AI Audit & Assessment for Australian Organisations
ASIC reviewed 23 licensees and found governance gaps at nearly all of them. APRA CPS 230 is now in force. The Privacy Act automated decision-making deadline is nine months away. An independent artificial intelligence audit tells you exactly where your organisation stands before regulators arrive to find out themselves.
Our specialists conduct comprehensive AI audits aligned to APRA CPS 230, ASIC REP 798 expectations, ISO 42001, NIST AI RMF, and the Privacy Act, covering everything from shadow AI discovery and algorithmic bias testing to generative AI governance and data governance practices. Board-ready findings. Regulator-ready evidence.
Why Australian Businesses Must Assess Now
Australia has the most stringent AI governance requirements in the Asia-Pacific region for financial services. Regulators are not waiting, and neither should your organisation.
ASIC Found Systemic Governance Gaps
ASIC REP 798 reviewed 23 AFS and credit licensees across 624 AI use cases and found governance gaps at nearly all of them. Nearly half lacked policies addressing consumer fairness or algorithmic bias. Most had immature generative AI governance. Third-party AI risks were inadequately assessed at organisations relying on external vendors for over 50% of their machine learning models.
Mandatory Requirements Are Active
APRA CPS 230 took effect 1 July 2025, requiring board-approved operational risk management frameworks that explicitly cover AI technologies. Privacy Act automated decision-making transparency requirements commence December 2026. The ANAO has already audited the ATO's governance of 43 AI models in production. Assessment now gives organisations time to identify and close gaps before enforcement escalates.
Personal Liability Under FAR
The Financial Accountability Regime holds accountable persons personally liable for AI governance failures, with individual penalties up to $1.565 million. Directors and senior executives need independent assurance that artificial intelligence governance is adequate. Board self-assessment is not sufficient when regulators can demonstrate that governance gaps existed and responsible individuals failed to take reasonable steps.
What an AI Audit Actually Covers
An artificial intelligence audit goes far beyond traditional IT audit. Where IT audits examine infrastructure, access controls, and system availability, an AI audit evaluates the unique risks that machine learning algorithms, generative AI, and automated decision-making systems introduce: bias in outputs, opacity in how decisions are reached, model drift over time, data governance failures in training pipelines, and the adequacy of human oversight mechanisms.
For Australian businesses, this distinction matters. CPS 230 requires organisations to manage operational risks created by AI systems, not merely the servers they run on. ASIC expects licensees to demonstrate that AI systems treat consumers fairly. The Privacy Act demands transparency about substantially automated decisions. A traditional IT audit does not test any of these. An AI-specific audit does.
Shadow AI discovery: Cataloguing every AI tool across the organisation, including generative AI adopted without formal approval, embedded AI in vendor platforms, and machine learning models operating outside central oversight
Algorithmic bias and fairness testing: Statistical analysis of model outputs across protected characteristics to detect discrimination patterns, with specific attention to credit scoring, insurance underwriting, and customer segmentation algorithms
Data governance assessment: Reviewing training data quality, representativeness, lineage, and compliance with Privacy Act requirements for personal information used in AI systems
Generative AI governance review: Assessing controls around large language models and generative AI tools, including acceptable use policies, output monitoring, hallucination risk management, and intellectual property protections
Assessment Options
Choose the assessment scope that matches your organisation's needs, from targeted regulatory gap analysis to comprehensive governance reviews that satisfy both internal audit requirements and regulator expectations.
Regulatory Gap Analysis
Focused compliance assessment
Targeted assessment against specific regulatory requirements. We map your current AI practices against APRA CPS 230 operational risk obligations, ASIC REP 798 governance expectations, or Privacy Act automated decision-making provisions, identifying exactly where gaps exist and their severity.
- Regulatory requirement mapping against ASIC's 11 questions for licensees
- Gap identification with severity rating and business value at risk
- Remediation strategy with regulatory deadline alignment
Typical duration: 3-4 weeks
Governance Maturity Assessment
Comprehensive governance review
End-to-end assessment of AI governance maturity across your entire organisation. We evaluate framework design, policies, processes, controls, and reporting against ISO 42001 and NIST AI RMF benchmarks, providing a clear maturity score and a practical roadmap for continuous improvement.
