Artificial Intelligence Risk Framework Development
We help Australian businesses build comprehensive AI risk frameworks that integrate with existing enterprise risk management, satisfy APRA requirements, and deliver board-ready reporting.
We develop AI-specific risk taxonomies, assessment methodologies, and controls libraries aligned to NIST AI RMF, ISO 42001, and Australian regulatory expectations, giving your organisation the governance solutions it needs for responsible AI adoption.
The Challenge Facing Australian Businesses
AI introduces novel risks that don't fit neatly into traditional risk categories. Most organisations are deploying AI solutions faster than their governance and risk management strategies can keep pace, creating exposure that boards and regulators are increasingly unwilling to accept.
Missing AI Risk Taxonomy
Generic enterprise risk categories don't capture AI-specific risks like model drift, training data bias, hallucinations in generative AI systems, or third-party vendor dependencies. Without a purpose-built taxonomy, businesses cannot systematically identify and assess their machine learning and AI risk exposure.
Assessment Complexity
Traditional risk assessment methods don't account for dynamic AI behaviour. A machine learning model performing well today may degrade silently over time without proper monitoring. Generative AI solutions add further complexity with emergent behaviours that static assessments cannot capture.
Control Gaps
Existing IT controls weren't designed for AI systems. Organisations lack controls for bias testing, explainability validation, model performance monitoring, and data governance throughout the AI lifecycle. These gaps create regulatory, reputational, and operational risk that existing strategies fail to address.
"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), reviewing AI governance at 23 AFS and credit licensees across Australia
Why AI Risk Frameworks Matter for Australian Businesses
Australian businesses are deploying AI faster than their risk management can adapt. Without structured frameworks, they face regulatory enforcement, financial losses, and erosion of stakeholder trust. A purpose-built AI risk framework transforms uncertainty into managed, measurable risk.
of AI use cases at Australian licensees are less than two years old or still in development, per ASIC REP 798. This means governance has not kept pace with deployment.
of generative AI use cases at reviewed Australian licensees were deployed in 2022-2023, creating a surge of ungoverned AI solutions with limited risk oversight.
of Fortune 100 companies now cite AI risk in board oversight reporting, up from 16%, reflecting a global shift in how boards govern AI.
average short-term cumulative abnormal return loss for financial services firms experiencing AI incidents, demonstrating the tangible business value of proactive risk management.
Regulatory Pressure Is Intensifying
APRA CPS 230 commenced 1 July 2025, requiring material service provider registers by 1 October 2025. The Privacy Act automated decision-making requirements take effect 10 December 2026. Businesses using AI in regulated contexts need robust governance frameworks now, not when enforcement actions begin. We help you build strategies that stay ahead of regulatory expectations.
AI Adoption Growth Demands Governance
ASIC found that 61% of licensees plan to increase their AI use in the next 12 months. This growth in AI and machine learning solutions is healthy, but only when accompanied by proportionate risk governance. Businesses that establish frameworks now will capture the benefits of AI while competitors face costly remediation later.
Our AI Consulting Methodology
We build AI risk frameworks that integrate seamlessly with your existing enterprise risk management. No parallel governance structures are needed because AI risk becomes part of how your organisation already manages risk.
Typical engagements run 10-16 weeks, with our consultants working alongside your risk, compliance, and technology teams to deliver solutions that are operationally practical, not just theoretically sound. This collaborative strategy ensures the framework is adopted and sustained long after our engagement ends.
Aligned to Leading Standards
Discovery and AI Landscape Assessment
We begin with a thorough assessment of your current AI landscape: every AI and machine learning system in production, in development, or planned. We map these against your existing risk management framework to identify gaps in coverage, governance maturity, and regulatory alignment.
This phase typically takes 2-3 weeks and includes stakeholder interviews across your technology, risk, compliance, and business teams. We also review your data governance practices to understand how AI solutions interact with sensitive data across your organisation.
AI Risk Taxonomy Development
We create a comprehensive AI risk classification system covering technical risks (model performance, drift, bias), operational risks (availability, integrity, continuity), legal risks (liability, privacy, intellectual property), and strategic risks, all mapped to your existing enterprise risk categories and aligned to the NIST AI RMF and MIT AI Risk Repository.
The taxonomy distinguishes between predictive AI, generative AI, and machine learning systems, applying differentiated risk strategies appropriate to each technology type. This ensures your risk management approach is proportionate and practical, not one-size-fits-all.
Assessment Methodology Design
We develop structured assessment approaches for different AI use cases: credit risk models, fraud detection, customer service automation, generative AI content creation, and operational decision support. Each methodology includes materiality thresholds aligned to your risk appetite and business value considerations.
We design tiered assessment models so that low-risk AI solutions receive proportionate oversight while high-impact systems undergo rigorous evaluation, ensuring governance scales without creating bottlenecks.
Controls Library Creation
We build a library of 50+ AI-specific controls mapped to NIST AI RMF functions (Govern, Map, Measure, Manage): preventive controls (data validation, access management, bias prevention), detective controls (performance monitoring, drift detection, output validation), corrective controls (retraining triggers, incident response, model rollback), and governance controls (approval workflows, audit trails, documentation requirements).
