--- title: What is shadow AI? description: Shadow AI is employee use of AI tools without IT oversight. Learn the risks, how to detect it, and how to govern AI app usage before data leaks. canonical_url: https://www.acronis.com/en/blog/posts/what-is-shadow-ai/ locale: en last_updated: 2026-10-27 --- [Acronis Blog](https://www.acronis.com/en/blog/) # What is shadow AI? Shadow AI is employee use of AI tools without IT oversight. Learn the risks, how to detect it, and how to govern AI app usage before data leaks. [Try Now](https://www.acronis.com/en/products/cloud/trial/) [![Image 2: Acronis Editorial Team](https://staticfiles.acronis.com/images/content/d85707ae91461544212d39836dc32b42.png)](https://www.acronis.com/en/blog/authors/acronis/ "Acronis Editorial Team") [Acronis Editorial Team](https://www.acronis.com/en/blog/authors/acronis/) 10 min read Last updated 20 September 2026 Shadow AI is the use of generative AI tools by employees without the knowledge, approval or oversight of an organization's IT or security teams. The term covers any AI tool an employee adopts on their own, from a free chatbot accessed through a browser to an AI feature inside an unsanctioned app. Adoption has outpaced governance at most organizations. The share of organizations using AI in at least one business function reached 78% in 2024, up from 55% a year earlier, according to the Stanford HAI 2025 AI Index Report, and more recent 2025 surveys suggest that share has climbed further. When that adoption happens outside approved channels, the organization loses visibility into what data is going where --- and managing shadow AI takes more than discovery. Organizations need to identify which AI apps are in use, decide which are approved, monitored, restricted or blocked, and enforce that decision before sensitive data leaves the business. ## Why is shadow AI different from shadow IT? Shadow IT is the use of unsanctioned software or hardware on a corporate network, such as an employee installing an unapproved app or signing up for a SaaS tool without telling IT. The core risk of shadow IT is access: an unmanaged tool widens the attack surface and sits outside patching and monitoring. Shadow AI carries that risk and adds another. An AI tool does not just sit on the network, it receives direct input from the employee. A worker pastes a contract, a patient record or a block of source code into a prompt to get help. That content leaves the organization the moment it is submitted. Depending on the provider, account type and configuration, that content may be retained, reviewed, or used in ways the organization has not approved. With shadow IT the question is what an employee installed. With shadow AI the question is what an employee typed. Data exposure, not just unmanaged access, is the defining risk. There is also a timing difference. Shadow IT often leaves traces an audit can find later, such as an installed application or a recurring SaaS charge. Without dedicated monitoring, a prompt may leave little or no useful internal record after the exposure has already happened --- though where the right controls exist, browser history, endpoint telemetry, DLP events, SaaS audit logs or AI-specific tooling can still capture a trace. That gap is what makes shadow AI both easy to overlook and hard to investigate after the fact. ## How does shadow AI enter an organization? Shadow AI rarely arrives through a single channel, and most of these paths look harmless to the employee taking them. The intent is almost always productivity, not evasion: a worker reaching for the fastest tool to finish a task. Common entry points include: * An employee signs up for a free ChatGPT account using a work email address, outside any IT process. * An employee uses a personal Gemini or Claude account on a managed endpoint, making the session harder to govern and potentially outside approved enterprise controls. * An employee uploads a work document to a consumer AI tool to summarize or rewrite it. * An employee pastes customer data or source code into a chatbot to debug a problem or draft a reply. * An employee installs an AI browser extension that processes page content through an external model. * An employee uses an AI feature inside an already-approved app without realizing data leaves the organization. ## Why does shadow AI matter? Shadow AI matters because the data employees move into these tools is often invisible to the organization and frequently sensitive. Among enterprise employees, about 45% use generative AI tools, and 77% of those users paste data into prompts, according to the LayerX Enterprise AI and SaaS Data Security Report 2025. The same research found that 82% of that paste activity comes from unmanaged personal accounts, which means most of the data going into AI tools moves outside any enterprise oversight. The cost shows up when something goes wrong. Shadow AI was involved in about 20% of breaches, and organizations with high levels of shadow AI averaged roughly $670,000 more per breach than those with low or no shadow AI, according to the IBM Cost of a Data Breach Report 2025. That same report found that 97% of organizations with an AI-related breach lacked proper access controls for AI, and 63% either had no AI governance policy or were still developing one. The pattern is consistent: adoption is racing ahead of oversight, and attackers are starting to exploit the gap. For an SMB, a single exposure of regulated client data can mean regulatory penalties, lost contracts and remediation costs that dwarf the price of governing AI use in the first place. Shadow AI visibility is the first step. Once organizations know which tools are being used, they also need controls to prevent sensitive data from being shared with public AI tools and to detect harmful prompts that could manipulate AI behavior or introduce unsafe content