How this lab works
Govern, in plain terms
This is what makes the system safe enough to put in front of customers and regulators. You tier each use case by risk, enforce policy at runtime, catch problems with guardrails and red-teaming, and produce the evidence an auditor will actually accept.
Register and risk-tier
Each use case is scored from its data sensitivity, deployment context, autonomy, business function, and human oversight.
Not all AI is equally risky — controls should match the tier, not be one-size-fits-all.
Policy as code
Turn on rules — PII redaction, protected-class checks, and more — that are enforced automatically on every request.
A policy that isn't enforced at runtime is just a document nobody reads.
Runtime guardrails
Every request is allowed, redacted, rewritten, blocked, or escalated based on the active policies.
This is where harm is caught and contained before it ever reaches a user.
Red-team and evals
Probe the system with adversarial prompts and evaluation suites to find where it fails.
Better to find the failure yourself than to let an attacker — or a headline — find it first.
Human review and audit
Risky requests queue to a person, and every decision is logged in a tamper-evident trail.
Accountability needs a paper trail — and someone who owns the call.
Evidence and readiness
Generate a report mapped to frameworks like NIST AI RMF, the EU AI Act, and ISO 42001.
'Trust us' doesn't pass an audit. Evidence, mapped to a standard, does.