Artificial intelligence governance must broaden its focus from the underlying model to the entire decision environment, which includes prompts, memory, and retrieval systems.
Because these components can change silently while the product name remains the same, institutions face a risk called post-modification safety drift where a tool’s behaviour becomes unpredictable or biased.
To manage this, organizations should maintain an approved-state record that serves as a technical and behavioural baseline for evaluating updates.
Effective change control requires more than technical monitoring; it necessitates a formal process of re-governance to ensure that human oversight and institutional safety claims remain valid.
Material changes should be judged by their impact on human rights and safety rather than the technical size of the update.
This approach ensures that AI systems evolve under institutional authority rather than through unmanaged, invisible transitions.
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