AI Governance : an AI framework to ensure Compliance and Risk control

03/03/2025

What is AI governance ?

Proper governance of artificial intelligence (AI) ensures effective management and oversight of AI systems, while guaranteeing their compliance and controlling the associated AI risks. This AI framework is based on several fundamental principles:

Transparency: Ensuring that the data used is secure, reliable and from ethical sources. The origin and use of the data must be clearly documented, enabling all stakeholders to understand how the data is used and how decisions are made.
Security: Implementing protective measures such as data encryption and evidence management in case of loss, to ensure optimal AI risk management.
AI compliance: Comply with current laws and regulations such as the EU AI Act, while being able to anticipate and react to rapid developments in the field of AI.
Responsibility: Clearly define roles within the organization, assigning responsibility for AI systems and information processing to specific parties.
Ethics: Establish a framework of ethical rules to protect against bias, respect privacy and conduct regular audits, thus ensuring responsible use of AI.

Implementing AI governance also involves training teams, establishing appropriate methodologies and using effective tools to ensure optimal management of AI systems. By adopting these best practices, organizations can improve the quality and security of their AI systems, while optimizing their potential and ensuring their compliance with AI.

Why implement AI governance?

The objective of AI governance in organizations is to manage and reduce these risks by establishing rules and standards throughout the AI lifecycle, from design to deployment and operation. It also aims to guarantee responsible and trustworthy AI that respects fundamental rights and ethical principles.

Thanks to a pragmatic and structured approach, AI governance enables organizations to:

Identify and mitigate the risks inherent in AI.
Define strategic use cases for the company.
Manage the AI products and their evolution over time.
Ensure compliance with current regulations and standards (AI Act, ISO 42 001 standard…)
Measure the impact of AI on performance, the environment and its internal adoption.

Who are the stakeholders of this governance?

LaThe implementation of AI governance relies on close collaboration between several teams within the organization:

Data teams: development and management of AI models.
IT teams: integration and deployment of AI systems.
Business teams: identification of relevant use cases.
Cyber teams: management of cybersecurity risks.
Legal & Compliance teams: compliance with regulations and data protection.

This approach must be driven top down by the Leadership Team of the organization, who must recognize AI as a strategic matter and make governance a corporate priority.

What are the benefits of effective governance?

Well-defined governance allows you to:

✅ Accelerate the deployment of AI projects while managing risks.

✅ Comply with regulatory requirements and avoid sanctions.

✅ Ensure transparency and documentation of algorithmic decisions.

✅ Evaluate and adjust algorithms continuously to detect and correct biases and hallucinations.

How can the effectiveness of AI governance be measured?

AI governance monitoring is based on key performance indicators (KPIs), such as:

📊 Bias detection and resolution score

📊 Score of compliance with standards and regulations

📊 Environmental impact of AI systems

📊 Rate of adoption of AI solutions internally

How to guarantee compliance and risk control : the AI governance framework

1. Identify priority projects according to the regulatory calendar

One of the first levers is to align actions with legal obligations and regulatory directives.

A concrete example is the management of prohibited AI:

February 2: Entry into force of the ban on AI with unacceptable risk.
February 4: Publication of the Commission’s guidelines on prohibited practices.
Action: suspend non-compliant AI systems or bring them into compliance.

2. Set up a pragmatic approach

An effective framework is based on concrete actions involving stakeholders and internal processes:

Identify the actors involved in AI systems and assign clear responsibilities.
Raise awareness and train the teams in regulatory requirements and upcoming milestones.
Map the AI systems in use (purchased, customized or developed in-house).
Qualify the risks of AI systems under control.
Manage the suppliers by integrating appropriate contractual clauses and raising awareness among the purchasing teams.

3. Framing the AI strategy

AI governance must be part of an overall strategic vision:

Formalize a trustworthy AI policy aligned with strategic objectives.
Define the critical issues and objectives (environmental impact, transparency, fairness, etc.).
Structure an AI governance framework (post-market surveillance, incident management).
Set up an appropriate organization (task force, factory, etc.).
Determine the tools for testing and quality assurance of AI systems.

4. Implement and manage actions

Finally, execution and monitoring must be based on clear and measurable processes:

Prioritize high-risk AI products.
Identify gaps between existing systems and regulatory requirements.
Plan and execute documentation, testing and transparency operations applied to AI systems.
Train and acculturate our teams through standardized practices.
Continuously monitor and evaluate practices throughout the life cycle of AI systems.

Operationalization of governance and privacy for our customers

We are now deploying this AI governance and compliance model for our clients, relying on tools and processes tailored to their specific challenges.

With Naaia, we support organizations in the implementation of robust and effective governance to guarantee compliance and risk management of AI systems.

Need effective AI governance?

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