gensai.aiGenerative artificial intelligence has established itself in companies within a few months. Internal chatbots, natural language search engines, writing assistants, automated document analysis, AI agents connected to CRM or business tools — uses are multiplying, driven by immediate productivity gains.
But this acceleration raises a strategic question: is AI opening new cybersecurity gaps in companies?
Shadow AI, prompt injection, sensitive data leaks, badly configured RAG (Retrieval-Augmented Generation) — the risks are real, and often poorly understood.
Here are the main cyber dangers linked to enterprise AI, and good practices for keeping them under control.
Shadow AI: an invisible but massive risk
"Shadow AI" refers to the use of artificial intelligence tools by employees without official validation from IT or management.
The phenomenon is comparable to "shadow IT", but it is even faster and more diffuse. A browser and a free account are all it takes to start using a generative AI.
Frequent examples:
- An employee pastes a confidential contract into a public AI tool to get a summary.
- A marketing team analyses customer data through an external platform.
- A manager creates an assistant connected to internal documents without precise control over access rights.
These practices are generally not malicious. They answer a need for efficiency. But they raise several major problems:
- Loss of control over sensitive data
- Non-compliance with GDPR
- No traceability
- Dependence on non-sovereign solutions
Shadow AI becomes a strategic risk because it escapes the company's governance.
Prompt injection: a new form of attack targeting LLMs
LLMs (Large Language Models) work from textual instructions called "prompts". Prompt injection consists of manipulating these instructions to bypass the rules defined by the system.
Unlike classic attacks (SQL injection, XSS and so on), prompt injection does not target a traditional technical flaw. It exploits the linguistic and probabilistic logic of the model.
Example:
A malicious user tries to insert an instruction into their request designed to force the AI to ignore its safety rules or reveal internal information.
In a system connected to a document base through RAG, this type of attack can aim to extract sensitive data.
The difficulty is that the model "understands" natural language. What therefore needs securing is:
- The structure of the prompts
- Input filters
- Access rights to the data
- The actions the AI agent is allowed to carry out
Cybersecurity for AI systems thus requires specific mechanisms, adapted to generative models.
Data leaks: where are the main risks?
AI-related data leaks can occur at several levels.
1. Use of unmanaged public tools
When employees submit sensitive documents to external platforms, the company can lose control over the data lifecycle.
2. Badly configured RAG
An answer engine connected to a poorly segmented internal document base can make confidential information accessible to unauthorised users.
3. Overly permissive AI agents
An agent able to access the CRM, email and accounting without fine-grained role management considerably increases the exposed surface.
4. No logging
Without logs or traceability, it becomes difficult to identify the origin of a leak or of abnormal behaviour.
Integrating AI does not merely create a new tool. It changes the architecture of data flows, and therefore the company's overall attack surface.
Why does AI require specific governance?
AI systems have particular characteristics:
- They are non-deterministic (the answer can vary).
- They interpret natural language.
- They can interact with several systems through APIs.
- They can carry out actions (AI agents).
A classic IT security policy is not enough. Structured AI governance is required.
This involves:
- Mapping AI use cases
- Identifying sensitive data flows
- Choosing models (LLM, SLM, open source, sovereign and so on)
- A hosting strategy (public cloud, sovereign cloud, on-premise)
- Managing permissions and roles
- Putting traceability mechanisms in place
- Training teams
Cybersecurity becomes a central pillar of AI projects.
How can you secure enterprise AI without slowing innovation?
The goal is not to ban AI. On the contrary, it is to structure its adoption.
An effective approach rests on several key steps:
- Audit existing uses — identify the tools already in use, officially or not.
- Define an AI policy — clarify what is allowed, restricted or forbidden.
- Provide secure alternatives — offer validated solutions hosted within a controlled framework.
- Compartmentalise data — segment document bases and manage access rights precisely.
- Apply least privilege to AI agents — strictly limit the actions an agent can carry out.
- Raise employee awareness — explain the risks linked to prompt injection and sharing sensitive data.
Innovation and security are not opposed. They must be thought through together.
Is AI a danger or a strategic lever?
AI is not only a source of risk. It can also strengthen cybersecurity:
- Anomaly detection
- Automatic log analysis
- Secure document classification
- Monitoring suspicious behaviour
But for AI to become a strategic lever rather than a vulnerability, it must be integrated with method, responsibility and technical rigour.
This implies:
- GDPR compliance
- Applying data minimisation principles
- Taking AI Act requirements into account
- Seeking sovereign solutions where necessary
- Documentation and auditability of systems
In summary: the main cyber dangers of enterprise AI
The major risks to anticipate are:
- Unmanaged shadow AI
- Prompt injection attacks
- Sensitive data leaks
- Badly configured RAG
- Overly permissive AI agents
- The absence of structured governance
AI is an accelerator of digital transformation.
But it is also an accelerator of risk if deployed without a clear framework.
The strategic question is: how do you design AI systems that are secure, responsible and suited to business needs?
Organisations that anticipate these risks from the design phase will gain an edge, both in performance and in control over their cyber exposure.