Services
AI systems fail in ways traditional security never had to model: a prompt that hijacks an agent, a model that leaks data it shouldn't, an output that's confidently wrong, a bias that quietly disadvantages a group of customers. Trust & Safety is its own discipline, and we build it in from day one — then run it continuously, because every model upgrade, new tool and new integration changes the failure surface.
What we design, build and operate
Prompt-injection & jailbreak defence. Input and output guardrails, tool-call allow-listing, and adversarial tests mapped to the OWASP LLM Top 10 — run in CI, not discovered in the wild.
Content safety & data protection. Output moderation, PII detection and redaction, retrieval scoped so an agent only ever sees what its caller is entitled to, and "do-not-say" registers enforced at the boundary.
Red-teaming & evaluation. Structured red-team exercises and regression eval suites for safety, accuracy and refusal behaviour, re-scored on every model upgrade.
Bias, fairness & ethical AI. Bias and fairness testing, explainability and model cards, and a responsible-AI governance model aligned to ISO/IEC 42001 and the NIST AI RMF — so decisions that affect people can be explained to the people they affect.
Human-in-the-loop & abuse prevention. Escalation thresholds, approval checkpoints on high-stakes actions, rate limiting and abuse controls, and an audit trail for every AI-assisted decision your compliance team can defend.
Standards we align to
ISO/IEC 42001 (AI management systems), ISO/IEC 23894 (AI risk management), the NIST AI Risk Management Framework, the OWASP Top 10 for LLM Applications, and the EU AI Act risk tiers. For the security and compliance frameworks underneath — ISO 27001, SOC 2, PCI DSS, NIST — see our Security & Compliance service; the two practices are run to the same discipline.
How it works
2–4 weeks to a baseline, then continuous. Per engagement:
Map your AI surface — models, agents, tools, retrieval paths — and threat-model it against the OWASP LLM Top 10.
Add prompt- and model-security tests, guardrails and evals to your CI, so regressions are caught before deploy.
Run a structured red-team baseline, and wire its findings into the regression suite.
Stand up escalation thresholds, approval checkpoints and the audit trail for AI-assisted decisions.
Re-score the whole suite on every model upgrade — including when you swap vendors.
Output
Prompt, model and Trust & Safety tests committed to your CI, with an evaluation and red-team report.
A responsible-AI / Trust & Safety policy pack — guardrails, escalation, bias and explainability standards.
A regression eval suite, re-runnable on demand and re-scored on every model change.
An audit trail and escalation playbook for every AI-assisted decision your compliance team can defend.
Cost: Engagement-based — scoped to your AI surface area. Continuous-operation retainers available.
