Artificial intelligence is revolutionising your business... and threatening your sensitive data ! Client portfolio, confidential financial figures, personal data between legal risks, data leaks and the need to protect shadow AIHow can you exploit AI without losing control? Discover concrete strategies for securing your information assets in the age of AI.
Artificial intelligence is making inroads into all business sectors, promising productivity gains. By 2024, 67 % of European VSEs and SMEs were already using AI tools, a figure driven by the boom in theGenerative AI.
But this revolution comes with a critical challenge: protection of sensitive data. Whether it's customer information, confidential financial figures or employees' personal data, 31 % of companies consider data confidentiality to be the main obstacle to the adoption of AI (Baromètre TPE-PME 2024, Quonto).
The challenge is therefore considerable: to exploit AI while complying with a demanding legislative framework and securing your information assets. How can you keep control of your data in the age of AI?
The new risks associated with AI
AI introduces new risk vectors that require a new analysis grid. The threat is no longer limited to cyber attacks traditional.
Data leakage through use
This is the most immediate and insidious risk. When an employee, wanting to use AI to make their work easier, submits a customer email, an extract from a contract, financial results or a CV to a public AI such as ChatGPT or Gemini, the data leaves the company's security perimeter.
This information can be reused to train models, resulting in a loss of control over intellectual property.
Employees often the source of leaks
A study in 2023 revealed that 4.2 % employees a panel of 1.6 million people had attempted to submit confidential or regulated information to a chatbot AIThis has led to leaks of source code, customer data and sensitive documents.
Case studies include an executive who copied an internal strategic plan into ChatGPT to obtain a presentation, and a doctor who entered a patient's name and medical record to write a letter.
Another statistic that shows the scale of the phenomenon: since early 2025, incidents of data leakage related to AI have increased by a factor of 2.5according to Palo Alto Networks. And 14 % of security incidents are directly attributable to generative AI applications.
Faced with this threat, companies are adopting a cautious approach, with the majority considering radical measures, according to a BlackBerry survey, 82 % of French companies were considering banning the use of ChatGPT and other generative AI on work tools, mainly because of the risks to data security and privacy (cited by 62 % of them). Nearly half also fear the potential impact on their reputation.
However, according to the President of the CNIL, 80 % of major data breaches could have been prevented with basic measures such as Double authenticationthe detection of mass extractions and raising employee awareness. It It is therefore essential to put safeguards in place to take advantage of AI without exposing your information capital.
Le shadow AI or the clandestine use of AI
This phenomenon, which is becoming more and more frequent, refers to the use of generative AI tools by employees without any supervision or declaration to the IT department or to management.
Nearly half of French employees use generative AI tools for professional purposes without telling their employers..
They are even twice as likely to use "shadow AI" than AI solutions provided by their company!
This clandestine use, often motivated by a lack of understanding of possible uses or a lack of in-house training, unwittingly exposes private or sensitive information and increases the number of data leakage incidents. It also creates a blind spot for cyber security and can hinder organisational transformation at scale.
Inference Attacks on sensitive data
An AI model, even when trained on anonymised data, can sometimes 'recreate' or infer personal information from cleverly repeated questions.
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Le prompt hacking and command injection
This involves manipulating the instructions given to the AI to make it ignore its security barriers, potentially forcing the AI to exfiltrate data to which the initial user should not have access.
The opacity of "black boxes
For many complex AI models, it is extremely difficult to understand precisely how they arrived at a conclusion, posing a major challenge to demonstrating compliance and the absence of bias discriminatory.
An increasingly strict legislative and regulatory framework
The acceleration of AI is taking place in a legal environment that is already demanding in terms of data protection.
The RGPD bulwark
In Europe, the General Data Protection Regulation (RGPD) applies as soon as an AI processes personal data, with penalties of up to 4 % of worldwide turnover.
It is crucial to understand that RGPD obligations fully apply to AI. Contrary to popular belief, the GDPR does not prevent AI innovation in Europe: it imposes a framework for responsible innovation. In 2024, the CNIL published concrete recommendations for developing AI systems. "privacy by designThis means respecting privacy from the outset.
These best practices include define a clear purpose to the AI project (to avoid collecting unnecessary data), determining an appropriate legal basis for each processing operation (consent, legitimate interest, etc.), and specify responsibilities (are you the controller or simply the processor?).
