Our Approach to Enterprise AI Governance
/tmp Labs builds AI platforms for environments where decisions, risk, and accountability matter.
Our governance approach is informed by real-world deployment experience in regulated industries and participation in public consultations on AI governance. We focus on operational governance — not theoretical compliance.
Governance Is a System Property
AI risk does not exist in isolation. It emerges from interactions between models, agents, data, tools, and humans.
Governance must therefore observe end-to-end decision flows, not individual components.
Human Accountability Is Explicit
AI systems must preserve clear ownership, escalation paths, and human override mechanisms.
We design for human-in-the-loop and human-on-the-hook accountability.
Observability Before Explainability
Post-hoc explanations are insufficient without runtime visibility, behaviour tracing, and historical reconstruction.
Auditability requires continuous observability.
Governance Must Be Embedded
Governance that exists outside the system fails in production. Controls must be native, enforceable, and measurable.
Governance Must Enable, Not Freeze
Excessive restriction creates shadow AI. Effective governance manages risk, supports innovation, and adapts with system evolution.
Disclaimer: This page reflects /tmp Labs' practitioner experience and design philosophy. It does not represent regulatory guidance or endorsement.