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It has become increasingly clear that the intersection of artificial intelligence (AI) and governance is pivotal for organizations looking to leverage the power of AI while mitigating associated risks.
The rapid evolution of AI, coupled with stringent regulatory frameworks such as the EU AI Act, necessitates a structured and comprehensive approach to AI governance.
1. AI Strategy and Core Principles
Effective AI governance begins with a clearly defined strategy set by senior leadership. This top-down approach ensures that AI use aligns with the company’s broader vision, focusing on core principles such as ethical usage, trust, and compliance with regulatory standards. Legal and risk management teams are then tasked with developing policies, controls, and frameworks to operationalize this strategy.
2. Artificial Intelligence Internal Stakeholders and Committees
To execute AI governance at the tactical level, organizations must establish dedicated AI governance committees. These committees, often comprising legal, IT, compliance, data, and cybersecurity experts, should be responsible for overseeing AI-related risks. Reporting to the senior management, this body plays a crucial role in policy approvals, vendor management, and integrating AI into existing risk structures. At the moment, this solution is preferable to the appointment of a single AI Officer which might not have all the competencies to address artificial intelligence compliance.
3. Identifying Use Cases under EU Rules
A fundamental aspect of AI governance is identifying which AI use cases fall under regulatory scrutiny. Given the broad legal definitions within the EU AI Act, even seemingly benign systems might be classified as AI. Organizations must carefully assess their AI systems, especially those that cross borders, as these might still fall under the purview of EU regulations.
4. Risk Identification and Categorization
Once AI use cases are mapped, organizations must categorize them based on risk levels—whether prohibited, high risk, or general-purpose AI. A proactive approach is essential, as risks could range from reputational damage to legal exposure, particularly in contexts like HR and credit checks, which the EU AI Act may deem high risk.
5. Implementing Controls
For each identified use case, controls should be put in place to mitigate risks. These could include human oversight, bias assessments, and robust technical measures to secure systems. High-risk AI systems must comply with statutory requirements, and organizations should also focus on vendor protections through contracts to ensure compliance across the board.
Finally, given the ever-changing nature of AI and the law, governance processes must be continuously updated. Committees should stay informed of legal and technological developments, ensuring that previously approved systems remain compliant as they evolve. The organizations that invest in solid AI governance stand to gain the most from AI’s capabilities, enjoying a measurable return on investment.
For organizations looking to integrate AI into their operations, a proactive approach to governance is no longer optional—it’s essential. By understanding and implementing a strong governance framework, companies can mitigate risks and position themselves to fully benefit from the opportunities AI presents.
On the topic, you can read the October issue of our AI law journal available HERE and the presentation of our AI compliance tool available HERE.
