AI Governance Operating Model & Evidence Baseline
Make artificial-intelligence governance work around real systems.
DDAI turns governance intentions into named ownership, operating processes, review points, and evidence connected to the organisation’s AI systems.
The problem
Policies do not govern a system by themselves.
Organisations may have principles and acceptable-use guidance but still lack a reliable system inventory, accountable owners, supplier review, human-oversight design, incident handling, evidence retention, and reassessment. The result is governance that cannot be reconstructed when a buyer, board, or assurance team asks what actually happened.
Workstreams
System and use-case inventory
Roles, accountability, and decision rights
Policy and acceptable-use structure
Risk and control model
Human-oversight and escalation design
Supplier, model, and service review
Incident, exception, and remediation process
Evidence-retention and disclosure approach
Training and competence records
Monitoring, change, and reassessment
Board and assurance reporting
Initial AI Evidence Passport structure
Deliverables
Governed system register
Responsibility and approval matrix
AI policy set
Risk and control matrix
Human-oversight model
Supplier-assurance process
Incident and escalation workflow
Evidence model and review cadence
Ninety-day implementation backlog
Leadership decision report
Evidary-ready evidence baseline
Standards and regulation
DDAI can organise evidence against relevant public guidance, procurement criteria, and licensed or permitted standards mappings. It does not determine certification, conformity, or legal sufficiency.
Evidary route
Organisations that need the record to remain current across several systems can move into Governance Workspace or Assurance Programme.
Move governance from a document set into an operating system.
DDAI can help you connect business value, technical implementation, human oversight, and governance evidence from day one.
Build the operating model