Trust

Governance and Risk

AI governance that holds up under scrutiny needs more than a policy document. Trust builds the operating model: the ownership, approval gates, and risk-tiering that let your organisation deploy AI with confidence and defend that deployment when asked.

Who it's for

Enterprise organisations building or scaling AI governance: Chief Data and Analytics Officers, Chief Risk Officers, General Counsel, and Heads of Data and Analytics.

Methodology

Discover

Map where AI is in use across the organisation, including shadow deployments and vendor-embedded AI that hasn't gone through formal review.

Assess

Evaluate current governance maturity against the Australian regulatory environment: the National AI Centre's Guidance for AI Adoption (AI6), the Voluntary AI Safety Standard, and ISO/IEC 42001.

Design

Build the governance operating model: risk-tiering, approval gates, ownership structures, and decision rights, mapped to your organisation's actual operating rhythm rather than a generic template.

Embed

Put the model into practice. Training, workflow integration, and the operational routines that make governance something teams do, not a document that sits on a shelf.

Assure

Ongoing assurance that the governance model is operating as designed, with evidence you can point to when a regulator, board, or customer asks.

Regulatory grounding

Australia currently governs AI through existing law, not a dedicated AI Act. Trust is built around the obligations that already apply: the Privacy Act, Australian Consumer Law, sector regulator expectations, and the government's AI6 guidance, supported by the Australian AI Safety Institute's advisory role. ISO/IEC 42001 provides the certifiable benchmark increasingly expected by boards and enterprise customers.

Engagement

Standard engagement runs 12 weeks. Retainer or fixed-scope options available depending on where your organisation sits in the five phases.

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