Shadow AI is the use of AI tools or applications within an organization without formal approval or oversight from IT, security, or compliance teams. This includes generative AI platforms, AI-enhanced SaaS features, and custom LLM integrations. Employees typically adopt these tools to improve productivity, without fully accounting for the data security, privacy, and compliance risks.
Shadow AI refers to the use of artificial intelligence (AI) tools and applications within an organization without the knowledge, approval, or oversight of IT, security, or compliance teams. Employees adopt these tools — often free, browser-based, or embedded in familiar software as a service (SaaS) platforms — without going through the formal review processes that protect corporate data and systems.
In practice, shadow AI can take several forms:
An employee pastes confidential customer data into a free chatbot to draft an email.
A developer uses an unapproved AI coding assistant on proprietary source code.
A marketing team purchases an AI content tool using a corporate credit card without a security review.
Teams use SaaS products with embedded, unapproved AI features.
Think of shadow AI the way you might think of someone plugging an unmanaged personal device into a corporate network. The device itself may be harmless, but without visibility and controls, it creates blind spots that attackers and regulators can exploit. Shadow AI works the same way: sensitive data, often source code, customer records, or unreleased product plans, moves to third-party systems that the organization never vetted and cannot monitor.
Generative AI tools like OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Gemini, Microsoft’s GitHub Copilot, Perplexity, and Midjourney, among many others, have made shadow AI a pressing risk. GenAI traffic has grown sharply year over year, and the average enterprise now runs dozens of AI applications, many of which were never approved by corporate IT. Data loss prevention (DLP) incidents tied to GenAI tools have grown in step, now representing a measurable share of all enterprise DLP alerts.
How does shadow AI happen?
Shadow AI typically starts with good intentions. An employee finds an AI tool that speeds up a repetitive task: summarizing meeting notes, drafting customer emails, writing code. They use it, then tell a colleague. Within weeks, sensitive business data flows into unvetted third-party AI systems.
Several factors drive this:
Ease of access. Most generative AI tools require only a browser and a free account. There are no procurement cycles, no software installs, no IT tickets.
Embedded AI features. AI capabilities are built into applications employees already use, including Google Workspace, Microsoft 365, Notion, and Slack. These embedded AI features represent new risks (cyber, legal, compliance, etc.) when the underlying models access sensitive data.
Productivity pressure. When employees are expected to do more with less, tools that deliver real productivity gains are hard to resist, even without IT approval.
Lack of clear policy. Many organizations have not yet defined formal AI usage policies, leaving employees uncertain about what is permitted.
Shadow AI vs. shadow IT
Shadow AI is a subset of shadow IT, the broader practice of using technology that IT has not approved. AI tools carry risks that most shadow IT does not.
Traditional shadow IT concerns centered on unsanctioned storage, apps, and infrastructure. Shadow AI adds:
Uncontrolled data ingestion. AI tools actively consume and process the data fed into them. When that data includes trade secrets, regulated personally identifiable information (PII), or customer records, the exposure is operational, not theoretical. Unlike shadow IT, the risk of data exposure to shadow AI is irreversible.
Algorithmic opacity. Unlike a rogue spreadsheet plug-in, an AI system can produce outputs that influence business decisions without a traceable audit trail. There is no easy way to reconstruct what data was used or why a model responded the way it did.
Model training risks. Some AI services use submitted data to improve their underlying models. Data entered today could train a public model tomorrow, potentially surfacing proprietary information in another user’s response.
What are the risks of shadow AI?
Shadow AI creates risks beyond simple data exposure. Organizations that allow it to spread, even unintentionally, face a connected set of problems:
Data leakage and exfiltration. When employees paste proprietary strategies, customer records, or source code into external AI tools, that data leaves the corporate security perimeter. It may be stored on third-party servers, logged indefinitely, or incorporated into model training datasets.
Regulatory and compliance violations. Privacy regulations like GDPR (Europe), regional frameworks like India’s DPDP Act, and specific sector laws like HIPAA (U.S. healthcare) or CCPA (California) impose strict controls on how personal and sensitive data is processed and shared. Submitting regulated data to an unapproved AI service may constitute a violation of contractual obligations on how a customer’s data is handled, regardless of whether a breach occurs or not.