- Complete AI inventory including shadow AI and third-party solutions
- Governance framework evaluation against six regulatory standards
- Maturity scoring and benchmarking against Australian industry peers
- Prioritised improvement roadmap aligned to digital transformation objectives
Typical duration: 6-8 weeks
AI System Audit
Technical and operational review
Deep-dive assessment of specific systems: machine learning model validation, algorithmic bias testing, performance monitoring effectiveness, data governance practices in training pipelines, documentation completeness, and control operating effectiveness. Essential for high-risk AI making decisions that affect consumers in Australia.
- Model documentation and lineage review
- Algorithmic bias and fairness assessment across protected characteristics
- Control design and operating effectiveness testing
Typical duration: 4-6 weeks per system
Common Findings in Australian AI Audits
Based on ASIC REP 798 findings and our assessment experience, these are the governance gaps that appear most frequently across Australian organisations. Recognising them early enables proactive remediation before regulatory intervention.
Incomplete AI Inventories
78% of organisations use AI, but most cannot produce a comprehensive inventory of every system in operation. Shadow AI, meaning tools adopted by teams without formal approval, creates unknown risk exposure that businesses cannot manage, measure, or report on.
Missing Fairness and Bias Policies
ASIC found that nearly half of licensees lack policies addressing consumer fairness or algorithmic bias in their AI systems. Without these policies, organisations have no consistent method to detect or prevent discriminatory outcomes in credit scoring, insurance pricing, or customer segmentation algorithms.
Governance Not Keeping Pace with Innovation
Only 11% of organisations have fully implemented responsible AI practices, even as adoption accelerates across every sector. The result is a growing gap between the pace of deployment and the maturity of governance frameworks meant to manage the risks these systems introduce.
Inadequate Third-Party AI Oversight
ASIC found that 30% of all AI use cases involve third-party developed models, yet many businesses lack robust vendor management procedures for AI solutions. Most licensees rely on external providers for at least half their models without adequate due diligence, performance monitoring, or contractual protections.
Generative AI Without Guardrails
63% of Australian and New Zealand organisations observed unauthorised generative AI use by employees, yet only 36% expressly permit AI use with appropriate controls. The rapid adoption of large language models has outpaced policy development, creating risks around data leakage, hallucinated outputs, and intellectual property exposure.
Weak Board-Level Reporting
Boards and audit committees frequently receive insufficient information about AI risk exposure. ASIC identified that many organisations assess AI risk through a business lens rather than a consumer lens, and board reporting rarely includes metrics on model performance, bias indicators, or incident trends that directors need for effective oversight.
Assessment Framework and Methodology
Our assessment methodology is purpose-built for the Australian regulatory environment, aligning to both domestic requirements and international standards. Every finding is documented with the evidentiary rigour that APRA, ASIC, and internal audit committees expect, providing assurance that satisfies regulators, boards, and external auditors.
Aligned to Standards
Scoping, Planning, and AI Discovery
We define assessment scope with your team, identify key stakeholders, and conduct a comprehensive discovery of all AI systems across the organisation, including shadow AI adopted without formal approval. We map each system to its data sources, business processes, and consumer impact to establish a risk-based audit plan.
Documentation and Data Governance Review
We review governance frameworks, policies, procedures, AI inventories, risk registers, data governance practices, and board reporting to assess design effectiveness. This includes evaluating whether documentation meets the standards that APRA, ASIC, and internal audit functions require.
Stakeholder Interviews
We conduct structured interviews with executives, risk teams, compliance officers, IT specialists, data science teams, legal, and business units. We assess whether actual practices align with documented policies, and whether the organisation has the capabilities and resources to govern AI effectively as adoption scales.
Control Testing and Algorithmic Assessment
We test the operating effectiveness of key controls: approval workflows, monitoring processes, incident response mechanisms, algorithmic bias detection methods, model validation procedures, and reporting mechanisms. For machine learning systems, we assess model performance, drift monitoring, and fairness metrics.
Findings, Reporting, and Remediation Strategy
We document findings with severity ratings, root cause analysis, and specific remediation recommendations. Reports are designed for board, audit committee, and regulator consumption. Every finding maps back to a specific regulatory requirement or industry standard, providing a clear strategy for prioritised remediation.
