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Shadow AI is the use of AI tools or features at work without the organization’s authorization or oversight. It becomes a security and governance concern when staff use them to handle company information without clear rules, visibility, or safeguards—not simply because an employee opens a consumer AI service.

What is shadow AI?

Shadow AI is the AI-specific counterpart to shadow IT: tools, platforms, or use cases adopted outside an organization’s approval and oversight. For example, an employee might use a large language model to draft a report without considering whether the material entered into it is sensitive. IBM describes such use as emerging in part when company-provided tools do not meet workers’ needs (IBM, July 30, 2025).

The term can cover consumer AI services used with personal accounts as well as AI capabilities adopted without normal security and governance review. The precise boundary depends on an organization’s policies: a tool may be acceptable for public information but prohibited for customer records or internal plans. The important distinction is whether the organization has assessed and authorized the use, not whether the tool is popular or free.

Why are employees using AI tools their company has not approved?

Often, workers are trying to get a task done more effectively. In IBM’s 2025 survey, 80% of surveyed American office workers said they used AI in their roles, but only 22% said they relied exclusively on employer-provided tools. Nearly 40% preferred external AI solutions because of their features. These results came from a US subsample of 1,000 full-time office workers familiar with AI tools, within a 3,000-person North American survey conducted with Censuswide; they do not represent all workers or organizations.

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Respondents also saw potential value: 97% believed AI boosts productivity and 75% reported moderate to significant improvement. Those are workers’ perceptions, not results from a controlled productivity test. IBM also found that 60% of surveyed employees said hands-on learning would boost their AI usage. Taken together, the findings point to possible gaps in tool fit, access, guidance, or training; they do not prove that any single fix will eliminate unauthorized use.

What are the risks of shadow AI?

Sensitive information can be exposed

IBM’s 2025 Cost of a Data Breach Report release found that one in five organizations in its study reported a breach due to shadow AI. The underlying research, conducted by Ponemon Institute and sponsored and analyzed by IBM, covered breaches experienced by 600 organizations globally from March 2024 through February 2025. Among shadow-AI security incidents, 65% involved compromised personally identifiable information (PII), compared with 53% across the report’s global average; 40% involved compromised intellectual property, compared with 33% globally.

These findings support treating sensitive-data handling as a central control. They do not establish that every unapproved AI service stores or trains on submitted data; organizations need to assess the specific service and its terms, configuration, and data flows.

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Visibility and access controls may be weak

In the same IBM report, 63% of breached organizations had no AI governance policy or were still developing one. Of organizations that did have AI governance policies, 34% performed regular audits for unsanctioned AI. IBM also reported that organizations with high shadow-AI levels had average breach costs $670,000 higher than organizations with low or no shadow AI. This is a comparison within the study, not proof that shadow AI alone caused the difference or a prediction of what any particular organization will pay.

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The report separately found that 13% of organizations reported breaches of AI models or applications, and that 97% of those with such compromises lacked AI access controls. These figures concern AI-related breaches broadly, not shadow-AI incidents specifically.

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AI systems bring security and quality considerations

NIST describes confidentiality, integrity, and availability risks affecting AI systems, their training and output data, and the software and hardware underneath them. It also identifies AI-specific concerns including evasion, model extraction, membership inference, and availability. These are risks to consider when assessing AI systems; they should not be read as a claim that ordinary use of a public chatbot necessarily involves such an attack. NIST notes that current frameworks do not comprehensively address every AI risk and that the field is changing rapidly (NIST AI Research – Security and Resilience).

Organizations may also need to consider output quality and compliance obligations. IBM identifies compliance violations and loss of control over sensitive business information as concerns. Applicable legal duties vary by jurisdiction, sector, and use case, so general AI guidance is not a substitute for organization-specific legal review.

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How can organizations detect and manage shadow AI?

1. Discover actual use

Inventory AI services and AI-enabled features in use, including the work they support and the kinds of information they handle. Use appropriate technical and administrative methods to identify unsanctioned use, while making clear that the goal is to manage risk and provide workable options—not to assume every use is harmful. IBM’s finding that only 34% of organizations with AI governance policies regularly audited for unsanctioned AI suggests that policy alone may not provide visibility.

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2. Provide approved tools that fit real workflows

Ask employees which tasks they are trying to improve and where approved tools fall short. Assess whether the sanctioned options provide useful features, are accessible to the people who need them, and can handle the organization’s intended use cases. The workforce survey’s reported preference for external tools because of their features makes usability a governance issue as well as a productivity issue.

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3. Set practical data-handling rules

Tell staff what information may be entered into which tools, what is prohibited, and who can approve an exception. Make the rules concrete: distinguish public material from internal information, personal data, customer records, credentials, source code, and confidential business plans as relevant to the organization. Review access controls and service settings rather than assuming all AI products handle data in the same way.

4. Train with hands-on examples

Use realistic workflows to show how to choose an approved tool, handle data safely, and check AI-generated outputs before relying on them. IBM’s survey found that 60% of respondents said hands-on learning would boost AI usage. Training is more useful when employees can practice the approved route for the tasks they actually perform.

5. Match governance to the AI system and its use

NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for incorporating trustworthiness into the design, development, use, and evaluation of AI products, services, and systems. NIST says AI RMF 1.0 is being revised; its Generative AI Profile was released July 26, 2024. The framework is a risk-management aid, not a certification or a complete answer to legal compliance. NIST also describes work on implementation-focused control overlays for generative AI, predictive AI, and single-agent and multi-agent systems, with confidentiality, integrity, and availability among the focus areas (NIST AI Risk Management Framework).

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For an organization, the useful question is whether its controls address the AI tools and workflows it actually uses: who can access them, what data they can process, how outputs are checked, and how risks are monitored. As NIST puts it, “The security and resilience of AI technologies is an area of active research, and challenges and potential solutions are changing very rapidly.”

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