Expanded attack surface. Unsanctioned AI tools often connect via personal accounts or unapproved APIs. These channels bypass endpoint security controls, DLP policies, and network monitoring, creating gaps that threat actors can exploit.
Lack of auditability. With shadow AI, there is no record of what data was used, what decisions were influenced, or what outputs were generated. This makes incident response, compliance audits, and legal discovery significantly harder and more expensive.
Model poisoning and bias. Unvetted AI models may have been trained on corrupted, biased, or manipulated data. Outputs from these models can introduce flawed conclusions into business-critical processes, with no visible indicator that something is wrong.
Overprivileged access. Shadow AI tools are often granted broad permissions by employees who want them to work correctly, without understanding the scope of data access they are enabling.
Data ownership confusion. When AI tools process organizational data under personal or free-tier accounts, questions of data ownership, retention, and deletion rights become murky and often difficult to resolve.
Shadow AI isn’t primarily a technology problem. Instead, it’s primarily a governance gap. The employees driving it are usually motivated people trying to accelerate productivity. The best way for organizations to respond is with clear policies and sanctioned alternatives, rather than blanket bans that simply drive users toward unmonitored personal accounts and devices.
Common examples of shadow AI in the enterprise
Shadow AI shows up differently depending on the team. A few common patterns:
A product manager summarizes a sensitive strategy deck using a public AI chatbot. The document contains unreleased roadmap information and partner agreements, details that now reside on a third-party server outside the organization’s control.
A software developer builds an internal customer-facing chatbot using an external large language model (LLM) API, without notifying the security team. Customer data flows through an integration that was never reviewed for risk.
A marketing designer uses an AI image generator embedded in a popular design tool, uploading brand copy that contains unreleased campaign materials, assuming the SaaS agreement covers all features without verifying how AI inputs are handled.
An HR professional uses a free AI resume-screening tool to process candidate applications, inadvertently exposing applicant PII to an unapproved platform.
A financial analyst feeds quarterly forecasts into an AI summarization tool to prepare board presentations, without recognizing that the data constitutes material nonpublic information subject to strict handling requirements.
How to detect and manage shadow AI
Addressing shadow AI requires visibility before governance. Trying to enforce policies you cannot see is ineffective, and blanket bans usually push usage underground, making the problem harder to track rather than fixing it.
Detecting shadow AI
Network and traffic analysis. The classic starting point is monitoring outbound traffic for connections to known AI service domains — chatbots, AI coding assistants, image generators, transcription tools. Secure web gateways, DNS filtering logs, and firewall data can reveal which AI endpoints employees are reaching and how often. The caveat from our earlier discussion applies: This catches stand-alone AI tools reasonably well, but misses AI features embedded inside already-sanctioned SaaS.
CASB and SaaS discovery tools. Cloud access security brokers (CASB) and SaaS management platforms can inventory which cloud applications are actually in use across the organization, flag new ones, and increasingly maintain AI-specific catalogs that classify services by risk level (e.g., if a tool does or does not train on customer inputs). This helps uncover both dedicated AI tools and AI capabilities inside existing apps.
Data loss prevention at the point of egress. Modern DLP can inspect what’s being pasted or uploaded into web forms and flag sensitive patterns — source code, customer PII, financial records — heading toward AI services. This shifts detection from “which tools are used” to “which data is leaving,” which is closer to the actual risk. Browser-level controls and enterprise browser extensions are an increasingly popular enforcement point here, since so much shadow AI happens in a browser tab.
Endpoint and identity signals. Endpoint management can reveal installed AI applications and browser extensions. Identity providers can show OAuth grants employees signing into AI tools with corporate credentials leave a trail in SSO and consent logs, which is one of the more reliable detection signals available.
Expense and procurement review. Simple but effective: Recurring charges to AI vendors on corporate credit cards often identify paid tools that did not go through formal security review.
Managing shadow AI
Start with policy. An effective acceptable use policy (AUP) for AI is specific and practical: which categories of tools are approved, which data classifications may and may not be entered into them, and what to do when someone wants a new tool. Vague “use good judgment” policies fail; so do blanket bans, which just drive usage onto personal devices and personal accounts where visibility drops to zero.
