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A four-phase methodology for building, governing, and sustaining enterprise AI risk programs. Developed by AI risk governance specialists and aligned with the world's leading AI and risk management standards.
AIRA, AI Integrated Risk Architecture, is a structured, repeatable methodology developed through enterprise AI risk advisory practice. It provides organisations with a clear path from AI risk awareness to operational governance, ensuring AI systems are assessed, controlled, monitored, and continually improved in a way that satisfies board, regulatory, and investor expectations.
The framework is practical by design. Each phase maps to tangible outputs: governance documents, risk registers, board reports, control registers, and monitoring frameworks. AIRA is designed to be compatible with ISO/IEC 42001:2023 (the world's first certifiable AI management system standard), ISO 31000, the NIST AI RMF, and the EU AI Act, allowing organisations to demonstrate alignment with multiple frameworks through a single implementation.
Have a question about the AIRA Framework or how it applies to your organisation?
A complete lifecycle for AI risk governance
Understand your AI risk landscape
Map every AI system across the organisation. Classify each by risk level, High, Elevated, or Standard, based on use case, data sensitivity, decision impact, and regulatory exposure. Identify which regulatory frameworks apply. Establish a current-state governance maturity baseline.
Outputs
Build governance that holds under scrutiny
Establish a formal AI Governance Board with defined accountability across leadership, legal, technical, and domain functions. Deploy model risk controls. Define risk appetite for AI systems. Build documentation infrastructure, policies, standards, and audit trails, that satisfies regulators and institutional investors.
Outputs
Monitor, report, and assure
Stand up continuous model performance monitoring against defined thresholds and Key Risk Indicators. Establish escalation protocols and board-level AI risk reporting cadences. Conduct periodic independent governance maturity assessments to identify gaps before regulators or investors do.
Outputs
Stay ahead of regulatory and model change
AI regulation evolves rapidly across jurisdictions. AI models drift over time. AIRA's Adapt phase builds the organisational capability to respond proactively, updating frameworks as models and rules change, scanning regulatory horizons, and embedding post-incident learning.
Outputs
Within each AIRA phase, AI systems are evaluated against four dimensions that determine governance requirements and risk classification. These dimensions are what the AIRA name encapsulates.
Is there a named person, not a team, accountable for this AI system's decisions, performance, and conduct? Accountability must be assigned before deployment, not scrambled for after an incident.
What is the maximum potential harm this AI system can cause, to individuals, the organisation, or third parties, across financial, legal, reputational, and safety dimensions?
Can the AI system's actions and decisions be corrected, appealed, or reversed? Is there a documented process for human override, and can affected parties seek redress?
Can the AI system's decision-making be reconstructed and explained? Are there sufficient logs, documentation, and traceability for an independent reviewer to determine what happened and why?
How the dimensions relate to the phases: In the Assess phase, each AI system is evaluated against these four dimensions to determine its risk classification and required controls. The Implement, Review, and Adapt phases then build and maintain the governance structures that address each dimension.
AIRA is designed to be compatible with the four frameworks that matter most for AI governance, enabling organisations to demonstrate alignment across all of them through a single implementation.
Enterprise organisations deploying AI across operations, products, or internal decision-making processes
Investment firms conducting AI company due diligence or managing AI risk across a portfolio
Regulated industries navigating sector-specific AI compliance obligations in financial services, healthcare, energy, or critical infrastructure
AI-native companies building governance maturity for fundraising, enterprise sales, or regulatory approval
This page describes AIRA, AIRiskAware's own proprietary AI risk governance methodology, and explains how it is designed to align with ISO/IEC 42001:2023, ISO 31000:2018, the NIST AI Risk Management Framework, and the EU AI Act (Regulation (EU) 2024/1689). Compatibility claims reflect AIRiskAware's own assessment, not a certification or endorsement by ISO, NIST, the European Commission, or any accreditation body, and do not by themselves satisfy any legal, regulatory, or certification requirement. This is general information only, not legal, compliance, or certification advice; always verify current obligations against the official ISO, NIST, and EU sources and your own qualified legal counsel or certification body before relying on it.
AIRiskAware provides specialist advisory support for organisations implementing the AIRA framework, from initial assessment through to governance design, ISO 42001 alignment, and ongoing board-level assurance.