AI governance needs evidence that the system actually produces.
Good AI governance connects policies and responsibilities with the software's behaviour in practice. If an organisation makes claims about quality, oversight or traceability, it needs processes and records capable of supporting them.
The EU AI Act creates different obligations depending on factors such as the system's use, classification and the role of the organisation. Not all AI systems are high-risk, and engineering controls are only one part of compliance.
/01Start with classification.
Work with the organisation's legal, risk and compliance specialists to determine what applies to the particular system and business. A generic list of AI risks is not a legal assessment.
/02Make quality measurable.
Agree what good performance means for the use case. Create realistic evaluations of system behaviour, failure modes and the limits of available data.
/03Build traceability where required.
Design logs, records and change controls proportionately. Teams should know which versions were evaluated, how decisions were made and how important issues are investigated. Timely records and contemporaneous monitoring can strengthen defensibility.
/04Include people in the control system.
Human oversight is useful only when the responsible people can understand, review and intervene where the workflow requires it. The right design depends on the decisions an application is permitted to make.
/05Revisit it after release.
A useful programme keeps evaluating relevant changes and incidents as the application evolves. It does not treat the launch date as the end of quality management.
/06Where PhasedAI fits.
We help teams implement evaluation, monitoring and supporting technical records around AI applications. PhasedLoop is one part of that engineering capability, not a legal assessment or compliance certification.
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Know how your AI is performing.
Tell us about the system and the questions you need to be able to answer about it.