Using AI governance to secure and optimise data flows
How to limit the legal, financial and digital risks linked to automated systems in companies ? The role of governance in artificial intelligence lies at the heart of this crucial issue. Thanks to this article, you will be able to understand these regulatory tools, but also their impacts, their functionalities and the best practices in terms of data monitoring.
Definition of AI governance
Is artificial intelligence the new IT revolution? Its capabilities have led the ISO 2382-28 standard to refer to an entity capable of functions such as “reasoning and learning”. In short, actions “generally associated with human intelligence”.
Given this potential, AI governance has come to answer the question: to whom and how should control and management of data be redistributed?
These systems regulate the relationships between processes, artificial intelligences and people in order to optimise and protect data. The aim is to guarantee the sovereignty of your digital environment and to channel access to it.
The benefits of establishing effective governance for AI
By resorting to artificial intelligence, a company opens the way to possibilities, provided it is used properly. Otherwise, it is exposed to digital, financial, legal, internal and external risks.
The main interest of effective governance for AI? Ensuring the normal and efficient operation of these digital systems. With safeguards, limitations and other parameters, these administrative tools give your company the means to use AI to its full potential.
More generally, control systems make workflow and machine learning processes more efficient.
In addition, governance to regulate AI offers various advantages. The ALLONIA AI Lake solution, for example, allows for optimal monitoring of computational performance in addition to the comprehensive basic functions of governance.
The concepts of AI governance
AI governance is based on fundamental concepts :
- Compliance: Data management is kept at its optimum level, ensuring that it is continuously processed and protected. In other words, the way you use digital information is systematically compliant with regulations, thanks to governance.
- Ethics: AI decisions are not always neutral or without consequences for human rights. To avoid discrimination in the actions of these robots, governance applies different limits to them.
- Accountability: the whole organisation is involved in data management. Real governance of artificial intelligence implies that at least one part of the team bears the responsibility for processing the information.
- Transparency: data governance respects this principle by providing records of each of its actions. Any questions about why or how any information was processed should be answered.
- Security: All protective measures (e.g. encryption) are taken to ensure data integrity. This includes the procedure to follow in case of loss, and the management of evidence.
Setting up governance in AI
Setting up an AI administration involves respecting certain principles. In order to successfully implement an AI governance for your company, you will need to :
- Involve the teams from the start of the process. In order to ensure the acceptability of the new processes, you will have to educate the various employees. By popularising the key concepts related to AI for example, you will succeed in implementing this organisational change in your company.
- Use monitoring tools to assess results against objectives and means. These indicators will monitor the way teams appropriate AI and its governance, but will also serve to ensure the cohesion between the action of artificial intelligence (and its administration) and the company’s mission.
Steps to deploy governance for artificial intelligence
1. Creation of a dedicated data flow governance team
You will need project management and a team focused on the deployment of governance in AI.
This group will be composed primarily of data scientists, and more generally of profiles close to digital and business data. We are indeed talking about the deployment of machine learning systems, with the skills that this implies.
The governance team ideally includes data engineers and business analysts in charge of identifying the AI infrastructure best suited to the planned uses. Other profiles such as scrum masters, delivery leads or data scientists will implement the platform that has finally been chosen.
2. Preparation of policies and procedures for AI
In order to anticipate the expected results, AI data processing policies must be carefully designed.
On the one hand, business leaders and managers must clearly define the role of governance in artificial intelligence for their company. For what purposes will it be used and for what results?
On the other hand, the teams of engineers and specialists programme the governance of AI according to the defined objectives. These ideally include input from future users, who may formulate specific needs, past experiences, etc.
3. Setting up processes for monitoring and evaluating AI
In order to avoid the legal, practical and financial risks associated with the misuse of AI, governance must implement the principles outlined above.
By letting the rules of ethics and transparency shine through in its attributes, the engineers protect the company from a possible failure of the AI. Therefore, the rules for the use and evaluation of these digital systems must also be defined from the outset.
4. Using governance tools to make AI more effective
In practice, data governance will connect data that would normally have evolved in silos. In other words, it takes the form of a platform dedicated to the collaboration of digital systems… All with a precise method of capture and extraction.
It should also be noted that, for any use, the role of governance in artificial intelligence goes hand in hand with the respect of principles, such as data quality.
The different governance tools for AI
What tools to monitor and evaluate AI models ?
Looking for tools to evaluate artificial intelligence models? You can first refer to the listing of AI monitoring systems proposed by the CNIL.
The ALLONIA platform offers a complete governance service with a clear objective: to guarantee the sovereignty of your data space.
Through the use of safeguards, such as TrustCheck, ALLONIA allows limits and guidelines to be placed on resources and data flows.
Other AI data governance systems exist. Microsoft has deployed Azure to ensure that target individuals have access to secure and trusted digital information.
Advice on selecting the right governance tools for your needs
To acquire the most appropriate tool for your situation, you will first need to clearly identify your expectations in terms of results. What are the risks you are most exposed to in relation to AI, and what functions would you prioritise in a governance system?
You will also need to be sure of the real benefits of the tool you are considering, such as :
- Optimising data quality by securing, analysing and cleaning data flows;
- Functions for monitoring AI actions throughout the process, from integration to use of the data;
- An infrastructure geared towards the user experience (UX), making it easier to search, reliable and accessible;
- Real monitoring and history of every action, and of all data;
- Software options such as automated learning, metadata management for your company…
The role of governance in artificial intelligence in decision-making
In one case, governance quickly becomes indispensable: when decision making includes automated learning processes.
By making data flows more fluid and secure, governance enables more rational cross-functional decision-making. And for good reason: managers and executives are assured of having the same level of information.
In other words, it is governance that positively impacts the entire business strategy. Through the standards it applies to AI, governance facilitates communication, identification of needs and appropriate solutions.
The impact of data governance on decision making
How AI governance can improve decision-making: a concrete example
Some platforms are equipped with reinforcement learning systems. Using immediate data collection methods, software such as Stradigi AI can improve the efficiency of workflows and thus improve decision making.
The objective? To have all the teams responsible for or managing the company rely on a single, reliable source of data. It is better to speak the same language when faced with a strategic dilemma in a company… This is what data governance systems allow.
The challenges of AI-assisted decision making and how to overcome them
A business decision has an impact on all employees: it must therefore be understood and accepted. And if an artificial intelligence has influenced the strategy adopted, it must be explained all the more.
Do you rely on AI to inform your choices? Then you need to be educational and clear about the reasons for the decision. Ideally, if a machine is involved, the teams should at least be consulted to reinforce the acceptance of the strategy.
Human teams must be familiar with the fundamentals of artificial intelligence. Here again, to reconcile management and digital technology, the stakeholders must speak the same language.
In addition, before deploying data governance, make sure that you have assigned responsibility for it to a clear and focused group of people. This is to avoid future setbacks and ambiguities, especially in the event of litigation or financial damage.
What to remember about AI governance
As you can see, the role of data governance in artificial intelligence is both important and strategic. Over time, companies will have increasingly specialised teams capable of managing and taking responsibility for these control systems.
The secret to effective AI governance? Be clear from the start about the role and needs of each employee and identify the major options for the future system. Platforms such as ALLONIA guarantee effective functionality that meets your expectations. For more information, please contact our team.