Each control includes implementation guidance, testing procedures, and effectiveness metrics. Controls are designed for both traditional machine learning systems and generative AI solutions, reflecting the distinct risk profiles of each technology.
Three Lines of Defence Integration
We define clear responsibilities across the three lines of defence for AI governance: first line (development standards, testing, ongoing monitoring), second line (independent validation, compliance oversight, risk reporting), and third line (internal audit, assurance reviews). No gaps, no duplication, no ambiguity.
This operating model is critical for APRA-regulated entities where CPS 230 requires clear accountability for operational risk management. We ensure each line understands its AI-specific responsibilities within your broader risk governance strategy.
Board Reporting and KRI Framework
We create Key Risk Indicators (KRIs) and board-level dashboards that communicate AI risk in terms executives understand. Clear escalation pathways, decision rights documentation, and reporting cadences ensure your board and risk committee receive actionable insight, not just data.
Our solutions include template reporting packs, quarterly risk summaries, and incident escalation protocols. We design these to demonstrate governance maturity to regulators while providing genuine business value to your leadership team.
What You Receive: Practical AI Governance Solutions
Every deliverable is designed for operationalisation, not just documentation. We provide solutions that your risk, compliance, and technology teams can put into practice from day one.
AI Risk Taxonomy
Comprehensive classification of AI risks tailored to your organisation and the Australian regulatory context. Includes document and Excel taxonomy for GRC integration, with distinct categories for machine learning, generative AI, and decision-automation systems.
Assessment Methodology
Step-by-step methodology for assessing AI risks across the full lifecycle, from design and development through deployment and monitoring. Includes templates for quantitative and qualitative assessment, with tiered approaches based on use case criticality.
AI Risk Register
Pre-populated risk register with common AI risks, control mappings, risk owners, and assessment fields. Delivered in Excel or GRC-compatible format, ready for integration into your existing enterprise risk management system.
Controls Library
50+ AI-specific controls mapped to risk categories and NIST AI RMF functions (Govern, Map, Measure, Manage). Includes policy templates, implementation guides, and testing procedures for both machine learning and generative AI solutions.
Three Lines Framework
Roles, responsibilities, and operating model for AI risk governance across first, second, and third lines of defence. Designed for practical adoption by your existing team structures with clear accountability and escalation pathways.
Board Reporting Pack
Templates for AI risk reporting to board and risk committee. Includes KRI definitions, dashboard designs, escalation protocols, and quarterly reporting cadences that demonstrate governance maturity to regulators.
Industries We Serve Across Australia
Our AI consulting services are tailored to the specific regulatory, operational, and strategic context of each industry. We understand that a risk framework for a bank looks fundamentally different from one designed for a healthcare provider or government agency, and we build solutions accordingly.
Financial Services
AI risk frameworks aligned to APRA CPS 230, ASIC REP 798, and FAR requirements. We understand credit risk models, fraud detection AI, and customer-facing machine learning solutions in the Australian financial services landscape.
Learn more →Healthcare
Governance strategies for clinical AI, diagnostic machine learning models, and patient data-driven AI systems. We address the unique ethical, safety, and data governance requirements of healthcare AI in Australia.
Learn more →Government
Risk frameworks aligned to the Australian AI Ethics Principles and Digital Transformation Agency guidelines. Our AI consulting solutions help government agencies balance innovation with transparency, accountability, and public trust.
Learn more →Technology
Risk governance for businesses building or deploying AI products at scale. We help technology companies establish frameworks that support rapid innovation while managing the risks of generative AI, machine learning, and automated decision-making.
Learn more →Who This Is For
We work with senior leaders and risk professionals who need to integrate AI risk into existing enterprise risk management without creating parallel governance structures or slowing innovation.
APRA-Regulated Entities
Banks, insurers, and superannuation trustees preparing for CPS 230 compliance and seeking strategies to govern AI across their operations.
Financial Services Licensees
AFS and credit licensees responding to ASIC REP 798 governance expectations for AI and machine learning solutions in customer-facing and operational contexts.
Enterprise Risk Teams
Organisations with mature ERM programs that need to extend coverage to AI systems, including generative AI tools adopted across the business.
Board and Audit Committees
Directors seeking assurance that AI risks are properly identified, assessed, and governed, with reporting solutions that support informed decision-making at board level.
Chief Technology and Data Officers
Technology leaders navigating the digital transformation of their organisations who need risk frameworks that enable responsible AI innovation and machine learning deployment without creating bureaucratic drag.
Why Australian Businesses Choose Our AI Consulting Team
We are not a general management consultancy that has added AI to its service catalogue. Our team is purpose-built to deliver AI governance and risk management solutions for organisations navigating Australia's evolving regulatory landscape.
AI Governance Specialists
We focus exclusively on AI governance, risk management, and compliance. This specialisation means deeper expertise, more refined strategies, and solutions that reflect the latest regulatory developments in Australia and globally.
