and isn't allowed to do.
Auditing the inference pipeline for reproducibility, testing refusal boundaries directly, and defining (then verifying) a safety envelope for the system's actions.
Related real work:
verification layer for AI in regulated industries: deterministic compliance checks, tamper-evident receipts, proof of inference
z3-verified runtime safety envelope
Both are possible, but the core of this work is auditing and verifying an existing or in-progress pipeline, not building product features.
Red-teaming finds specific failure prompts. This also looks at the underlying architecture — provenance, refusal design, safety envelope — so fixes address the class of problem, not just the instance found.
Access to your technical documentation and a diagnostic call about what needs verifying and why it matters to your specific system.