1. Define the Purpose and Scope of AI Governance

Clarify Objectives: Determine why AI governance is essential for your organization, focusing on legal compliance, risk management, information security and relevant considerations based on the client’s needs and use cases.

Scope Identification: Specify which AI systems and processes fall under the governance framework.

2. Map and Classify AI Systems

Inventory AI Assets: Document all AI systems in use or under consideration to understand their functions and associated risks. 

Risk Assessment: Evaluate each AI system’s potential impact concerning fairness, transparency, privacy, and compliance.

3. Design an AI Governance Framework

Framework Selection: Tailor an AI governance framework specific to the client needs and used based on the industry leading standards (e.g., NIST AI RMF, OECD AI Principles), and integrate the requirements from AI Act, Data Protection, Cyber security and relevant legislation.

Define Roles and Responsibilities: Establish clear accountability and decision-making structures for AI initiatives.

4. Engage Leadership and Align Stakeholders

Leadership Commitment: Secure support and resources from top management to champion AI governance.

Stakeholder Collaboration: Involve key internal and external parties, including legal, compliance, IT, data owners, and procurement teams, to ensure cohesive governance practices.

5. Develop AI Assessment and Procurement Processes

Assessment Criteria: Create procedures to evaluate AI systems for fairness, accountability, transparency, and privacy throughout their lifecycle.

Procurement Policies: Integrate compliance checks into procurement to address data protection and bias mitigation.

6. Foster a Culture of Responsible AI

Training Programs: Educate staff, especially data protection officers, IT teams, data scientists, and procurement professionals, on responsible AI principles.

Ethical Awareness: Promote understanding of ethical considerations and compliance obligations related to AI.

7. Implement Continuous Monitoring and Improvement

Performance Monitoring: Regularly track AI systems for compliance and alignment with governance objectives.

Framework Adaptation: Update governance structures to reflect regulatory changes, technological advancements, and organizational shifts.

Periodic Audits: Conduct audits to assess the effectiveness of AI governance and identify improvement areas.