AI impact assessment for Australian businesses.
ASIC reviewed 23 AFS and credit licensees and uncovered 624 AI use cases alongside systemic governance gaps. Independent impact assessments identify the risks, validate the controls, and provide board-ready reporting to close those gaps.
Built for
What you walk away with.
Board-ready assessment report
Executive summary, findings with risk ratings and root cause analysis, mapped to ASIC, APRA CPS 230, and Privacy Act expectations.
AI model inventory
Complete catalogue including shadow AI, generative AI, machine learning models, third-party vendor mapping aligned to CPS 230.
Gap analysis & roadmap
Current state against every Australian framework, with a prioritised action plan aligned to CPS 230 and Privacy Act ADM deadlines.
Knowledge transfer
Workshops with internal audit, risk, and data governance teams. Capability building so your team can sustain governance after we leave.
AI adoption is outpacing governance.
Regulatory reporting has revealed governance gaps across Australian organisations deploying AI. The same patterns appear repeatedly when we assess businesses across industries.
- 01624 use cases
Adoption is accelerating faster than oversight.
ASIC REP 798 identified 624 AI use cases across just 23 licensees, revealing the scale of deployment in Australian financial services alone. Across every industry, machine learning models, generative AI tools, and automated decision-making systems are being deployed at a pace governance frameworks have not matched. Impact assessment establishes a baseline for responsible oversight.
- 0248% gap
Governance gaps are systemic.
ASIC found that 48% of licensees lack policies addressing fairness or bias in their AI systems. Governance frameworks are lagging behind AI adoption across the board. Without independent assessment, businesses cannot identify the specific gaps that expose them to regulatory action, consumer harm, or reputational damage.
- 03FAR liability
Business value is at stake.
AI delivers real value when governed well. Ungoverned systems create liability. FAR personal accountability penalties reach $1.565 million for individuals. Businesses that demonstrate responsible AI practices build trust with customers, investors, and partners. Impact assessment protects that value by ensuring AI systems are both effective and responsible.
- 04Dec 2026
Regulatory expectations are escalating.
APRA CPS 230, effective 1 July 2025, requires operational risk management frameworks that include AI systems. The Financial Accountability Regime establishes personal accountability obligations for directors and senior executives, with penalties up to $1.565 million. Privacy Act automated decision-making transparency requirements commence December 2026. Internal audit teams require specialist expertise to assess AI-specific risks.
What an impact assessment covers.
Every dimension of AI governance, from strategic oversight and data governance through machine learning model validation and generative AI controls. Each area maps to Australian regulatory expectations and to ISO 42001 plus the NIST AI RMF.
01
Governance framework
Evaluation against regulatory expectations and APRA CPS 230 requirements. Strategic oversight, policy coverage, board reporting, accountability frameworks, and alignment to Australia's 8 AI Ethics Principles.
02
Model inventory & data governance
Discover and document all AI systems operating in your organisation including shadow AI. Each model is catalogued with ownership, purpose, data sources, risk tier, and deployment status. Training data quality, lineage, and Privacy Act compliance for personal information used in AI are also assessed.
03
Bias and fairness testing
For high-risk AI systems, algorithmic bias testing using industry-standard fairness metrics. Whether machine learning models discriminate across protected characteristics or treat consumers fairly, a specific ASIC REP 798 concern. We test both traditional AI and generative AI outputs.
04
Third-party AI vendor assessment
Evaluation of AI vendor governance aligned to CPS 230 material service provider requirements. Machine learning models provided by vendors, generative AI platforms used by your team, and the data governance practices of third-party AI providers.
05
Risk assessment and controls
Risks identified across the AI portfolio using a framework aligned to APRA and ASIC expectations, incorporating NIST AI RMF and ISO 42001. Operational and consumer impact perspectives both apply. We test whether governance controls are designed well and operating effectively against model drift, hallucination, and automated decision-making opacity.
06
Regulatory compliance mapping
Current state mapped against ASIC REP 798, APRA CPS 230, Privacy Act requirements, Australia's Voluntary AI Safety Standard guardrails, and relevant Government AI frameworks. A compliance gap analysis with remediation priorities and actionable strategies.
How the assessment runs.
Four stages grounded in IIA standards, the NIST AI RMF, and ISO 42001 Annex B impact assessment guidance. Six to twelve weeks end-to-end.
- 01
Discovery and planning.
1 to 2 weeks
Interviews with key stakeholders (CRO, CAE, CDO, model owners) to understand the AI landscape. Use cases mapped across generative AI, machine learning, and automated decision-making. Third-party AI identified and scoped against your regulatory context and risk profile.
- 02
Assessment and testing.
2 to 6 weeks
Evaluation of governance frameworks, model documentation, data governance practices, and controls. Where required, algorithmic bias testing, fairness analysis, and control effectiveness reviews. For generative AI we assess acceptable use policies, output monitoring, and intellectual property protections. Evidence collection follows professional audit standards.
- 03
Analysis and reporting.
1 to 2 weeks
Findings consolidated into a board-ready report with executive summary, detailed observations, root cause analysis, and recommendations mapped to ASIC and APRA expectations. Compliance gap analysis against ISO 42001, NIST AI RMF, and Australia's AI Ethics Principles with prioritised remediation strategies.
- 04
Delivery and knowledge transfer.
1 week
Final report delivery includes presentations to audit committees and executive teams. Management action plan support and knowledge transfer sessions build your team's ongoing capability so governance becomes embedded rather than dependent on external consultants.
The frameworks every assessment maps to.
Australia's regulatory environment for AI is evolving fast. Multiple frameworks now require or strongly encourage impact assessments. Our methodology stays current with each.
Effective 1 July 2025. Board-approved operational risk frameworks must explicitly cover technology risks including AI. Material service providers are in scope, meaning third-party AI vendors must be assessed under your operational risk framework.
23 AFS and credit licensees reviewed across 624 AI use cases. Governance frameworks lagging adoption, 48% of licensees without fairness and bias policies. An impact assessment directly addresses these gaps.
Automated decision-making transparency requirements commence December 2026. Organisations must provide meaningful explanations when substantially automated decisions affect individuals. Impact assessment evaluates whether your systems can meet these transparency requirements.
Australia's 8 AI Ethics Principles and the Voluntary AI Safety Standard's 10 guardrails provide assessment criteria that complement mandatory requirements. ISO 42001 Annex B and the NIST AI RMF provide structured methodology across the AI lifecycle.
Related services.
Impact assessment is one component. Most Australian businesses combine assessment with ongoing advisory, framework development, and regulatory work.
Build comprehensive AI governance frameworks that satisfy regulatory requirements and support responsible adoption.
Assess and manage risks from AI vendors and embedded third-party AI, aligned to APRA CPS 230 material service provider requirements.
Navigate overlapping AI compliance requirements across APRA, ASIC, and the Privacy Act.
Close your AI governance gaps before regulators map them for you.
Independent assessment provides Australian businesses with assurance that AI systems meet regulatory expectations, operate fairly, and have appropriate governance in place. Initial consultation at no obligation. Fixed-price engagements. Board-ready deliverables.