Get a free 14-day trial of DataBee® Continuous Controls Monitoring (CCM)
Join the Waitlist

Last reviewed and updated | October 20, 2026

AI governance

The policies, processes, controls, and oversight used to manage how AI systems are developed, deployed, monitored, and used within an organization.

Glossary

Compliance
Control
Continuous controls monitoring (CCM)
Control effectiveness
Control coverage
Control mapping
Control gap
Finding
Traceability
Continuous assurance
Point-in-time audit
Compliance drift
Governance, risk and compliance (GRC)
Compliance
Cybersecurity
Frontier AI
AI-accelerated threat
Exposure management
Continuous threat exposure management (CTEM)
CVSS
Exposure scoring with business context
Asset criticality
Application owner attribution
Remediation validation
Zero-day vulnerability
AI governance
Risk prioritization
Exposure
CVE
Data Management
Entity resolution
Data lineage
Identity and Access
User access review (UAR)
Access certification
Entitlement
Identity governance and administration (IGA)
Disconnected application
Mainframe access review
Reviewer rubber-stamping
Joiner, mover, leaver
Access removal time
Non-human identity
Security Data Fabric
Security data fabric
Open Cybersecurity Schema Framework (OCSF)
Data federation

Why it matters

  • As AI becomes embedded in business processes, organizations need a way to manage risk, accountability, compliance, and trust.
  • AI governance helps ensure AI systems are used responsibly and aligned with business, legal, and ethical requirements.

How it works

AI governance helps security teams understand where AI is being used, what data it can access, who is accountable for its operation, and whether appropriate controls are in place. This reduces the risk of unauthorized access, data exposure, compliance violations, and unsafe or unapproved AI use.