> ## Content Index > Fetch the complete content index at: https://www.aigl.blog/llms.txt > Use this file to discover other available public pages before exploring further. # AI Risk Management Toolkit: guidance - URL: https://www.aigl.blog/ai-risk-management-toolkit-guidance/ - Published: 2026-09-28T11:01:16.000Z - Updated: 2026-09-28T11:01:16.000Z - Description: UK government guidance from DSIT that helps public sector teams identify, assess, score and treat AI risks across the AI lifecycle, with risk appetite levels, quantification scales, identification questions and treatment suggestions in four appendices. - Author: Jakub Szarmach - Tags: Toolkit, Risk Management, AI Governance, Public Sector & National Security, Compliance & Audit, AI Policy & Regulation, #aigl-library ## ⚑ Quick Summary Published by the Department for Science, Innovation & Technology (DSIT) and dated 8 September 2026, this guidance accompanies the AI Risk Management Toolkit for the UK public sector. It is aimed at anyone involved in the design, operation, procurement and delivery of AI-enabled products, and at multi-disciplinary project teams spanning data science, engineering, project delivery, IT, change management and communications. The toolkit is positioned as a starting point for good risk management. It implements Section D: Risk Management Processes of the Orange Book β€” risk identification and assessment, risk treatment, risk monitoring and risk reporting β€” and is designed to work alongside the Cyber Assessment Framework (CAF). It covers the whole AI lifecycle, from use case identification to retirement, and argues that because AI systems change and evolve there is no 'final version' to validate against, so risk management must be continuous. Its deliverables are a guide to AI risk assessment, a set of critical risk identification questions, a workbook to record risks and treatment actions, and an AI Risk Monitoring Dashboard showing the risk profile of a solution and the chances of different degrees of success and failure. Risks are scored on 1 to 5 likelihood and impact scales, multiplied together to give a risk score. ## 🧩 What’s Covered - **Purpose and audience:** the toolkit supports anyone involved in the design, operation, procurement and delivery of AI-enabled products, benefits multi-disciplinary teams, and is a starting point for good risk management β€” aligned to the Orange Book and designed to work with the Cyber Assessment Framework. - **Using these tools:** argues there is no 'final version' of an AI system to validate against, so risk management runs from use case identification to retirement, and names the AI risk management team roles, from AI governance officers and senior leaders to data teams, AI practitioners, security, legal and compliance professionals, business domain experts and end users. - **AI risk identification:** defines a risk as a potential future event that affects objectives, and lists nine categories, including financial, legal and regulatory compliance, appropriate transparency and explainability, fairness, accountability and governance, contestability and redress, technical robustness, security, and risks to people and the environment. - **AI risk appetite:** states that appetite should be set organisationally, aligned to departmental appetite and signed off at board level, and presents four levels in Appendix 2: Averse, Minimal, Cautious and Open. - **Quantifying risk:** likelihood and impact are scored 1 to 5 and multiplied to give a risk score; the likelihood scale runs from rare (under 5 per cent) to almost certain (over 80 per cent), estimated through historical data analysis, model analysis, expert judgement, and experimentation and monitoring. - **Quantifying impact:** separates quantifiable impacts, such as financial loss, operational downtime and complaint volumes, from non-quantifiable ones, such as strategic setback, morale and public trust; Appendix 3 sets impact bands from under Β£10,000 to more than Β£1 million. - **Treatment:** four categories β€” avoidance, limiting, transference and acceptance β€” with Appendix 4 suggesting treatments for risks such as poor accuracy, bias and fairness, transparency, privacy, security, hallucinations and third-party failure. - **Additional resources:** points to standards work by ISO, IEC and IEEE SA, the AI Standards Hub, and DSIT's CAIRF team, which is preparing common scenarios and mapping the causes of AI risks. ## πŸ’‘ Why it matters? Teams adopting AI in UK public bodies often lack a shared vocabulary for describing what could go wrong and how much risk is acceptable. This guidance supplies that vocabulary: nine risk categories, a common 1 to 5 scoring scale, probability bands and a four-level appetite ladder, so that risk registers, assurance work and board sign-off can be compared across projects. It connects AI risk work to instruments teams already use β€” the Orange Book, the Cyber Assessment Framework, data protection and equality law, the Public Sector Equality Duty and the Algorithmic Transparency Recording Standard β€” and lets them reuse evidence already gathered for cyber assessments where the response is expanded to cover AI. ## ❓ What’s Missing The toolkit is deliberately a starting point rather than a complete method. Appendix 1 is described as not exhaustive, and Appendix 4's treatments are 'general and without context', so teams must judge which options fit their situation. No worked scoring example, no weighting method and no reproduced workbook or dashboard are supplied, even though the workbook and AI Risk Monitoring Dashboard are named as parts of the toolkit. The document is written for UK public sector organisations and cites UK law, regulators and guidance; the EU AI Act appears only as a consideration for continuing to export to trading partners. ## πŸ‘₯ Best For This suits AI governance officers, risk and assurance leads, and multi-disciplinary delivery teams in UK public sector organisations who need to identify, score and treat AI risks, set or apply a departmental risk appetite, and populate a risk register. Privacy, security, legal and business domain experts asked to contribute to risk identification will find the question sets in Appendix 1 directly usable. ## πŸ“„ Source Details *AI Risk Management Toolkit: guidance*, published by the Department for Science, Innovation & Technology and dated 8 September 2026; Crown copyright 2026, licensed under the Open Government Licence v3.0\. The publication runs to 44 pages, and this review is based on the full text extraction (44 of 44 pages). The document prints its landing page: [https://www.gov.uk/government/publications/ai-risk-management-toolkit/ai-risk-management-toolkit-guidance](https://www.gov.uk/government/publications/ai-risk-management-toolkit/ai-risk-management-toolkit-guidance?ref=aigl.blog). No individual authors are named; the publisher is the department. Several cells in the 'Open' column of the Appendix 2 table are truncated in the extraction.