The Australian Regulatory Landscape for AI
Australia uses a multi-layered, sector-specific approach to AI regulation. Understanding how these overlapping requirements interact is essential for any organisation pursuing compliance.
APRA CPS 230: Operational Risk Management
Effective 1 July 2025, CPS 230 requires APRA-regulated entities to manage operational risks from AI technologies within board-approved frameworks. AI systems supporting critical operations (payments processing, claims handling, investment management) must have defined tolerance levels, business continuity plans, and recovery objectives. Third-party AI vendors are subject to Material Service Provider requirements including mandatory registers, right-to-audit provisions, and ongoing performance monitoring.
ASIC REP 798: Governance Gaps Exposed
ASIC's review of 23 licensees across 624 AI use cases classified organisations into three maturity levels: latent, decentralised, and strategic. ASIC published 11 questions every licensee should be able to answer, covering centralised visibility of AI use, third-party governance, consumer fairness, and board oversight. Businesses that cannot answer these questions face heightened regulatory scrutiny and potential enforcement action.
Privacy Act: Automated Decision-Making
From 10 December 2026, organisations must provide transparency about substantially automated decisions that significantly affect individuals. This applies to any AI system making or materially influencing decisions about credit applications, insurance claims, employment, or service eligibility. Organisations need to identify which AI systems fall within scope, document decision logic, and establish mechanisms for individuals to request human review.
Multi-Regulator Complexity
Australian businesses navigate overlapping jurisdictions from APRA (prudential regulation), ASIC (market conduct), OAIC (privacy), and ACCC (consumer law). Each regulator has distinct expectations for AI governance. The Financial Accountability Regime adds personal liability for accountable persons, while the AI Safety Institute provides advisory support on emerging risks. This complexity demands assessment strategies that address all applicable obligations simultaneously.
What You Receive
Actionable findings and recommendations designed to drive meaningful improvement and provide documented evidence of proactive governance for regulators, boards, and internal audit teams.
Assessment Report
Comprehensive findings report with executive summary, detailed observations, severity ratings, root cause analysis, and evidence documentation. Structured in a board-ready format suitable for audit committee presentation and regulatory correspondence with APRA or ASIC.
Gap Analysis Matrix
Detailed mapping of current state against regulatory requirements across APRA CPS 230, ASIC REP 798, ISO 42001, NIST AI RMF, Privacy Act, and AI Ethics Principles. Each gap includes severity classification, compliance status, and recommended remediation actions.
Maturity Scorecard
Governance maturity ratings across key domains: framework design, policies and standards, risk management, data governance, controls effectiveness, monitoring, and reporting. Benchmarked against Australian industry peers and international standards to contextualise your organisation's position.
Remediation Roadmap
Prioritised action plan with specific recommendations, ownership assignments, effort estimates, and suggested timelines aligned to regulatory deadlines. Designed to support both immediate compliance requirements and longer-term governance transformation strategies.
Board Presentation
Executive presentation summarising key findings, risk exposure, FAR implications for accountable persons, and recommended actions. Structured for board and audit committee consumption with clear visualisations of maturity levels, gap severity, and remediation priorities.
Management Debriefs
Working sessions with management and internal audit teams to discuss findings, validate observations, answer questions, and agree on remediation approach. Includes knowledge transfer to build internal capability for ongoing AI governance monitoring and assessment.
Who This Assessment Is For
AI audit and assessment is for any organisation that needs independent assurance about the effectiveness of its governance, risk management, and regulatory compliance posture in Australia.
Board Directors and Audit Committees
Seeking independent assurance on AI governance effectiveness and FAR compliance. Directors face personal liability under the Financial Accountability Regime and need evidence that reasonable steps have been taken to manage AI risks across the organisation.
APRA-Regulated Entities
Banks, insurers, and superannuation funds that must comply with CPS 230 operational risk requirements for AI systems. Assessment covers critical operations identification, third-party AI vendor management, and board accountability obligations now in effect.
