Guide to implementing UK Code of Practice for the cyber security of AI

Awareness and training

A successful cyber security strategy begins with empowering people. Raising awareness through comprehensive training ensures your teams - from developers to operational staff - can identify, understand and mitigate AI-specific threats such as data poisoning, adversarial attacks and prompt injections. Tailored training programs foster proactive security practices and enhance human responsibility and oversight within your AI environment.

Secure design and testing

Security must be integral from the initial stages of AI system development. By embedding security considerations through AI-specific threat modeling and early incorporation of controls, you mitigate vulnerabilities proactively. Rigorous testing and evaluation ensure systems remain resilient against evolving threats. Regular, structured testing scenarios further validate AI performance under potential cyber-attacks and unexpected inputs, ensuring both security and reliability.

Comprehensive risk management and governance

Implementing AI systems within a structured governance framework, such as the NIST AI Risk Management Framework, is essential for systematic risk oversight and accountability. This structured approach supports ongoing evaluation, facilitates timely mitigation of threats and ensures a clear chain of human responsibility. Embedding AI-specific risk assessments within your broader governance processes enhances compliance, mitigates vulnerabilities and fosters a culture of informed and transparent decision-making.

Infrastructure and operational security

Resilient AI infrastructure depends on foundational operational security measures. Regular security updates, patches, and continuous monitoring are vital to protect against evolving threats. Ensuring secure cloud configurations, robust access controls, encryption and proactive incident response mechanisms further fortifies the operational resilience of your AI deployments - significantly reducing potential vulnerabilities.

Asset, data and supply chain security

Managing your AI assets including datasets, models, software dependencies and APIs requires rigorous documentation, tracking and protection measures. A thorough inventory and auditing process safeguards the integrity of your assets throughout their lifecycle, supported by strong documentation practices. Equally, robust due diligence within your supply chain ensures third-party components and services meet your security standards, mitigating risks associated with external dependencies.

Transparent stakeholder communication

Clear and transparent communication with end-users and stakeholders enhances trust and confidence in AI systems. By openly disclosing AI system functionalities, potential risks and implemented safeguards, you promote informed decision-making, responsible usage and swift incident responses. Transparency also bolsters stakeholder trust and aligns your organisational values with societal expectations.

The benefits of adopting the Code of Practice

Adopting the UK's AI Cyber Security Code of Practice provides your organisation with a structured and practical approach to managing AI security risks. Beyond compliance, it strengthens your cyber security posture, enhances stakeholder trust and positions your organisation as a responsible innovator. By embedding these practices into your AI lifecycle, you gain not only regulatory confidence but also the resilience required to innovate safely.