Risk Practitioners and Enterprise Risk Teams
Risk practitioners are the operational backbone of AI governance. The methodology you already know — risk identification, control design, monitoring, reporting — extends to AI more cleanly than it appears.
For: Risk managers, enterprise risk practitioners, first and second line of defence, risk consultants
For risk practitioners, AI governance is the most rapidly developing risk category in the operational portfolio. The good news: the foundational discipline transfers. Risk identification, control mapping, residual risk assessment, monitoring, and reporting all apply to AI. The work is to extend existing practice to AI-specific characteristics — model behaviour that shifts over time, vendor concentration in a small number of frontier providers, data flows through external systems, and emergent failure modes in autonomous agents. AIRiskAware's practitioner coverage is built by risk professionals for risk professionals — the language, methodology, and depth match what you actually need in the day-to-day.
What this role is accountable for
The substantive AI governance responsibilities that fall to this role under current Australian and global expectations.
- 1AI risk identification and classification across the organisation
- 2Risk register maintenance with AI-specific risks treated as first-class entries
- 3Control design and testing for AI use cases — both first-party and vendor-provided
- 4AI vendor risk assessment as part of broader third-party risk management
- 5Risk reporting — translating AI-specific risks for executive and board audiences
- 6Engagement with second line (compliance, GRC) and third line (audit) on AI matters
- 7Professional development — ISRM, ISACA AAIR, RMIA credentials, and AI governance training
Most relevant intelligence
Curated coverage selected for this role — frameworks, regulatory developments, and operational guidance you can act on.
How to Get Involved in Australian AI Policy
For risk practitioners wanting to contribute to Australian AI governance beyond their day job.
AI Governance Maturity Model
A five-stage maturity model — useful for benchmarking and improvement planning.
Integrated Assurance for AI Governance
APRA's integrated assurance framing in practical terms.
How to Write an AI Policy
Practical policy template with the mandatory elements.
AI Vendor Due Diligence
The questions to ask, the evidence to obtain.
AI Incident Response
Incident response procedures for AI-specific failure modes.
Frameworks that apply
The regulatory frameworks, standards, and guidance documents most relevant to this role.
General risk management framework extended for AI-specific guidance.
AI Management System — the certifiable standard for AI governance.
Govern, Map, Measure, Manage — widely adopted in enterprise risk practice.
Operational resilience — the procedural foundation for AI risk integration.