Enterprise / Security policies & governance
Turn policy intent into operating responsibilities.
Clear security expectations need owners, practical rules and review. Start with organization-wide governance, then address the additional decisions AI introduces.
General security governance
General security governance.
Security governance establishes who makes decisions, what information needs protection and how expectations become responsibilities. It applies whether or not your organization uses AI.
Policy work should reflect the business systems, people and constraints you actually have. A document is useful when someone can apply it and knows when to ask for approval.
Questions it should answer
- Who owns access, information and security decisions?
- What rules govern sharing, account changes and suppliers?
- Who approves exceptions, and when are they reviewed?
- What happens when systems or responsibilities change?
- Policies and information handling
- Review or develop selected security guidance, acceptable-use rules and information-handling expectations with the organization’s owners.
- Access and third parties
- Clarify who approves access, reviews supplier responsibilities and withdraws permissions when needs change.
- Accountability and review
- Document decision owners, exception responsibilities and review practices. Align guidance with practical implementation dependencies.
AI security governance
AI security governance.
AI introduces questions about what systems can read, where information is processed and what connected tools can do. AI security governance makes those decisions explicit rather than assuming existing acceptable-use wording covers every path.
Build on general security governance. Retain the organization’s information, access and supplier responsibilities while defining additional rules for AI-enabled work.
Define the permitted use
- Approved AI purposes, applications and owners.
- Sensitive-data and provider-processing boundaries.
- Connected actions and human approval points.
- Exceptions and review when tools change.
- Use and ownership
- Agree who approves a use case and who is accountable for information access, outputs and changes.
- Providers and integrations
- Identify allowed processing destinations and dependencies. Clarify what needs approval before connecting a source or enabling an action.
- Review over time
- Plan separately agreed periodic reviews as providers, policies and workflows evolve. This is not continuous monitoring.
Policy development and control evaluation
Writing the rule and checking the control are different work.
Define or improve expectations
Governance work can produce agreed draft guidance, named responsibilities and open implementation decisions. Your organization approves and adopts its policies.
Evaluate what systems allow
AI policy & control evaluation compares selected expectations with permissions, configurations and authorized behavior. It is separately scoped, not automatically included in policy development.
Explore AI policy & control evaluation →Relevant NIST, OWASP or CMMC context may inform discussion. Awareness is not accreditation, government authorization or a claim of compliance achievement.
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Continue the conversation.
AI policy & control evaluation
Compare AI policy expectations with agreed permissions, configurations, and authorized behavior tests to identify enforcement gaps and practical improvements.
Explore priorityAdopt AI securely
Plan AI adoption around data access, application behavior, accountable ownership, and testing boundaries before expanding use.
Explore serviceSecurity roadmapping & advisory
Organize security and technology improvements around dependencies, decision criteria, architecture considerations, and agreed advisory scope.
ExploreDiscuss your needs
A useful next step starts with your environment.
Discuss the decision, systems, and boundaries that matter. We can establish what work is appropriate before making commitments.