The RGPD also requires data minimisation (use only the data that is really necessary) and the storage limitation. The CNIL points out that algorithms can be trained on large volumes of data while respecting the minimisationprovided thatremove all unnecessary personal data upstream and to set up technical filters to collect only what is strictly necessary. Similarly, we need to set a shelf life learning data consistent with the objective, even if it means anonymising or aggregating the data after a certain period of time.
Finally, when an AI presents particular risks (sensitive data, large scale, vulnerable people involved, innovative use, etc.), carry out a impact assessment on Data Protection (AIPD) is strongly recommended.
This study enables risks to be mapped (discrimination, data breaches, etc.) and appropriate mitigation measures to be planned. before effective deployment.
CNIL sanctions any failure to comply with the RGPD
France, via the CNIL, is keeping a watchful eye: in 2024, the CNIL issued 331 corrective measures, including 87 sanctions handed down, resulting in fines of over €55 million. The number of data breach complaints has reached a record high (17,772 requests in 2024). The authorities will no longer tolerate breaches, even when they involve AI systems.
Alongside the RGPD, other texts provide a framework for AI, such as the brand new European regulation on AI (AI Act) which classifies AI systems by level of risk and imposes additional obligations for so-called "high-risk" AI. For example, an AI system for human resources or medicine will require certification of compliance and a governance strengthened. The message is clear: no company, in any sector, can afford to deploy AI without integrating compliance from the outset.
Practical strategies for keeping control of your data
Faced with these challenges, how do you reconcile innovation and safety? Here are a few examples concrete strategies to take advantage of artificial intelligence while retaining control of your data:
1. Implement rigorous data governance
Make an inventory of your information and classify sensitive data (customers, finance, R&D, HR, etc.). Appoint people to be in charge (Chief Data Officer, "IT and freedoms" officers) and get the whole company involved. DPO from the outset of any project involving personal data.
You should also make sure that new AI projects are subject to legal review (validation by the DPO) and, where appropriate, to an impact assessment to identify the necessary risks and protection measures at an early stage. Clear processes governing the collection, access, storage and sharing of data can prevent a great deal of negligence.
2. Internal policies to govern the use of AI
Establish clear rules on what is permitted or prohibited with artificial intelligence tools. For example, prohibit users from submitting personal data or strategic information in public AI systems, and prohibit the use of these tools for automated decisions without human validation. List the approved use cases (e.g. generation of generic marketing texts) and those that are prohibited (e.g. analysis of real customer data via an uncontrolled cloud service), then have this policy validated by management so that it applies to everyone.
3. Raising awareness and training employees
No technical measure will be effective without the support of employees. Organise training sessions to explain the AI risks (data leaks, bias, etc.) and the best practices to adopt. In practice, a few simple principles should be borne in mind: never divulge confidential information in a query, check the parameters confidentiality of the tools used, etc. As with the phishingTo achieve this, we need to instil a sense of digital prudence in everyone. The CNIL points out that digital vigilance is everyone's business within the organisation.
4. Choosing secure, controlled AI solutions
For sensitive uses, considerinternalise your AI models or use solutions hosted locally rather than sending strategic data to public cloud platforms. Keeping AI on premise or with a trusted European service provider, you avoid critical information (customer files...) being lost, secrets This approach also reduces dependency on foreign suppliers and facilitates compliance. This approach also reduces dependency on foreign suppliers and facilitates compliance. French alternatives are emerging: for example, the start-up Mistral AI offers high-performance open source models that can be deployed on your own infrastructure, enabling a bank to guarantee that its sensitive data does not leave the national territory.
5. Reinforcing the technical security of data
Apply the same high security standards to your AI projects as to the rest of your information system.
Encrypt sensitive data both at rest and when exchanging with models. Activate the Double authentication on access to critical databases (the CNIL requires this for files containing more than 2 million people). Strictly limit access to confidential information to authorised personnel only.
Deploy Data Loss Prevention to block abnormal extractions: some software can detect the presence of sensitive data in a request sent to a chatbot and can block automatically. Regularly audit your AI systems.
In conclusion, keeping control of your data in the age of AI is a strategic asset. Protecting data is not a brake on innovation; on the contrary, it's a guarantee of confidence and durability. As one expert points out, "The key to success will lie in choosing and implementing the right tools to ensure visibility and control of AI applications".. In other words, it is by retaining control - technical, organisational and legal - that you will be able to exploit AI with peace of mind. AI and data protection can and must go hand in hand to ensure your company's success in the current revolution.