Internal Audit Teams and Chief Audit Executives
Supplementing internal audit capabilities with specialist AI governance expertise. Only 39% of internal auditors currently leverage AI audit tools, and most teams lack the specialist knowledge to assess machine learning model risk, algorithmic fairness, or generative AI controls. Our co-sourcing model builds lasting internal capability through knowledge transfer.
Organisations Scaling AI for Growth
Businesses pursuing AI-driven transformation who want to establish a governance baseline before scaling. A pre-implementation assessment ensures that growth objectives are supported by governance structures that prevent costly remediation later.
Frequently Asked Questions
How does an AI audit differ from a traditional IT audit or internal audit?
Traditional IT audits evaluate infrastructure controls, access management, and system availability. An AI audit examines risks unique to these systems: algorithmic bias in decision-making, opacity in how machine learning models reach conclusions, data governance failures in training datasets, model drift degrading performance over time, and the adequacy of human oversight for automated decisions. Internal audit teams lack the specialist expertise to assess these risks, which is why organisations engage external specialists to complement their existing capabilities. The ISACA AAIA certification for AI auditors reflects the growing recognition that this work requires distinct skills beyond traditional IT or financial audit competencies.
Can the findings be shared with regulators?
Yes. Our reports are designed to be regulator-ready from the outset. Many Australian businesses use our assessments to demonstrate proactive governance efforts to APRA or ASIC during supervisory engagements. The report structure follows the evidentiary standards regulators expect: specific findings linked to regulatory requirements, severity classifications based on risk impact, root cause analysis, and documented remediation strategies with timelines. This approach provides organisations with a defensible position that demonstrates reasonable steps were taken to manage AI risk.
What access do your consultants need?
We need access to governance documentation (policies, frameworks, risk registers), key stakeholders for structured interviews across risk, compliance, technology, data science, and business units, and relevant systems for control testing. For algorithmic bias testing, we require access to model outputs and, where possible, training data characteristics. Our team works with your organisation to minimise operational disruption while ensuring comprehensive coverage that delivers meaningful findings.
How does the assessment address generative AI and large language models?
Generative AI introduces governance challenges that traditional AI audit frameworks were not designed for: hallucination risk, prompt injection vulnerabilities, intellectual property exposure, and the difficulty of controlling outputs from systems that generate novel content. Our assessment evaluates your organisation's generative AI governance posture including acceptable use policies, output monitoring controls, data leakage prevention, vendor management for generative AI solutions, and the adequacy of training and guidance provided to employees using these tools.
Do you also help with remediation?
We provide remediation support as a separate engagement to maintain audit independence. Many organisations use our AI Governance Consulting, Risk Framework Development, or Policy Development services to address assessment findings. This separation ensures the integrity of the audit while providing a clear pathway from findings to solutions, whether that involves building governance frameworks, developing AI policies, implementing bias testing procedures, or preparing for ISO 42001 certification.
How does your assessment align to ISO 42001 and NIST AI RMF?
Our methodology maps to both ISO 42001 (the international standard for AI management systems, with over 100 organisations globally now certified since its December 2023 publication) and the NIST AI Risk Management Framework. We assess governance maturity against the control objectives and risk management practices these standards prescribe, while layering on Australian-specific regulatory requirements from APRA, ASIC, and the Privacy Act. This dual approach ensures findings are relevant both for domestic compliance and for organisations pursuing international certification or operating across multiple jurisdictions.
Related Services
AI Governance Consulting
Build the governance framework and operating model to address assessment findings. Transform audit insights into working governance that supports responsible AI adoption.
Learn more →Risk Framework Development
Develop AI risk taxonomies and assessment methodologies to close the control gaps identified during audit. Aligned to CPS 230 operational risk requirements and NIST AI RMF categories.
Learn more →AI Policy Development
Create the policies, standards, and procedures identified as gaps during assessment, including acceptable AI use policies, algorithmic fairness standards, and data governance frameworks for AI systems.
Learn more →Know Where Your Organisation Stands
Request an independent AI governance assessment to understand your current maturity, identify compliance gaps across APRA CPS 230, ASIC REP 798, ISO 42001, and Privacy Act requirements, and get a clear remediation strategy aligned to regulatory deadlines. Assessment by specialists who understand Australia's multi-regulator environment.