01 / The guide
What's inside
- AI inventory and use-case classification
- AI risk assessment and control mapping
- NIST AI RMF and ISO/IEC 42001 alignment
- Generative AI, security, and data risks
- Human oversight and model governance
- Third-party AI and regulatory considerations
02 / The context
Why this guide matters
AI governance connects business purpose to risk, controls, accountability, and evidence. A lifecycle model gives teams a repeatable way to inventory, classify, assess, approve, deploy, monitor, and improve AI use cases.
03 / The practical view
Key insights
The NIST AI Risk Management Framework organizes risk-management guidance around Govern, Map, Measure, and Manage.
Source: NIST AI Risk Management FrameworkRegulatory obligations vary by jurisdiction, use case, and risk category; governance decisions need traceable context.
Assess third-party AI services for data, security, model behavior, contractual responsibility, and operational dependency.
04 / Takeaways
What you'll learn
- AI inventory and classification
- Risk assessment and control mapping
- Data governance and privacy
- Model governance and human oversight
- AI security and third-party risk
- AI monitoring, assurance, and incident management
- Governance operating model
05 / A connected system
Architecture at a glance
- 01Inventory
- 02Classify
- 03Assess
- 04Approve
- 05Deploy
- 06Operate and evaluate
- 07Improve
Governance and controls span the AI lifecycle, with decisions and evidence carried from discovery through operation and improvement.
06 / A phased approach
Implementation roadmap
- Weeks 1-4AI inventory and use-case discovery
- Weeks 5-8Risk classification and governance model
- Weeks 9-12Control framework and approval process
- Weeks 13-16Monitoring, assurance, and third-party controls
- Weeks 17-20Governance optimization and evidence management
07 / For decision makers
Who should read this
- CIOs, CTOs, and CISOs
- Chief risk and compliance leaders
- AI and data leaders
- Enterprise architects and technology governance leaders
08 / Aidant IT relevance
Move from guidance to a plan
Aidant IT can support AI use-case discovery, governance and security architecture, control mapping, and implementation planning across data, cloud, and enterprise technology teams.
09 / Common questions
FAQs
Does AI governance apply only to regulated industries?
No. Inventory, risk, security, accountability, and monitoring are relevant to any enterprise deploying material AI use cases.
Should every AI use case follow the same process?
No. Controls should be proportional to risk, business impact, data sensitivity, and applicable obligations.
What should be established first?
Create an AI inventory, define ownership, classify use cases, and establish a repeatable risk and approval process.