Dainin Reference
How should enterprise AI be governed?
Enterprise AI governance establishes responsibility, policies, controls and review across the use of AI. It includes the data and models involved, the people responsible, the decisions delegated and the evidence retained—not simply a safety label on a chatbot.
Connect the policy to the use case.
A useful governance discussion begins with what the system is being asked to do, who is affected and where errors or inappropriate actions could matter. The controls should match that scope.
The NIST AI Risk Management Framework provides a voluntary framework for managing AI-related risks. It is not a certification of a particular Dainin deployment.
Technical controls and business authority.
Identity, access restrictions, model evaluation and logging are important. They do not replace the question of who may make a consequential business commitment.
Conversely, delegated authority does not replace secure implementation. The organisational mandate and technical enforcement need to work together.
Make exceptions and change visible.
Policies evolve, data becomes stale and unanticipated situations arise. Define review, escalation, correction and retention responsibilities before assuming the system will resolve them automatically.
A record should distinguish a proposed action, an authorised action, actual execution and confirmed outcome.
How Dainin applies the concept.
Decision Authority, Authority Lock, Organisational Truth and Decision Ledger explain important governance relationships inside CEOS. Detailed security and data commitments remain in the existing Trust Center and applicable agreements.
A website simulation or reference article is not a security audit or guarantee of regulatory compliance.
Sources and further reading
Bring one business question.
Explore the relevant Dainin workflow and the decisions behind it.
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