AI risk frameworks built to satisfy APRA, ASIC, and your board.
We help Australian businesses build comprehensive AI risk frameworks that integrate with existing enterprise risk management, satisfy APRA CPS 230, and deliver board-ready reporting. AI-specific risk taxonomies, assessment methodologies, and 50+ controls aligned to NIST AI RMF, ISO 42001, and Australian regulatory expectations.
Built for
What you walk away with.
AI risk taxonomy
Comprehensive classification covering technical, operational, legal, and strategic risks. Distinct categories for predictive ML, generative AI, and decision-automation.
Assessment methodology
Tiered assessment models with materiality thresholds. Templates for quantitative and qualitative review across the full AI lifecycle.
50+ controls library
Preventive, detective, corrective, and governance controls mapped to NIST AI RMF functions (Govern, Map, Measure, Manage).
Three lines of defence
Roles, responsibilities, and operating model across first, second, and third lines, with KRIs and a board reporting pack ready to use.
Why generic risk frameworks miss AI exposure.
AI introduces novel risks that do not map onto traditional risk categories. Most Australian organisations are deploying AI faster than their governance can adapt, and the cost of catching up after a regulator surfaces a gap is materially higher than building the framework first.
- 01
Missing AI risk taxonomy.
Generic enterprise risk categories do not capture model drift, training data bias, hallucinations in generative AI systems, or third-party AI vendor dependencies. Without a purpose-built taxonomy, businesses cannot systematically identify and assess their machine learning and AI risk exposure.
- 02
Static assessments miss dynamic behaviour.
Traditional assessment methods do not account for dynamic AI behaviour. A machine learning model performing well today may degrade silently over time without proper monitoring. Generative AI adds emergent behaviours that static review cannot capture.
- 03
Existing IT controls leave gaps.
Existing IT controls were not designed for AI. Organisations lack controls for bias testing, explainability validation, model performance monitoring, and data governance throughout the AI lifecycle. The gaps create regulatory, reputational, and operational risk.
"The maturity of governance and risk management did not always align with the nature and scale of licensees' AI use. Nearly half of the licensees we reviewed do not have a policy on fairness and bias for their AI use."
ASIC REP 798. Beware the gap, October 2024. Review of 23 AFS and credit licensees.
The data that makes governance urgent.
ASIC REP 798
57%
of AI use cases at Australian licensees are less than two years old or still in development.
GenAI
92%
of generative AI use cases at reviewed Australian licensees were deployed in 2022 to 2023.
Board focus
48%
of Fortune 100 companies now cite AI risk in board oversight reporting, up from 16%.
Market impact
-21%
average short-term cumulative abnormal return for financial services firms experiencing AI incidents.
Our risk framework methodology.
Engagements typically run 10 to 16 weeks. We design frameworks that complement your existing enterprise risk management. AI risk becomes part of how your organisation already manages risk, not a parallel governance structure.
- 012 to 3 weeks
Discovery and AI landscape assessment.
Every AI and machine learning system in production, in development, or planned. Mapped against your existing risk framework to identify gaps in coverage, governance maturity, and regulatory alignment.
- 02
AI risk taxonomy development.
A classification system covering technical risks (model performance, drift, bias), operational risks (availability, integrity, continuity), legal risks (liability, privacy, IP), and strategic risks. Mapped to your enterprise risk categories and aligned to NIST AI RMF and the MIT AI Risk Repository.
- 03
Assessment methodology design.
Structured assessment approaches for credit risk models, fraud detection, customer service automation, generative AI content creation, and operational decision support. Tiered so low-risk solutions receive proportionate oversight while high-impact systems undergo rigorous evaluation.
- 04
Controls library creation.
50+ AI-specific controls mapped to NIST AI RMF functions: preventive (data validation, access management, bias prevention), detective (performance monitoring, drift detection), corrective (retraining triggers, model rollback), and governance (approval workflows, audit trails). Each control includes implementation guidance, testing procedures, and effectiveness metrics.
- 05
Three lines of defence integration.
Clear responsibilities across the three lines for AI governance: first line (development standards, testing, ongoing monitoring), second line (independent validation, compliance oversight, risk reporting), and third line (internal audit). Critical for APRA-regulated entities where CPS 230 requires clear accountability for operational risk management.
- 06
Board reporting and KRI framework.
Key Risk Indicators and board-level dashboards that communicate AI risk in terms executives understand. Template reporting packs, quarterly risk summaries, and incident escalation protocols designed to demonstrate governance maturity to regulators.
Practical governance artefacts.
Every deliverable is designed for operationalisation, not just documentation. Your risk, compliance, and technology teams can put each artefact into practice from day one.
Related AI consulting services.
Build a risk framework that integrates with what you already have.
Schedule a consultation to discuss your risk management requirements. We help Australian businesses build governance solutions that satisfy APRA and ASIC, protect your organisation, and enable responsible AI adoption.