What the playbook covers
The UK Government published their Artificial Intelligence (AI) Playbook on 10 February 2025, setting out 10 Principles for using AI in government organisations.
The playbook updates previous UK government publications, providing an expanded guide designed to help public sector organisations harness AI technologies safely, effectively, and responsibly. It is a must-read for risk managers, cyber-security professionals, and compliance experts working with or within the UK public sector. The playbook provides guidance and principles to navigate the unique challenges and opportunities presented by AI.
The 10 principles set out to guide the safe, responsible, and effective use of AI in government organisations, building on the 5 principles for regulators outlined in the 2023 policy paper (white paper) A pro-innovation approach to AI regulation.
Who should read this
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For risk managers, the playbook emphasises understanding AI’s limitations and potential risks. It introduces key principles for using AI lawfully, ethically, and responsibly, including managing the AI lifecycle and ensuring meaningful human control. The playbook also highlights the importance of risk assessments and mitigation strategies.
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Cyber security professionals will find valuable insights into securing AI systems. The playbook flags potential security risks, such as data poisoning, perturbation attacks, and prompt injections. It stresses the need for robust security measures, including security testing and content filtering. A cross-government AI security group has been set up (gov.uk email addresses only) to bring expertise together from across government departments.
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Compliance experts are guided to ensure AI systems adhere to legal and regulatory requirements. The playbook highlights the importance of data protection, privacy, and ethical considerations. It refers to existing guidance and frameworks, such as the Data Ethics Framework and the Algorithmic Transparency Recording Standard (ATRS). It provides guidance on implementing transparency measures and addressing fairness, bias, and discrimination in AI systems.