Shadow AI can seriously compromise the security and reputation of a company and its employees. Here’s what it is, the risks, and how to address it effectively.
Artificial intelligence has entered businesses at an unprecedented speed. Tools like OpenAI with its ChatGPT chatbot, Anthropic with Claude, or Google with Gemini are now part of the daily routine of many workers, from drafting reports and writing code to analyzing data and creating presentations.
However, when these tools are used without the approval or oversight of IT and security departments, we enter the realm of so-called "shadow AI," a phenomenon that is becoming a major concern for those responsible for cybersecurity in companies. But what is it and why is it so risky for businesses?
What is Shadow AI
Shadow AI refers to the unauthorized use of artificial intelligence models or services within an organization without formal approval from IT. The concept is reminiscent of "shadow IT," which arose with the spread of software and cloud services adopted independently by employees. But while shadow IT could involve personal accounts on unapproved storage platforms or project management tools, shadow AI introduces a higher level of risk.
Generative AI tools are extremely accessible, making them difficult to intercept with traditional control systems. Unlike many traditional SaaS applications, large language models (LLMs) process data, learn from statistical patterns, and produce outputs that can influence strategic decisions or production-ready code.
Furthermore, many AI tools are available in "freemium" mode or as ready-to-use cloud services. For example, an employee can upload a document to get a summary, paste a block of code to debug, or generate a draft presentation in minutes. In contexts where internal processes are perceived as slow or overly restrictive, unauthorized AI appears as a legitimate shortcut to "doing it better and faster."
The problem is that this adoption, while driven by good intentions, often occurs without awareness of the risks associated with data management and regulatory compliance.
Why it threatens corporate security
The first risk is data leakage. Every prompt entered into an AI system can contain sensitive information: proprietary code, financial data, customer or employee personal information. If this data is uploaded to external platforms, the company loses control over where it is stored, for how long, and for what purposes it can be reused.
In some cases, the terms of service stipulate that inputs can be used to improve the model. This means that confidential information could end up, even indirectly, in larger datasets, with unpredictable consequences.
A second level of risk concerns regulatory compliance. Regulations such as the General Data Protection Regulation (GDPR), as well as industry frameworks such as HIPAA, PCI DSS, or SOC 2, impose stringent requirements on the handling of personal and sensitive data. If an employee uploads protected data to an unauthorized AI service, the organization could face serious violations, with fines that, in the case of the GDPR, can reach up to 4% of annual global revenue or €20 million, whichever is higher.
Then there is the risk of technical vulnerabilities. The unauthorized integration of AI service APIs into enterprise systems can create new, unmonitored access points, expanding the attack surface. A compromised chatbot or an unverified browser extension can become vectors for malware and phishing.
Intellectual Property and Loss of Competitive Advantage
A less obvious but equally critical issue concerns intellectual property. When confidential source code or documents are inserted into public AI tools, the company risks compromising its trade secrets.
In a specific case that occurred in 2023, an electronics company discovered that some employees had uploaded portions of proprietary code to a chatbot to obtain debugging support. The material, potentially stored by the system, could then be resurfaced in responses to other users. In addition to the financial damage, the incident had a significant reputational impact.
Even on the creative and communication level, the uncontrolled use of AI can dilute a brand’s distinctive voice. Feeding public models with proprietary content means contributing to an ecosystem where what makes an organization unique can be assimilated into generalist models.
It should always be kept in mind that generative AI models produce outputs based on statistical probabilities and, therefore, lack context in the human sense of the word and can generate errors, biases, or even outright "hallucinations." If an unauthorized tool is used, for example, to filter resumes or support selection decisions, any discrimination or errors could expose the company to litigation. The lack of traceability typical of shadow AI also makes it difficult to reconstruct the decision-making process, complicating legal defenses.
What Employees Risk
The use of unauthorized AI often arises in good faith, with the intent of improving personal productivity. However, the risks do not only affect the organization.
An employee who uploads confidential data to an external platform may violate internal policies, confidentiality agreements, or contractual obligations. In the event of an incident, this can result in formal warnings, disciplinary action, dismissal, or, in the most serious cases, civil and criminal liability.
Even on a professional level, uncritical reliance on AI-generated content can compromise individual credibility. Submitting factually inaccurate reports and biased analyses can undermine the reputation of those who produced them, especially if it emerges that they were generated with unauthorized AI tools.
The Importance of Governance
In the face of Shadow AI, a total ban rarely works. Blocking all platforms can push usage to personal devices or external networks, making the phenomenon even more opaque.
More mature organizations are adopting a governance and training-based approach, aimed at defining clear policies on the acceptable use of AI, classifying the risk levels of different tools, monitoring traffic to unapproved AI services, and integrating Data Loss Prevention solutions capable of intercepting the sending of sensitive information.
At the same time, it is crucial to offer secure alternatives, such as enterprise models with contractual guarantees on privacy and data processing, sandbox environments for controlled experimentation, and training programs that explain why certain practices expose them to concrete risks.
Original article published on Money.it Italy. Original title: Cos’è l’AI shadow, perché rappresenta un rischio per la sicurezza aziendale e cosa rischiano i dipendenti