into workflows. ## What types of data are most at risk? Shadow AI is most dangerous when employees feed it data the organization is obligated to protect. The types most at risk include: * Personally identifiable information (PII), such as names, addresses and identification numbers, which triggers privacy obligations if exposed. * Protected health information (PHI), which carries strict handling rules in healthcare and related sectors. * Payment card data (PCI-DSS), which exposes the organization to fraud and compliance penalties. * Source code, which can reveal intellectual property and security weaknesses. * Internal strategy documents, which lose their value if they reach a competitor or a training set. * Credentials and access tokens, which can hand an attacker a direct route into corporate systems. ## Shadow AI vs shadow IT: key differences Shadow IT Shadow AI Definition Unsanctioned software or hardware on the network Unsanctioned AI tools that receive employee input Data risk Widened attack surface from unmanaged tools Sensitive data leaving in prompts and uploads Detection method Network and asset inventory scans Visibility into AI tool use and prompt content Primary control Access management and patching AI app governance, prompt inspection, and data-use policy ## How do you detect shadow AI in your organization? Detecting shadow AI starts with finding where AI use is already happening, then bringing it under policy. Practical steps include: 1. Audit network and proxy logs for traffic to known AI tool domains. 2. Review browser activity on managed endpoints for AI tool sessions and extensions. 3. Inventory AI features inside already-approved SaaS applications. 4. Survey employees directly about which AI tools they use and why. 5. Check identity logs for sign-ups to AI services using corporate email addresses. 6. Establish a baseline of normal AI use so anomalies stand out. 7. Document findings and turn them into a written AI usage policy with clear approved, monitored, restricted and blocked statuses. ## How do organizations govern and control shadow AI? Two frameworks anchor most AI governance programs. The NIST AI Risk Management Framework 1.0, published January 2023, organizes AI risk into four functions: Govern, Map, Measure and Manage. It gives an organization a structured way to identify where AI is used, assess the risk, and put controls and accountability in place, with governance running across all of it. ISO/IEC 42001:2023 is the world's first AI management system standard. It specifies requirements for establishing, implementing, maintaining and continually improving an AI management system, which lets an organization treat responsible AI use as an auditable, certifiable process rather than an informal practice. Together, the two give an organization a common language for what AI governance should cover and how to prove it is working. For a smaller organization, the value is less about certification and more about structure: know where AI is used, decide what is allowed, put a control in place, and review it. In practice, that structure looks like a short cycle: * Discover existing AI use across browsers, endpoints and SaaS. * Classify tools by the risk they carry --- what data they touch and who uses them. * Assign each AI app a status: approved, monitored, restricted or blocked. * Define what data can and cannot be shared with each status. * Monitor usage against policy and review the list on a regular cadence. Effective shadow AI governance should assign each AI app a clear status: approved, monitored, restricted, or blocked. That turns discovery into an enforceable control model instead of a static inventory. Governance does not have to mean banning AI --- most organizations get more value from sanctioning a small set of tools, setting clear rules for what data can go into them, and monitoring against those rules than from a blanket ban that simply pushes usage further into the shadows. And governance should not stop at standalone chatbots: it should cover AI features embedded in sanctioned SaaS apps, browser extensions, plugins and assistants, since those are just as capable of moving data out of the organization. For the policy side of this work, see our guide to [writing an AI usage policy](https://www.acronis.com/en/blog/posts/ai-usage-policy-template/). ## How does Acronis GenAI Protection address shadow AI? The hardest part of shadow AI is that you cannot govern what you cannot see. Employees adopt AI tools faster than IT can sanction them, and network-only and legacy endpoint tools often provide incomplete visibility into which AI apps are being used and what data may be flowing into them, especially when employees use browser prompts, personal accounts, or AI features embedded inside SaaS apps. Acronis GenAI Protection helps MSPs and the businesses they serve move from shadow AI visibility to AI app governance. It discovers and monitors the generative AI tools used across protected environments, identifies unsanctioned or risky usage, surfaces behavior patterns, and provides reporting to support policy decisions. With governance controls, MSPs can help clients define which GenAI / AI apps are approved, monitored, restricted, or blocked --- keeping AI usage aligned with company policy. GenAI Protection also supports the broader Acronis GenAI security model by helping protect sensitive data in AI interactions and detect harmful prompts, including prompt injection attempts. Because it is delivered through the existing Acronis platform rather than a separate console, a provider can extend AI governance to client environments without adding another point product. Coverage runs on Windows 10, Windows 11 and macOS endpoints. For sensitive data leaving through other channels, [Acronis Data Loss Prevention](https://www.acronis.com/en/products/cloud/cyber-protect/data-loss-prevention/) extends