AI Governance

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 Principles

01

Governance Is a System Property

AI risk does not exist in isolation. It emerges from interactions between:

  • Models
  • Agents
  • Data
  • Tools
  • Humans

Governance must therefore observe end-to-end decision flows, not individual components.

02

Human Accountability Is Explicit

AI systems must preserve:

  • Clear ownership
  • Escalation paths
  • Human override mechanisms

We design for human-in-the-loop and human-on-the-hook accountability.

03

Observability Before Explainability

Post-hoc explanations are insufficient without:

  • Runtime Visibility
  • Behaviour Tracing
  • Historical Reconstruction

Auditability requires continuous observability.

04

Governance Must Be Embedded

Governance that exists outside the system fails in production.

Controls must be:

  • Native
  • Enforceable
  • Measurable
05

Governance Must Enable, Not Freeze

Excessive restriction creates shadow AI.

Effective governance should:

  • Manages risk
  • Supports innovation
  • Adapts with system evolution

Disclaimer: This page reflects /tmp Labs’ practitioner experience and design philosophy. It does not represent regulatory guidance or endorsement.