The widespread adoption of artificial intelligence in businesses is accompanied by a worrying phenomenon: the uncontrolled use of AI tools by employees. Shadow AI refers to the use of artificial intelligence applications and services outside of the company's official validation and control channels. This phenomenon creates major vulnerabilities in terms of data security, regulatory compliance, and access governance. Understanding the risks associated with Shadow AI is essential to protecting your organization.
What is Shadow AI and why is it emerging?
Shadow AI is a continuation of shadow IT, the well-known phenomenon where employees use applications and services that have not been approved by the IT department. With the explosion of generative AI tools such as ChatGPT, Midjourney, and Claude, employees now have powerful assistants at their fingertips.
The ease of access to these technologies largely explains their spontaneous adoption. An employee can create a free account on an AI platform in a matter of seconds, without prior authorization. This democratization, while beneficial for individual productivity, completely bypasses the traditional control mechanisms of information systems.
There are many reasons for this: saving time, improving the quality of work, automating repetitive tasks. However, the "shadow AI risk" (a term that originated in the United States when the first generative AI systems became popular in 2023) lies precisely in this unregulated adoption that circumvents established security policies.
The four major risks of Shadow AI
1/ Exposure of sensitive data
The first risk concerns the unintentional leakage of confidential information. When an employee uses an external AI tool to rephrase a document, analyze customer data, or generate code, they potentially transmit sensitive information to a third party.
Data entered into these tools may be stored on foreign servers, used to train AI models, or even exposed in the event of a security breach. Strategic business information, customer personal data, trade secrets, or intellectual property become vulnerable.
Harmonic Security (Q1 2025) — According to this study, 79.1% of data exposed via generative AI tools passes through ChatGPT, and approximately one-third of internal data leaks to unsecured environments now originate from the use of generative AI tools — without any trace of attack, alert, or malware.
The issue is exacerbated by regulations such as the GDPR in Europe. A company remains responsible for the processing of personal data, even when its employees use unauthorized tools. Financial and reputational penalties can be considerable in the event of a breach.
2/ The proliferation of uncontrolled SaaS access
Shadow AI increases the number of unsecured entry points into the company's digital ecosystem. Each new AI tool used represents a new external SaaS service with its own security policies, terms of use, and specific risks.
IT departments are losing visibility over these access points. They are unable to inventory the tools used, assess their security level, or ensure their compliance with company standards. This situation creates a blind spot in the overall cybersecurity strategy.
- Inability to maintain an up-to-date inventory of third-party applications
- Lack of control over data retention policies
- Difficulty auditing outgoing data flows
- Increase in potential attack surfaces
This fragmentation of access also complicates the implementation of consistent security policies. How can strong authentication, appropriate encryption, or granular access rules be applied to tools whose very existence is unknown to IT teams?
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3/ The problem of shared accounts
In the context of Shadow AI, informal account sharing is becoming common practice. An employee creates an account on an AI platform and shares their login details with colleagues to avoid multiple subscriptions or to circumvent usage restrictions.
This practice destroys any principle of individual traceability. It is impossible to determine who performed which action, who accessed which data, or who generated which content. In the event of a security incident or audit requirement, the company finds itself in a dead end.
Account sharing also violates the terms of service of most AI services, exposing the company to service suspension without notice. More seriously, this practice compromises authentication mechanisms and renders strong password policies ineffective.
4/ Lack of traceability and auditing
The fourth major risk concerns the inability to maintain adequate traceability of interactions with AI. In a professional environment, every action involving sensitive data should be recorded, time-stamped, and attributed to an identified user.
Shadow AI makes this traceability impossible. Companies cannot answer fundamental questions: What data was shared with which AI tools? When did these exchanges take place? Which employee initiated them? What results were obtained and how were they used?
This lack of traceability poses problems on several levels. From a security standpoint, it prevents early detection of incidents and complicates post-incident investigations. From a compliance standpoint, it makes it impossible to demonstrate compliance with applicable regulations.
Security and compliance audits rely on the ability to track and verify data access. Without this capability, a company cannot prove compliance or identify risky behavior before a major incident occurs.
How to detect shadow AI in your organization
The first step in managing risk is to identify its extent. There are several approaches that can be used to detect the use of unauthorized AI tools within the company.
Network flow analysis is an effective technical method. By monitoring outgoing connections, security teams can identify frequent access to domains associated with AI services. CASB (Cloud Access Security Broker) solutions automate this monitoring and provide real-time alerts.
- Analysis of proxy logs and DNS queries
- Monitoring application downloads on workstations
- Employee questionnaires and audits
- Review of business expenses to identify personal subscriptions
A complementary approach is to establish open dialogue with teams. Rather than adopting a purely repressive stance, understanding the needs that motivate the use of Shadow AI makes it possible to identify alternative solutions that comply with security policies.
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Mitigation strategies and access governance
In light of the identified risks, companies must develop a structured strategy combining technical, organizational, and human measures.
The implementation of an AI usage policy forms the basis of this strategy. This document must clearly define which tools are authorized, under what conditions, for what uses, and with what data. It must also specify the consequences of non-compliance.
At the same time, the company should offer official, secure alternatives. By deploying AI solutions that are approved and comply with security standards, you can meet the legitimate needs of employees while maintaining control. This approach reduces the temptation to resort to shadow AI.
On a technical level, several measures strengthen access governance. Single sign-on (SSO) with Identity and Access Management (IAM) solutions allows you to centralize and control access to approved SaaS applications. Data Loss Prevention (DLP) solutions can detect and block attempts to transfer sensitive data to unauthorized services.
However, awareness remains the most crucial factor. Employees must understand the risks of shadow AI, not as an arbitrary constraint, but as a means of protecting the company and their own data. Regular training and targeted communications maintain this awareness of the issues at stake.
The adoption of artificial intelligence must be controlled.
Shadow IT poses a major governance challenge for modern businesses. Risks related to sensitive data, uncontrolled SaaS access, shared accounts, and lack of traceability threaten the security and compliance of organizations.
Rather than seeking to ban the use of AI altogether, a pragmatic approach is to regulate and secure its adoption. By combining clear policies, appropriate technical solutions, and ongoing awareness, you can reap the benefits of AI while managing the risks.
Access governance in the age of AI requires an evolution in traditional security practices. The companies that will succeed are those that manage to strike a balance between innovation and protection, between agility and control. Shadow AI is not inevitable, but a symptom that calls for a structured and proactive organizational response.