protection beyond browser-based GenAI interactions. The result turns shadow AI from an unmanaged risk into a governed service, where AI use is inventoried, measured against policy, and brought under control before sensitive data leaves the organization. ## Put shadow AI under policy Seeing which AI tools your clients already use is the fastest way to start governing them. [Book a 1:1 demo of Acronis GenAI Protection](https://www.acronis.com/en/products/cloud/demo/?service=genai-protection) or [start a free trial](https://www.acronis.com/en/products/cloud/trial/) to see discovery and governance running together, or explore where it fits inside the wider [Acronis AI-native platform](https://www.acronis.com/en/acronis-ai/). If you're evaluating options, our comparison of [the best AI security tools for SMBs and MSPs](https://www.acronis.com/en/blog/posts/best-ai-security-tools-smb/) lays out how different approaches to discovery, governance and data protection stack up. ## Key takeaways * Shadow AI is the use of generative AI tools by employees without IT or security oversight, and it differs from shadow IT because the core risk is sensitive data leaving in prompts, not just unmanaged access. * Detection is only the first step. Effective governance assigns each AI app a status --- approved, monitored, restricted or blocked --- which turns a static inventory into an enforceable control. * Adoption is widespread, with 78% of organizations using AI in at least one business function in 2024, according to the Stanford HAI 2025 AI Index Report, and 2025 surveys suggest adoption has climbed further since. * Most data going into AI tools is hard to see without dedicated monitoring, with 82% of AI prompt pastes coming from unmanaged personal accounts, according to the LayerX Enterprise AI and SaaS Data Security Report 2025. * Shadow AI was involved in about 20% of breaches, and organizations with high levels of shadow AI averaged roughly $670,000 more per breach than those with low or no shadow AI, according to the IBM Cost of a Data Breach Report 2025. * AI usage policies should cover standalone GenAI tools, AI features embedded in sanctioned SaaS apps, browser extensions and personal accounts used for work --- not just the obvious chatbots. * The NIST AI Risk Management Framework 1.0 and ISO/IEC 42001:2023 give organizations a structured, auditable way to govern AI use. ## Frequently asked questions **What is the difference between shadow AI and shadow IT?** Shadow IT is unsanctioned software or hardware on a corporate network, where the main risk is unmanaged access. Shadow AI is the use of unsanctioned AI tools that also receive direct employee input, so the main risk is sensitive data leaving the organization in a prompt or upload. Shadow AI is a subset of the broader shadow IT problem with a sharper data-exposure edge. **Is using ChatGPT at work shadow AI?** It depends on whether the organization has approved and governed its use. If an employee uses ChatGPT through a personal account or without IT approval and monitoring, that is shadow AI. If the organization has approved the tool, defined what data can be used, set account and access requirements, and monitors usage against policy, it becomes governed AI use rather than shadow AI. **What is the difference between shadow AI visibility and shadow AI governance?** Visibility shows which AI tools employees are using, who is using them, and how often. Governance turns that visibility into action by deciding which tools are approved, monitored, restricted, or blocked, and by enforcing rules for what data can be shared. Visibility is the starting point; governance is what actually reduces risk. **How do I know if my employees are using shadow AI tools?** Start by auditing network and proxy logs for traffic to AI tool domains, reviewing browser activity and extensions on managed endpoints, and checking identity logs for sign-ups using corporate email. A direct employee survey often surfaces tools that logs miss. A dedicated discovery tool inventories which AI tools are in use, by which employees, and how often. **What data is most at risk from shadow AI?** The data an organization is obligated to protect is most at risk: personally identifiable information, protected health information, payment card data, source code, internal strategy documents and credentials. Shadow AI incidents disproportionately expose customer PII and intellectual property, which is why ungoverned use carries both regulatory and competitive consequences. **What frameworks cover shadow AI governance?** The NIST AI Risk Management Framework 1.0, published January 2023, organizes AI risk into four functions: Govern, Map, Measure and Manage. ISO/IEC 42001:2023 is the world's first AI management system standard and specifies requirements for an auditable AI management system. Both give organizations a structured way to bring shadow AI under policy and prove the controls work. [Cyber protection](https://www.acronis.com/en/blog/tags/cyber-protection/) [MSP cybersecurity](https://www.acronis.com/en/blog/tags/msp-cybersecurity/) [Antivirus protection](https://www.acronis.com/en/blog/tags/antivirus-protection/) [Corporate cybersecurity](https://www.acronis.com/en/blog/tags/corporate-cybersecurity/) [Cyber protection for businesses](https://www.acronis.com/en/blog/tags/cyber-protection-for-business/) ## Written by ![Image 3: Acronis Editorial Team](https://staticfiles.acronis.com/images/content/d85707ae91461544212d39836dc32b42.png) Acronis Editorial Team Acronis is a global cyber protection company that provides natively integrated cybersecurity, data protection, and endpoint management for managed service providers (MSPs), small and medium businesses (SMBs), and enterprise IT departments. 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