AI Safety & Governance
"AI safety" gets talked about as a research abstraction. In practice it's an engineering discipline: how do you know the system did the right thing, and can you prove it later? The teams who take this seriously build evaluation, guardrails, and audit trails in from the start — not as a panic layer after a bad output ships.
What we keep seeing fail is governance theater: a policy document with nothing enforcing it in the runtime. Real safety is testable — adversarial probes, boundary checks, and logs you can actually review. It's less glamorous than the headlines and far more useful.
These are the AI-safety and governance concepts our council surfaced from real demand. They treat trustworthiness as something you measure and enforce, not something you assert.
