Artificial intelligence is making its way into every aspect of our economy, drawing on its ability to handle immense volumes of data. Data collection and analysis—which, until very recently, required significant resources—have become streamlined, automated processes, with results available with unprecedented agility.
Data management thus becomes a powerful strategic tool for businesses. By optimizing their operational processes and resources, they can speed up decision-making and are better equipped to anticipate market trends.
However, this cannot be done without assistance, and while it may be tempting to entrust your databases to professionals who seem to have a firm grasp on these new technologies, caution is advised to ensure data protection.
The data generated and managed by companies across all industries is diverse and comes from a variety of sources. Whether quantitative (sales, financial statements, etc.) or qualitative (consumer reviews, engagement survey results, etc.), it should all be handled with the utmost care—particularly when it comes to employees’ personal data (contact information, compensation, hiring, disciplinary records, etc.) and customer data (contracts, billing, nature of engagements, information about their activities, etc.).
This requires a thorough understanding of the nature of the data and the ability to segment it, since not all data is strategic or confidential. Unfortunately, companies often neglect to take the necessary steps to ensure sound data governance, even though it is essential for the quality and integrity of their data.
Faced with the sheer volume of data they hold, some companies—particularly those with limited resources—are tempted to turn to external service providers who appear to be experienced.
As a result, there are now a growing number of independent providers whose data management services rely on AI tools accessible to everyone, such as generative AI models, which are, however, not very secure. Under the pretext that these tools allow companies to take inventory of their data quickly and at very competitive prices, this often-unstructured approach proves to be risky.
Entrusting a third party with cleaning your data to facilitate its analysis, use, or migration—for example, as part of a digital transformation project—without taking precautionary measures first is equivalent to granting full access to your company’s and your customers’ critical information.
In the event of data loss, a data breach, or data theft, the legal consequences can be severe, as can the damage to the affected company’s reputation.
In this context, it would be advisable to incorporate data governance from the outset of pilot projects. Embarking on a new project while ensuring that the data used is properly classified and inventoried will help ensure that existing access control mechanisms are followed.
A pilot project also makes it easier to establish, from the outset, the audit and traceability rules governing data use and, as a result, to better anticipate and detect potential information leaks.
Let’s consider a company’s salary data. If an employee were to ask AI what the CEO of their organization earns, that information should not be provided to them if they do not have the necessary permission to access it. This is the type of information that would be identified as critical during a data inventory process and for which specific access rules would need to be established.
Taking advantage of a pilot project to establish a data governance framework is an excellent starting point for building or updating the organization’s data registry and incorporating this approach into its best practices.
The point here is not to dismiss the potential benefits of artificial intelligence tools. However, it is important to meet certain minimum requirements.
Best practices include:
· Establish a data registry. A clear and rigorous structure will make it possible to identify critical data and determine where and how it is stored;
· Establish access rules for different categories of information based on each employee’s role within the company;
· Implement a process for regularly updating data to ensure its quality and integrity;
· Conduct a risk assessment and define a risk management plan in the event of an incident (cyberattack, data breach, etc.).
Although it is not always necessary to establish a comprehensive governance framework, it is recommended to comply with a minimum set of rules, such as those defined by Law 25 in Quebec. These rules serve as the foundation for an approach aimed at protecting data—both yours and your customers’.
It is also best to handle data governance internally. If you outsource to specialists, make sure their technological infrastructure is robust enough to protect the data you trust them with and that they also comply with a specific regulatory framework.
As artificial intelligence becomes more powerful, security breaches are on the rise, as are self-proclaimed experts… Taking the time to think this through and put in place the essential safeguards for data protection will prove worthwhile in the long run.
