AI governance is the set of policies, roles, processes and controls an organization uses to decide which AI tools and uses it allows, how they are approved and monitored, and who is accountable for the results. It covers both AI the company builds and AI it buys, including AI features inside existing software. In a mid-market company it can be as simple as an acceptable-use policy, an approved tool list and a named owner, growing as AI use grows.
At a glance
- AI governance answers who decides, what is allowed, how it is checked and who is accountable for AI use.
- It typically covers acceptable use, data handling, vendor review, risk assessment, output review, monitoring and incident handling.
- It applies to purchased AI features and staff use of public tools, not just models you build.
- Legal requirements vary by country, state and use case; voluntary frameworks give a starting structure.
- It works best folded into existing risk, security and compliance processes rather than run as a separate program.
What problem it solves
AI tends to arrive in an organization from many directions at once: staff using public tools, vendors switching on AI features, teams buying AI apps, and leadership asking for an AI strategy. Without governance, nobody knows what is in use, what data it touches, whether the output is reliable or who answers for problems. That is how shadow AI spreads, and how a customer questionnaire or board question about AI risk becomes hard to answer.
AI also brings risks that ordinary software review may not catch: outputs that are confidently wrong (AI hallucination), bias in decisions about people, manipulation through prompt injection, and software that takes actions on its own (agentic AI). Governance gives the business a consistent way to say yes to useful AI while managing those risks.
How it works
A practical AI governance program usually includes:
- Ownership. An accountable executive and a small cross-functional group covering IT, security, legal or compliance and the business.
- Policy. Acceptable-use rules for staff, including which tools are approved and what data may be entered into them.
- Inventory. A list of AI tools, features and models in use, who owns each and what data they process.
- Risk assessment. A tiered review for new AI uses: light for low-risk productivity tools, deeper for uses that affect customers, employees, money or regulated data. Many organizations extend existing risk assessments rather than create a new process.
- Vendor review. Contract and security checks on AI providers: data use for training, retention, location, subprocessors and liability.
- Controls and monitoring. Access controls, logging, human review of high-impact outputs and periodic checks on accuracy and bias.
- Incident handling. A way to report and respond when an AI system causes harm or behaves unexpectedly.
Many organizations start from a set of responsible AI principles, such as fairness, transparency and accountability, and use governance to check that each AI use follows them.
Frameworks such as the voluntary NIST AI Risk Management Framework and ISO/IEC 42001 offer structure. In the EU, the AI Act has been adopted with obligations applying in stages depending on how AI is used; elsewhere, rules vary by country and state, and many existing privacy and consumer laws already apply.
When it matters for buyers
- When the board or investors ask about AI. Governance is how you show AI is being adopted deliberately.
- When customers ask. Security questionnaires and contracts increasingly include AI questions about data use and oversight.
- When rolling out an AI assistant or agents. Approval, data rules and logging should be set before broad access.
- When regulations or deadlines apply to your sector or region. Check with counsel which rules apply and from when.
- When choosing tooling. Governance, risk and compliance (GRC) platforms increasingly include AI inventories and assessments; see our governance, risk and compliance overview.
Questions to ask vendors
- Do you use our data, prompts or outputs to train or improve your models, and can we turn that off by contract?
- Where is our data processed and stored, and for how long?
- Which third-party AI models or subprocessors do you rely on, and will you notify us of changes?
- What logs and admin controls do we get to monitor use?
- How do you test for accuracy, bias and security issues such as prompt injection?
- Which AI frameworks or standards do you align with, and can you share evidence?
- What liability and indemnity do you offer for AI outputs?
How it differs from data governance
Data governance manages the data itself: who owns it, how good it is, who can access it and how long it is kept. AI governance manages how AI systems use data and what they do with it: which uses are allowed, how outputs are checked, how risk is assessed and who is accountable for decisions AI influences. The two depend on each other. AI built on poorly governed data inherits its problems, which is why AI readiness assessments often start with data governance.