A structured 48-item risk register across 8 risk domains with a 5x5 scoring matrix to help CISOs identify, assess, treat, and track AI-specific risks. Covers data privacy, model reliability, bias, security, compliance, operational, and reputational risk categories with board-ready reporting dashboards.
A structured AI risk register template with 48 risk items across 8 domains. Includes 5x5 scoring methodology, risk treatment planning, and board reporting frameworks.
Organisations using AI extensively but without security safeguards pay an average of $4.88M per data breach - and an additional $1.76M compared to those with AI-specific governance controls in place, making a structured AI risk register one of the highest-ROI risk management investments available (IBM 2024 Cost of a Data Breach).
Only 1 in 4 generative AI projects include any form of risk assessment before deployment, yet 82% of enterprise boards now expect quarterly AI risk reporting - this template bridges the gap with a ready-to-use register structure that maps directly to NIST AI RMF, EU AI Act, and ISO/IEC 42001 requirements.
The 5x5 risk scoring matrix in this template uses clearly defined likelihood and impact criteria calibrated to AI-specific scenarios - from training data poisoning (likelihood 3, impact 5) to model drift degradation (likelihood 4, impact 3) - enabling consistent, defensible risk prioritisation across business units.
AI model and technical risks account for 40% of all AI-related incidents in enterprise environments, yet most risk registers treat AI as a single line item under technology risk. This template breaks AI risk into 8 distinct domains with 48 specific risk items, giving risk managers the granularity needed for effective treatment planning.
Regulatory enforcement risk is accelerating: EU AI Act penalties reach 7% of global revenue, the Colorado AI Act took effect in February 2026, and NIST AI RMF adoption is now expected for US federal contractors. This register includes cross-jurisdictional compliance risk tracking to prevent gaps as enforcement timelines converge.
A structured risk register for identifying, scoring, treating, and tracking AI-specific risks across your organisation with board-ready reporting.
Establish the foundational structure for your AI risk register. A well-structured register ensures consistent documentation, clear ownership, and traceable risk management decisions.
Build a comprehensive catalogue of AI risk categories tailored to your organisation. Effective risk identification requires structured analysis across technical, legal, operational, and reputational dimensions.
Maintain a living AI risk inventory, report quantified risk posture to the board, and demonstrate risk reduction over time
Operationalise AI risk identification, scoring, and treatment planning with consistent methodology across business units
Track regulatory compliance risks across EU AI Act, NIST AI RMF, GDPR, and emerging state-level AI legislation
Identify and escalate technical AI risks including model drift, bias, hallucination, and adversarial vulnerabilities
Integrate AI risk into the enterprise risk management framework with consistent scoring and appetite alignment
Sections 4 and 5 address AI-specific risks for healthcare organisations processing PHI, including clinical decision support hallucination risk, patient data exposure in AI prompts, and FDA pre-market requirements for AI/ML-enabled medical devices. Risk scoring criteria are calibrated for patient safety impact.
Sections 3 and 5 provide AI model risk management aligned to SR 11-7 (Federal Reserve guidance on model risk), including model validation requirements, bias testing for lending and insurance decisions, and DORA ICT risk management for AI-dependent financial processes.
Sections 4 and 6 address legal-sector-specific risks: AI hallucination in legal research and contract drafting, client confidentiality exposure through AI prompts, cross-border privilege implications, and compliance with bar association ethics opinions on AI usage in legal practice.
Sections 2 and 6 align to NIST AI RMF Govern, Map, Measure, and Manage functions with additional coverage of Executive Order 14110 requirements for federal AI safety, FedRAMP-aligned deployment controls, and public accountability obligations for government AI systems.
Establish the foundational structure for your AI risk register. A well-structured register ensures consistent documentation, clear ownership, and traceable risk management decisions across the organisation.
Build a comprehensive catalogue of AI risk categories tailored to your organisation. Effective risk identification requires structured brainstorming across technical, legal, operational, and reputational dimensions - not just a generic IT risk list.
Implement a consistent, defensible risk scoring methodology calibrated for AI-specific scenarios. The 5x5 likelihood-impact matrix provides sufficient granularity for meaningful prioritisation without creating false precision.
Drill into AI-specific data and privacy risks that go beyond traditional data protection. AI systems create novel data exposure vectors - from training data memorisation to inference-based re-identification - that require dedicated risk entries.
Take our 2-minute assessment and get a personalised AI governance readiness report with specific recommendations for your organisation.
Start Free AssessmentAddress the technical risks unique to AI and machine learning systems. These risks are often invisible to traditional IT risk management but can cause significant operational, financial, and reputational damage if left unmanaged.
Track the rapidly evolving AI regulatory landscape and identify compliance gaps before they become enforcement actions. AI regulation is moving from voluntary frameworks to mandatory obligations with significant penalties.
Translate risk assessment into action. Every risk above your defined appetite threshold must have a documented treatment decision - accept, mitigate, transfer, or avoid - with clear rationale, control mapping, and residual risk tracking.
Establish a disciplined review rhythm and board reporting framework. An AI risk register is a living document - its value depends on consistent updates, trend analysis, and clear communication of risk posture to executive leadership.
Build a complete AI governance programme with these complementary templates.
A comprehensive 47-point checklist across 9 security domains to help CISOs build a board-ready AI governance policy. Covers acceptable use, data classification, shadow AI, vendor assessment, compliance mapping, incident response, and more.
Download FreeA comprehensive 58-control checklist across 9 compliance domains to help organisations achieve full conformity with the EU AI Act (Regulation (EU) 2024/1689). Covers AI system classification, prohibited practice screening, high-risk requirements, transparency obligations, data governance, human oversight, GPAI model compliance, risk management, and documentation requirements - mapped to specific Articles and Annexes of the regulation.
Download FreeA structured board reporting template with 48 items across 8 sections for presenting AI risk posture to directors and executives. Includes executive dashboard structure, risk scoring visualisation, compliance status tracking, incident reporting cadence, ROI metrics, peer benchmarking, and quarterly workflow guidance aligned to NIST AI RMF, EU AI Act, ISO/IEC 42001, and SOX requirements.
Download FreeA comprehensive framework for quantifying AI governance ROI, including cost models, TCO comparisons, and a CFO-ready business case template. Learn how structured AI governance delivers 3-5x return within 18 months.
A step-by-step framework for creating an AI governance program in a mid-market organization. Covers stakeholder alignment, policy development, tool selection, deployment, compliance mapping, and measurement with a 90-day implementation timeline.
Ungoverned AI costs mid-market enterprises an average of $4.2M annually through data breaches, compliance penalties, productivity loss, and vendor sprawl. This analysis quantifies each cost category with real-world examples and calculates the ROI of AI governance.
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“This framework saved us 3 months of policy development. We went from zero AI governance to audit-ready in under 2 weeks.”
— Security Leader, Mid-Market Healthcare Organisation
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