Ownership
Accountability and ownership
Identify the people who approve, operate, challenge and monitor AI systems.
Explore →AI governance
AI governance built into delivery
Governance is not a document at the end of an AI project. It is a set of decisions, owners, controls and evidence embedded across the AI lifecycle.
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Core governance capabilities
Risk
Risk management
Assess foreseeable harms, security, privacy, performance and operational risks.
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Controls
Lifecycle controls
Map decision gates and evidence from discovery through retirement.
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Oversight
Human oversight
Define when humans review, intervene, override or escalate AI decisions.
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Operations
Monitoring and incidents
Establish metrics, incident handling, change control and reassessment.
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Evidence
Assurance evidence
Maintain traceable records of assessments, approvals, tests and exceptions.
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END-TO-END AI ASSURANCE
Governance across
Governance across
the AI lifecycle.
Governance isn't a final checkpoint. It moves with the system — from the first business decision to ongoing monitoring and retirement.
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01 / DISCOVER
Discovery
Business purpose, intended users, risk classification and accountable sponsor.
Purpose & ownership -
02 / DESIGN
Design
Data, model, vendor, security and oversight requirements.
Requirements & controls -
03 / VALIDATE
Build & test
Evaluation criteria, test evidence, failure scenarios and remediation.
Testing & evidence -
04 / RELEASE
Deploy
Approvals, monitoring, incident response and operating ownership.
Approval & accountability -
05 / OPERATE
Monitor & retire
Drift, change, complaints, periodic review and decommissioning.
Ongoing assurance
Continuous governance & evidenceRisk · Human oversight · Audit trail · Change control
EVERY STAGE