When it comes to Artificial Intelligence (AI), almost every leader will ask the same questions:
How can I trust our AI systems? How do I provide confidence to my agency, ministers and, importantly, the Australian public that our use of AI is responsible, ethical and trustworthy?
The consequences of not being able to answers these questions can be significant. From lost productivity and internal change resistance to serious regulatory issues and lost citizen trust.
The Australian Government has set up principles, policies and guardrails for AI. These include the National AI Centre (NAIC) Guidance for AI Adoption and the Digital Transformation Agency (DTA) AI Plan and Policy for the Responsible Use of AI in Government 2.0. These help ensure AI systems are designed and used ethically and responsibly. They also support growth in Australia's AI industry, promote innovation and embed responsible practices.
It is now up to individual agencies to ensure their development, management and use of AI aligns with these principles, policies and guardrails. AI is already being used in many agencies, whether this be via official AI systems, shadow AI or through vendors enabling AI services on previously approved services and products, and many agencies are struggling to implement the policies and governance frameworks to ensure these systems are safe, reliable and trustworthy. They also need proof that these systems are working as they should.
Assurance demands evidence, not just rules on paper.
What is ISO/IEC 42001: Artificial Intelligence Management Systems (AIMS)?
An AIMS is a structured, enterprise-wide, framework designed to govern the development, deployment, and operation of AI.
The ISO/IEC 42001 is an internationally recognised standard for establishing, implementing, maintaining and continually improving an AIMS.
The standard provides assurance by requiring organisations to systematically manage AI risks, controls, governance, monitoring and continuous improvement. This created confidence in AI performance and safety
AIMS is more than a compliance checklist. It strengthens trust, transparency, accountability, decision-making, security, safety, fairness and data quality.
When departments and agencies implement an AIMS, they demonstrate that their AI systems are managed in a structured and systematic manner, thereby building trust and confidence.
Assurance provides trust and confidence, backed by evidence.
Understanding AI-related risks
When AI is deployed without understanding risks, adverse outcomes are inevitable. AIMS enables organisations to prevent such outcomes and mitigate the consequential loss of trust, harm, compliance failures, service disruptions and reputational damaged.
AIMS requires organisations to identify and assess AI-related risks and develop treatment plans aligned with policy directions and objectives, thereby producing consistent and comparable results. This approach transforms uncertainty into documented risks analysis, supported by clear, defensible decisions.
Assurance is where risk is not just assessed once, but actively managed.
Controls are defensible under scrutiny
When asked "why this control and not another?", the answer is evidence-based, auditable and defensible, not assumed.
AIMS establishes a structured risk treatment process that enables organisations to select appropriate responses to identified risks. This process guides the selection of controls for responsible AI management.
These selections are central to ISO/IEC 42001, along with any extra controls your situation requires. The outcome is a Statement of Applicability, which explains why each control is included or excluded. This statement is key to showing your approach to responsible AI.
A control that is not implemented does not provide assurance – it remains as a documented intention.
Verification, validation and independent review
In the absence of verification, validation and independent review or internal audit, leaders are deprived of the objective evidence necessary to demonstrate that AI systems are operating as intended.
Organisations can address this gap by embedding verification, validation and internal audit within their everyday AI operations. Regular testing, continuous monitoring, documented evidence and independent review give leaders confidence and trust that their AI systems are safe and operating as intended.
Verification confirms the build, validation proves it works as intended, and independent review provides the objective confirmation necessary to establish trust.
Evidence and traceability
Without evidence, organisations have no reliable means for demonstrating what was done, why it was done or whether it worked. Decisions are reduced to conjecture, and leaders cannot demonstrate that risks were appropriately managed or that controls operated as intended. In such circumstances, trust is unattainable.
Documentation and evidence traceability are essential to demonstrating trust and assurance. They establish that decisions, data, models, risks, controls and outcomes have been appropriately recorded. This enables end-to-end traceability across the AI lifecycle. ISO/IEC 42001 mandates robust documentation and traceability requirements. Without them, an organisation cannot prove compliance. It cannot respond effectively to audits or incidents.
No evidence and no documentation means no traceability, no actability and no assurance.
Avoid three fatal mistakes
- Treating AI governance as IT's problem: Its enterprise governance requiring cross-functional ownership.
- Building policy without proof systems: Controls only matter if you can demonstrate they're operating effectively.
- Waiting for regulatory clarity: Australia has chosen not to introduce an EU-style AI Act. The National AI Plan confirms the government will continue to build on Australia's existing legal and regulatory frameworks. It will consider where uplifts are required with focus on high-risk use cases areas. Recent examples include automated decision-making and public reporting under the Freedom of Information Act.
In conclusion, the ISO/IEC 42001 is your foundational structure for day-to-day AI governance, translating high-level principles into daily operational practices, covering risk, monitoring and AI lifecycle management. It is certifiable, internationally recognised, and designed for any organisation using AI. Structured risk and impact assessment produces defensible decisions: documented, consistent, comparable results that satisfy audit and executive scrutiny. Internal audit and management review prove effectiveness: test actual operation, not just policy intent, with evidence retained for accountability.
Let's take your next AI step together.
Contact us to discuss your AI assurance approach with our accredited ISO/IEC 42001 AI Advisory specialist.
Ashish Das, Jason McQuillen