Australian Regulatory Depth
We understand the intersection of APRA CPS 230, ASIC REP 798, the Privacy Act, and the Australian AI Ethics Principles. We build frameworks that address the specific requirements facing Australian businesses, not generic international templates repackaged for the local market.
Integration-First Approach
We design AI risk frameworks to complement your existing enterprise risk management, not replace it. This integration-first strategy means faster adoption, lower cost, and genuine transformation of how your organisation governs AI without disrupting what already works.
Multi-Framework Expertise
Our AI risk taxonomy is aligned to NIST AI RMF, ISO 42001, ISO 31000, and the MIT AI Risk Repository. This multi-framework approach ensures your risk governance meets international best practice while addressing Australia-specific requirements for data governance and AI accountability.
Operational Focus
Every deliverable is designed for operationalisation. We test frameworks against real AI use cases in your organisation, ensuring the solutions we deliver are practical and not shelf-ware that fails at first contact with operational reality.
Defined Timeline and Investment
Typical engagements run 10-16 weeks with clear milestones and transparent pricing. We scope engagements to deliver maximum business value within your budget and timeline, with no open-ended retainers or scope creep.
Frequently Asked Questions
Common questions from Chief Risk Officers, board directors, and technology leaders considering AI risk framework development for their organisations.
How does this integrate with our existing ERM framework?
We design AI risk frameworks to complement your existing enterprise risk management, not replace it. AI risks are categorised within your existing risk taxonomy where possible, with new categories only where AI-specific risks genuinely differ. This integration strategy ensures your risk team can manage AI risks using the same tools, processes, and reporting structures they already know, accelerating adoption and reducing cost.
Does this satisfy APRA CPS 230 requirements?
Yes. Our frameworks are specifically designed for APRA-regulated entities in Australia. We map all AI risks to CPS 230 operational risk management requirements and include material service provider assessment for third-party AI vendors. CPS 230 commenced 1 July 2025, with the material service provider register deadline of 1 October 2025. We help businesses meet both requirements as part of a unified governance strategy.
What about generative AI risks?
Our AI risk taxonomy includes comprehensive generative AI-specific risks: hallucinations, prompt injection, data leakage, intellectual property concerns, model output accuracy, and unintended content generation. ASIC found that 92% of generative AI use cases at reviewed licensees were deployed in 2022-2023, meaning most organisations have deployed these AI solutions without proportionate risk governance. We provide specific assessment methodologies and controls for generative AI that address these emerging risks while enabling responsible innovation.
How long does the engagement take and what does it cost?
Typical engagements run 10-16 weeks depending on scope: Discovery (2-3 weeks), Design (4-6 weeks), Validation (2-3 weeks), and Delivery (2-4 weeks). Framework development engagements are scoped to deliver maximum value within defined timelines. We can accelerate for CPS 230 deadline requirements or stage deliverables to align with your organisation's roadmap and budget cycle.
How do you handle machine learning model risk versus generative AI risk?
Our risk taxonomy and assessment methodology differentiate between predictive machine learning, generative AI, and rule-based automation. Each technology type has distinct risk profiles: machine learning models carry drift, bias, and explainability risks; generative AI solutions introduce hallucination, prompt injection, and IP risks. We design tiered assessment approaches and specific controls for each, ensuring your governance strategies are proportionate and technology-appropriate.
What data governance considerations are included?
Data governance is foundational to effective AI risk management. Our frameworks address data quality, lineage, consent, and privacy requirements throughout the AI lifecycle. For Australian businesses, this includes alignment with the Privacy Act, the Australian AI Ethics Principles, and industry-specific data handling requirements. We ensure your AI risk framework connects to your existing data governance program so that data risks are managed holistically, not in isolation.
Can you support organisations outside financial services?
Absolutely. While we have deep expertise in financial services governance, we serve businesses across healthcare, government, technology, and other sectors across Australia. Our methodology adapts to the specific regulatory environment, risk appetite, and AI maturity of each organisation. The core principles of AI risk management (identification, assessment, control, and monitoring) apply universally, but the implementation strategies differ significantly by industry.
Related AI Consulting Services
AI risk framework development is one component of a comprehensive governance strategy. Australian businesses combine this service with complementary solutions to accelerate their governance maturity.
AI Governance Consulting
Comprehensive governance program design including operating models, committee structures, and policy frameworks for AI. We help your team build end-to-end governance solutions.
Learn more →AI Audit & Assessment
Independent assessment of your current AI governance maturity and regulatory readiness. We evaluate your AI risk management against Australian regulatory expectations and international standards to identify gaps and prioritise remediation.
Learn more →ISO 42001 Certification
Implementation consulting for the international AI management system standard. Demonstrate your organisation's commitment to responsible AI through certification that is increasingly recognised by Australian regulators and stakeholders.
Learn more →Build Your AI Risk Framework
Schedule a consultation to discuss your risk management requirements. We help Australian businesses build governance solutions that satisfy regulators, protect your organisation, and enable responsible AI adoption.