What is AI governance?
AI governance is the system of policies, responsibilities, processes, and controls an organization uses to manage how artificial intelligence is developed, acquired, deployed, monitored, and retired. For CISOs, GRC leaders, security teams, and risk managers, it provides a practical way to maintain visibility into AI use, assign accountability, manage security and third-party risk, and give leadership confidence that AI adoption is not outpacing the organization’s ability to govern that risk.
Effective AI governance helps answer the questions security and risk teams are already being asked: Which AI systems are in use? What data can they access? Who owns them? Which systems create the most risk? Which vendors and dependencies support them? And can the organization detect and respond quickly if that risk changes?
A mature approach usually addresses:
- AI policies and acceptable use
- AI system and vendor inventories
- Roles and accountability
- Risk classification
- Security and privacy controls
- Third-party AI risk
- Testing and human oversight
- Incident response
- Continuous monitoring
- Regulatory and standards alignment
Why is AI governance important?
Organizations increasingly use AI across software development, security operations, analytics, customer interactions, and business decision-making. That can improve efficiency and productivity, but it can also create new data flows, dependencies, privileges, attack paths, and third-party risks.
For security and risk leaders, the concern is not simply whether the organization uses AI. The more important question is whether teams can see, assess, and control the risks created by that use.
Strong AI governance can help organizations:
- Assign accountability for AI systems and outcomes
- Identify AI tools, models, vendors, and dependencies
- Apply stronger controls to higher-risk use cases
- Protect sensitive and regulated information
- Integrate AI into cybersecurity and incident response processes
- Support audits and regulatory requirements
- Reduce inconsistent or unauthorized AI use
- Give executives and boards clearer evidence of how AI risk is being managed
These outcomes matter because weak governance can create operational problems quickly. Security teams may struggle to investigate incidents involving unknown AI systems, GRC teams may lack evidence for audits, and leadership may have little visibility into whether AI adoption is increasing enterprise risk.
In Bitsight's State of Cyber Risk and Exposure 2025 research, 39% of respondents identified the explosion of AI as the biggest factor making cyber risk harder to manage.
What is third-party AI governance?
Third-party AI governance extends an organization's governance program to AI systems, models, services, infrastructure, and data processing provided by external vendors and partners.
This is especially important for TPRM and GRC teams because an AI application may depend on a foundation model provider, cloud service, API, open-source component, agent tool, Model Context Protocol (MCP) server, or downstream provider that the organization does not directly control.
Third-party AI governance should include:
- Identifying vendors that provide or use AI
- Assessing what organizational data AI systems can access
- Reviewing security controls and assurance documentation
- Evaluating fourth-party and technology dependencies
- Defining contractual requirements for data use, security, and incident notification
- Monitoring vendor security posture after onboarding
- Reassessing high-risk providers when services or threat conditions change
For teams already managing vendor risk, the key takeaway is straightforward: AI should become part of the existing third-party risk lifecycle rather than a separate review performed once at procurement.
Bitsight's TPRM approach similarly emphasizes combining assessments with objective security data and continuous monitoring throughout the vendor lifecycle.
What are AI governance best practices for enterprises?
The most effective AI governance best practices connect governance to the processes security and risk teams already use. Organizations can build on existing cybersecurity, privacy, compliance, procurement, data governance, and enterprise risk programs rather than creating a completely separate operating model.
For most enterprises, the highest-value starting point is visibility and accountability. Teams should first determine where AI is being used, who owns each system, and which use cases create the most risk.
Key practices include:
- Create an AI inventory: Identify internally developed systems, commercial AI tools, embedded AI features, models, agents, APIs, and third-party dependencies. This gives security and GRC teams a baseline for determining what needs oversight.
- Classify AI by risk: Consider data sensitivity, business criticality, autonomy, regulatory impact, external exposure, and potential consequences. This helps teams focus effort where a failure or compromise would matter most.
- Define ownership: Assign accountable business and technical owners and clarify the roles of cybersecurity, privacy, legal, compliance, GRC, and procurement.
- Govern AI throughout its lifecycle: Review systems before deployment and reassess them when models, data, integrations, use cases, vulnerabilities, or regulatory requirements change.
- Apply security and privacy by design: Use least privilege, access controls, encryption, vulnerability management, monitoring, logging, and appropriate data protections.
- Require appropriate testing: Test systems for reliability, security, privacy, misuse, and other risks relevant to the use case.
- Maintain human oversight: Define where people must review, override, suspend, or retire AI systems.
- Document material decisions: Keep records of approvals, testing, risk assessments, exceptions, incidents, and major changes.
- Continuously govern third parties: Assess vendors before onboarding and monitor relevant security conditions afterward.
The practical goal is to reduce uncertainty. Security and risk teams should be able to explain which AI systems matter most, why they matter, and what controls are in place.
What should be included in an AI governance checklist?
An AI governance checklist gives teams a repeatable way to evaluate AI systems and document decisions.
For each AI system, security and risk teams should be able to answer:
Inventory and ownership
- What AI system or service is being used?
- What business purpose does it serve?
- Who owns it?
- Which systems and vendors support it?
Risk
- What decisions can it influence or make?
- What happens if it fails, is manipulated, or produces incorrect or harmful output?
- How autonomous is it?
- Does it interact with critical systems or users?
Data
- What data does it process?
- Does it handle confidential, personal, or regulated information?
- Is organizational data retained, shared with subprocessors, or used for training?
Security
- How is access controlled?
- What systems, APIs, or tools can it reach and what actions can it take?
- How are vulnerabilities monitored and remediated?
- Are user and administrative actions logged?
Third parties
- Which vendors and subcontractors support the service?
- What security evidence has been reviewed?
- How will incidents or material changes be reported?
Oversight and compliance
- Where is human review required?
- Who can suspend the system?
- Which laws, policies, and standards apply?
- When will the system be reassessed?
For GRC teams, this checklist can become part of a control review. For TPRM teams, it can inform vendor assessments. For security teams, it can help identify access, exposure, monitoring, and incident response requirements.
