AI safety & evaluation
AI safety evaluation and guardrails for high-stakes products
Working and safe are not the same bar. Working means the system produces valid output. Safe means the output cannot harm a vulnerable user. Most teams ship the first and assume the second. In regulated and high-stakes domains that assumption is the risk, and adversarial evaluation is how you find it before your users do.
Who this is for
- ▹You are shipping AI into healthcare, finance, legal or another regulated domain
- ▹You need to prove safety to a board, a client or a regulator
- ▹Your safety testing is happy-path only
- ▹You handle PII or PHI and need it controlled before it reaches a model or a log
What the work covers
Adversarial evaluation
Scripted multi-turn audits across scenarios and languages, scored on safety, boundary adherence and quality, with permanent regression harnesses.
Guardrails and detection
Tiered detection cascades that resolve the obvious cases in milliseconds and escalate only the ambiguous residue, so latency and cost stay flat.
PII and PHI handling
Detection and redaction before data reaches a model or a log, with on-device options where privacy matters most.
Prompt-injection defense
Layered input screening against the OWASP LLM risks, including indirect injection through retrieved documents.
Evidence this works
- ✓Real-time crisis detection resolving over 99% of traffic in under 100ms at roughly 99.7% accuracy, multilingual
- ✓A 204-turn adversarial audit that surfaced 7 failures on a system already considered working
- ✓Migration from 40+ brittle keyword rules to an LLM classifier covering 22+ intents in any language
How the engagement runs
- 1Discovery sprint: I define what failure actually costs a user in your domain, then design the audit around those failure modes.
- 2Audit and build: adversarial evaluation, guardrails and the regression harnesses that keep them enforced.
- 3Handover: a repeatable safety suite your team runs on every change.
Common questions
How do you test AI safety beyond the happy path?
With adversarial audits: scripted multi-turn scenarios covering indirect language, edge cases and the culturally specific ways people express distress, scored against explicit safety criteria. Functionality tests will not find these failures.
Does a safety layer slow the product down?
It should not. A tiered cascade resolves the overwhelming majority of traffic with pattern and lightweight checks in well under 100ms, and only sends genuinely ambiguous cases to a slower model.
Can safety work in languages other than English?
It must. An English-first classifier plus English keyword lists will silently miss non-English risk, which is a failure mode I have found and fixed in production. Intent-based classification handles language and paraphrase.
Related work and demos
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