Governance, Safety & Risk Management

Implement guardrails and safety controls

Layer prompt guidance with deterministic input/output filters, policy checks, and hard blocks on irreversible tool paths — prompts alone never satisfy “must always” safety language.

Identify risks, limitations, and failure modes of LLM systems

Enumerate hallucination, drift, tool misuse, outages, and policy gaps with user impact, mitigations, and residual risk owners — never “the model will handle it.”

Apply human-in-the-loop validation strategies

Route high-risk or ambiguous work to humans with measurable escalation triggers and a structured handoff — facts, suggestion, uncertainty, and required decision.

Ensure compliance with regulations (e.g., GDPR, HIPAA, FedRAMP)

Map data classes, regions, and flows to the named regulatory regime; minimize sensitive data in prompts and logs; document control ownership and evidence.

Address ethical AI considerations (bias, fairness, transparency)

Treat bias, fairness, and transparency as design requirements: measure disparate outcomes, disclose AI involvement when required, and provide recourse — not slogans.