Use-case approval
Decide whether a need may use AI and which conditions precede a pilot or deployment.
What is AI governance? Principles, roles, and controls
AI governance is the set of responsibilities, policies, processes, and controls that guide the selection, design, use, and monitoring of artificial intelligence systems. It connects business objectives with risk, individual rights, security, data quality, and verifiable accountability across the system life cycle.
In one sentence
AI governance determines who may use which system, for what purpose, with which data, controls, and accountability.
Key points
- Governance covers purchased tools, embedded models, and informal use, not only internally developed projects.
- Controls should vary with the impact of an error and the exposure of affected people.
- An inventory of uses, owners, data, and vendors is the starting point.
- Continuous monitoring matters because models, data, prompts, and operating context change.
Term at a glance
- AI governance
- Artificial intelligence governance · AI management framework · responsible AI governance
- English term
- Artificial intelligence governance
- Domain
- AI risk management
- Category
- Organizational governance
- Level
- Intermediate to advanced
What does AI governance cover?
Governance turns general principles into operating decisions: permitted use cases, prohibited data, prior assessment, human validation, incident handling, and stop criteria. It assigns roles across leadership, business units, IT, security, legal, privacy, and vendors.
The control level should match risk. A tool that summarizes internal notes does not carry the same consequence as a system influencing access to employment, credit, or service. Classification considers severity, likelihood, reversibility, affected population, and a person’s practical ability to challenge the result.
Documentation should reconstruct a decision: system version, purpose, data sources, assessments, known limits, accountable owner, and remedy. Documentation does not replace testing. Performance, bias, security, privacy, and robustness must be evaluated in the intended context and monitored after release.
How do you establish AI governance?
- 01
Inventory AI uses
Record tools, vendors, models, data, users, owners, and supported decisions, including experiments.
- 02
Classify risk
Assess impact, likelihood, affected people, sensitive data, autonomy, and ability to correct an outcome.
- 03
Assign controls and decisions
Define approval, testing, human oversight, logging, security, contracts, remedy, and retirement criteria.
- 04
Monitor the life cycle
Reassess after changes to model, data, vendor, context, performance, or legal requirements.
Concrete example
A company permits an assistant to summarize internal documents but prohibits customer data in public accounts. The use case has an owner, approved sources, answer tests, a visible warning, and an incident process. A candidate-screening project receives a separate review and stronger controls because its decisions can materially affect people.
Governance applications
Vendor management
Assess data, security, subprocessors, location, model changes, and audit options.
Model controls
Define tests, thresholds, monitoring, human intervention, and retirement when results degrade.
Employee use
Provide practical rules on permitted tools, shareable information, and output verification.
Benefits and limitations
- Explicit ownership and decisions.
- Risk addressed before broad release.
- Safer experimentation under known rules.
- Stronger evidence of diligence for customers and partners.
- Bureaucracy if every use receives the same control.
- False confidence when policies are not tested.
- A quickly stale inventory without a continuous process.
- Cross-functional skill requirements across business, IT, security, and law.
Organizational value
Governance should not slow every innovation equally. It creates a fast path for low-risk use and stronger review for consequential decisions. Metrics include inventory coverage, completed assessments, incidents, approval lead time, and control compliance. The linked service page describes Daillac’s support; this glossary page explains the general framework.
Frequently asked questions
Who is responsible for AI governance?
Leadership is accountable, but delivery is shared. Business owners define purpose and impact; IT and security control architecture, data, and protection; legal and privacy functions support obligations. Every system still needs one named owner.
Is an employee ChatGPT policy enough?
No. It can guide use, but governance also includes inventory, risk assessment, vendors, testing, monitoring, incidents, and retirement decisions. AI embedded in processes needs technical and operating controls.
How can a small business start?
Inventory existing use, stop clearly unsafe practices, name an owner, and adopt a simple impact scale. Add proportionate requirements rather than copying a complex framework the organization cannot operate.
Related terms
Sources and references
Want to govern AI without blocking useful adoption? We can build an inventory, risk scale, and practical controls.
Structure AI governance