AI governance: best practices

Artificial intelligence (AI) has been deployed in virtually every sector of activity and has seen exponential growth in recent years. And while a new regulatory framework is beginning to take shape (with the forthcoming implementation of the AI Act, in particular), organizations already need to take measures. To exploit the full potential of this new technology responsibly and securely, it is imperative to establish and implement genuine AI governance.

So how do you manage and control the deployment of artificial intelligence within your business? What are the best practices for AI governance? And how can they be put in place?


AI governance, to manage and reduce the risks of artificial intelligence


From virtual assistants to diseases detection systems, from predictive analysis to identifying patterns of financial fraud… AI systems (AIS) are revolutionizing all sectors of activity. They promise revolutionary scientific advances, and offer major development opportunities for businesses. But using this dynamic technology is not without risks…

Artificial intelligence systems are now capable of delivering content, predictions and recommendations based on learning data. Thanks to increasingly powerful learning models (machine learning and deep learning), they can adapt quickly and become increasingly autonomous. But if this data is biased or erroneous, so are the results! And this can have serious consequences for the organizations that use them.

Distorted decision-making based on the wrong information, dissemination of sensitive data, unauthozised use of personal information and security breaches… Unless they put in place a strict framework and rules of good conduct, the use of AI can expose businesses to serious legal, reputational and financial risks. This is why guidelines and rules of good practice are beginning to emerge.

Critical today, the implementation of AI governance within the organization aims to improve the quality and security of the systems deployed. As well as optimizing the potential of AI systems, it enables risk management to be steered and regulatory compliance to be ensured.


How can AI governance be established and implemented?

As an overall management and monitoring framework, AI governance requires the implementation of several concrete actions: the definition of a strategy, responsibilities and main principles, the implementation of methodologies and training, and the use of effective tools.


  1. Defining the governance framework and key principles

To guarantee the effective and secure deployment of AI systems within the company, AI governance must be based on several fundamental principles and concepts:


  • Transparency: the organization must be able to rely on secure and reliable data from ethical sources. Data must be recorded, and its origin and use clearly documented. All stakeholders must know how the data is used and how decisions are made;
  • Security: for optimum risk management, AI governance must include the implementation of all the necessary protection measures (data encryption, impermeability of mechanisms, management of evidence in the event of loss, etc.);
  • Compliance: the company must obviously comply with the latest applicable laws and regulations. To keep pace with the rapid evolution of AI, its governance must also combine anticipation and responsiveness;
  • Responsibility: roles must be clearly defined within the company. One part of the organization must bear the responsibility for AI systems and information processing;
  • Ethics: protection against prejudice, respect for privacy, regular audits, etc. Governance must provide a framework and rules for ethical behavior, to guarantee the use of responsible AI.
  1. Train the teams to the usage of AI systems

To optimize the deployment and use of its AI systems, the organization obviously needs to be able to rely on competent, well-trained teams. This means:

  • creating a dedicated team: the company must be able to entrust the management and responsibility for AI to one (or more) teams who know the rules and good practice to be applied. This team needs to be aware of the global regulations governing AI, and what is and isn’t possible depending on the use case. It can be made up of several types of profile, each with a role defined upstream. An audit committee can also be set up to oversee data control;
  • training for other teams, at all levels of the company: everyone involved must fully understand the organization’s AI guidelines, and have the same level of information. As well as sharing a common language, some teams also need to know how to use and master the systems deployed within the structure. AI governance necessarily involves training AIS employees and users, as soon as they are set up.
  1. Deploying the right governance tools

Having defined the rules to be followed and trained its teams, the organization can now turn its attention to the practical implementation of AI governance solutions. An AIMS (“Artificial Intelligence Management System”) offers a number of innovative functionalities, enabling it to manage, govern and optimise its AI systems, while reducing its operational costs and the associated risks.

Inventory and qualification of artificial intelligence systems, collaboration of digital assets for easier access, action plan to achieve compliance, risk assessment, monitoring and management, employee training… A high-performance AIMS guarantees the use of safe and reliable artificial intelligence, via a centralized tool.

 First AIMS® on the market in Europe, Naaia is a SaaS solution for the governance and management of AI systems. With a unique end-to-end platform vision and advanced legal expertise, it enables AI systems to be organized, managed and controlled. As well as optimizing business performance, this “all-in-one” tool meets the triple imperatives of trust, performance and compliance for AI systems. Contact our teams to find out more.