Solution Capability · 1.4
Create clear roles, policies, controls, review points, and evidence so AI can be used with appropriate oversight.
AI adoption becomes risky when teams cannot answer who approved a use case, which data it can access, how outputs are reviewed, how incidents are handled, or how changes are tested before release.
AI governance councils
Risk, privacy, legal, security and compliance leaders
CIO, CTO, data and AI leaders
Product and application owners
Internal audit and operational control teams
Assess current policies, decision bodies, controls, and use cases.
Classify AI use cases by risk and consequence.
Define accountable owners, approval gates, Human in the Lead requirements, and prohibited uses.
Create standards for data, access, evaluation, release, monitoring, incidents, vendors, and evidence.
Integrate governance into delivery and AgentOps workflows.
Establish review cadence and a living governance register.
AI use-case intake and approval.
Risk classification and control mapping.
Human review and escalation design.
Model and vendor assessment.
Evaluation and release standards.
Incident, change, and evidence management.
Microsoft, Salesforce, and enterprise applications.
OpenAI, Claude, and IGNA.
Data platforms and client policy or GRC tools.
Security & Governance
This capability is the security and governance focus. Website copy uses careful language such as “designed to support governance requirements” and “supports auditability.” It does not claim blanket certification or guaranteed security.
A readiness workshop is the fastest way to find out if this is the right starting point.
