Defend inputs, outputs, identities, data, models, tools, and decisions while operationalizing privacy, compliance, responsible AI, and human oversight.
Layer validation, prompt-injection defenses, content controls, output schemas, grounding, policy checks, and safe failure behavior.
Apply identity, encryption, private connectivity, tenancy boundaries, minimization, masking, retention, and traceable access.
Define ownership, inventory, approval, evidence, monitoring, change control, exception handling, and regulatory mapping.
Evaluate fairness, harmful behavior, transparency, explainability, human oversight, misuse, and affected-user consequences.
Models are probabilistic components, not policy enforcement points. Apply identity, authorization, tenancy, data minimization, encryption, private connectivity, tool permissions, budgets, approval, and audit in deterministic systems. Use model-facing guardrails as an additional layer.
Separate direct prompt injection, indirect injection through retrieved or tool content, data exfiltration, harmful output, insecure output handling, excessive agency, denial of service, model misuse, and privacy risk. Bound untrusted content, minimize context, validate outputs, authorize every action, and fail safely.
Maintain an inventory of use cases, owners, models, datasets, prompts, tools, risks, approvals, evaluations, incidents, exceptions, and changes. Map compliance obligations to visible controls and evidence. Define when human review is required and ensure it is meaningful rather than ceremonial.
Evaluate performance across relevant groups and use cases, harmful behavior, transparency, explainability needs, accessibility, affected-user recourse, misuse, and feedback. Average quality cannot compensate for a critical safety or authorization failure.
Threat-model an agent that reads internal documents and can open a support ticket. Identify what the model may suggest, what it may execute, what requires approval, and what evidence proves the boundaries work.
