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1. Scope and Inventory
Know what AI systems you have, where they operate, and what decisions they influence. You cannot govern what you have not catalogued. This includes third-party AI tools, not just internally built systems.

2. Risk Classification
Tier your AI systems by risk level based on use case, data sensitivity, and potential harm. The EU AI Act tiering logic (unacceptable, high, limited, minimal) is a useful starting structure, but organizations should layer in their own sector-specific criteria.

3. Roles and Accountability
Assign clear ownership. Who approves an AI system for deployment? Who monitors it post-launch? Who is responsible when something goes wrong? Without named accountability, governance frameworks stall at the policy level and never reach practice.

4. Data Governance
AI systems are only as trustworthy as the data behind them. This component covers how data is collected, labeled, stored, and used in AI training and inference. It addresses data quality standards, lineage tracking, consent and privacy requirements, and controls to prevent bias from entering the model at the data level. Without this, risk classification and policies operate on an unstable foundation.

5. Policies and Controls
Documented standards covering data governance, model transparency, bias testing, human oversight requirements, and vendor due diligence. 

6. Lifecycle Management
Governance does not end at deployment. The framework must cover ongoing monitoring, performance drift, incident response, and decommissioning. This is where most organizations have the largest gaps.

7. Training and Culture
Frameworks fail without human understanding behind them. Governance has to be embedded in how teams make decisions day to day, not treated as a compliance checkbox.

The best frameworks are also regulatory-ready by design, meaning they produce documentation and audit trails as a natural byproduct of normal operations rather than requiring a scramble before an audit.