Compliance, Audit & Documentation

As AI regulation increases globally, the ability to demonstrate that your AI systems are developed and operated responsibly is becoming a practical necessity, not just a nice-to-have. Compliance means understanding which regulations apply to your AI activities - from the EU AI Act to sector-specific rules in finance, healthcare, and employment - and ensuring your practices meet those requirements. Audit means being able to prove it, which requires documentation. Good AI documentation records the decisions made throughout the development lifecycle: what data was used and why, how the model was validated, what risks were identified and how they were mitigated, who approved deployment, and how the system is being monitored. This documentation needs to be maintained as a living practice, not created after the fact for an auditor. Many organisations are discovering that the documentation habits they need for compliance are actually valuable for their own purposes - they make it easier to debug problems, onboard new team members, and learn from past projects. Building documentation into your standard AI development workflow is far less painful than trying to reconstruct it retrospectively.