Who This Guide Is For
This guide is designed for CISOs, CTOs, and compliance leaders who need to establish AI governance but don't know where to start. We walk through a practical, phased approach that balances thoroughness with speed to value.
Step 1: Inventory Your AI Assets
You cannot govern what you cannot see. Begin by cataloging every AI model, algorithm, and automated decision system in your organization. Include third-party AI embedded in SaaS platforms.
For each asset, document: purpose, data inputs, decision outputs, risk classification, and responsible owner.
Step 2: Define Risk Categories
Not all AI systems carry the same risk. The EU AI Act provides a useful framework: unacceptable risk, high risk, limited risk, and minimal risk. Map your inventory to these categories.
High-risk systems (those affecting employment, creditworthiness, healthcare, or safety) require the most rigorous governance controls.
Step 3: Establish Policies
Create written policies covering: acceptable use of AI, model development standards, testing and validation requirements, monitoring obligations, and incident response procedures.
Step 4: Implement Technical Controls
- Automated bias testing in CI/CD pipelines
- Model performance monitoring dashboards
- Data lineage tracking from source to model input
- Explainability reports for high-risk decisions
Step 5: Train Your People
Governance frameworks fail without organizational buy-in. Conduct role-specific training for data scientists, engineers, product managers, and executive leadership.
The SamurAI offers facilitated workshops that accelerate this process, typically reducing framework implementation time from 12 months to 4.