Provide sanctioned alternatives. This is the single most effective management lever. Shadow AI is a demand signal — people want these capabilities. Organizations that deploy enterprise-grade AI tools (with contractual data protections, no training on inputs, audit logging, and SSO integration) see shadow usage drop substantially because the approved path is genuinely good. If the sanctioned option is worse than the free consumer tool, the shadow persists.
Tier your response by risk, not by tool. Rather than a binary allow/block list, mature programs classify use cases: low-risk uses (brainstorming with public information) get a green light, medium-risk uses require an approved tool, and high-risk uses (regulated data, customer PII, material nonpublic information) are restricted regardless of tool. This keeps governance proportionate and credible.
Establish ownership and governance. Many organizations are standing up cross-functional AI governance committees (security, legal, privacy, IT, and business representatives) to maintain the approved tool catalog, review new requests, track regulatory developments, and keep the policy current as the landscape shifts.
Train for the why, not just the what. Employees who understand why pasting a customer contract into a free chatbot is risky — retention, training data, irreversibility — comply far better than those handed a list of banned URLs. Short, scenario-based training tied to real job tasks outperforms annual compliance modules.
Monitor continuously and iterate. The AI tool landscape changes monthly. Detection inventories, the approved catalog, and the policy all need a review cadence, because a one-time cleanup will be stale within a quarter.
Apply technical enforcement. Policy without enforcement is a suggestion. Inline security controls that can inspect, allow, block, or log AI-bound traffic give organizations the ability to act on policy in real time. Data loss prevention rules applied to AI-destined sessions can prevent sensitive data from leaving before it becomes a problem.
Build a lightweight AI intake process. Create a defined path for employees to request approval for new AI tools. Promote it actively. When employees have a clear official channel, they are less likely to work around it. Time-limited approvals (6 to 12 months) work well given how rapidly AI tools evolve.
How Akamai can help with the threats of shadow AI
Akamai Workforce Protector (formerly LayerX) is an interaction security platform that governs how users and AI agents behave inside applications, not just what traffic reaches the network perimeter. Because shadow AI risk lives in the session (the prompts that employees type, the data they paste, the files they upload, among others), network-level controls alone leave a significant gap. Akamai Workforce Protector closes it.
Discover shadow AI across your environment. Workforce Protector maps AI tool usage across web apps, browser-based tools, and desktop applications, including generative AI features embedded inside SaaS products that employees already use. Security teams get a complete, identity-attributed picture of which tools are in use before writing a single policy.
Govern prompts and responses in real time. Once AI usage is visible, Workforce Protector applies adaptive, context-aware policies to the interaction itself: warn, redact, block, or guide based on what data is being shared and with which tool. This lets organizations permit approved AI use while preventing sensitive data from reaching tools that have not been reviewed.
DLP coverage that reaches where files do not. Traditional DLP tools inspect file transfers. Workforce Protector extends that coverage to text input, copy/paste actions, uploads, and downloads inside AI applications, capturing the file-less data paths that most endpoint and network controls miss.
Lightweight deployment, no network redesign required. Workforce Protector works through a browser extension across all major web browsers, including Google Chrome, Microsoft Edge, Mozilla Firefox, and Apple Safari. There is no proxy to deploy, no traffic redirection, and no changes to existing network architecture. It also covers bring your own devices (BYOD) and unmanaged devices, enforcing read-only sessions and watermarking where full control is not possible.
Audit trail with identity attribution. Every prompt, data exchange, and user action is captured with identity context. This gives security and compliance teams the audit record they need for incident response, regulatory review, and governance reporting, none of which are available when AI use happens outside sanctioned channels.
Akamai Workforce Protector gives security teams the means to say yes to AI without losing sight of what employees are doing with it. Rather than forcing a choice between access and control, it governs the interaction layer directly, where shadow AI risk actually occurs.
Frequently Asked Questions
Shadow IT is the broader category covering any technology used within an organization without IT approval. Shadow AI is its AI-specific subset. Shadow AI introduces additional risks: uncontrolled data ingestion by external models, the potential for submitted inputs to train public AI systems, lack of auditability in AI-generated outputs, and the risk that unvetted models may be biased or manipulated.
GenAI traffic has grown sharply year over year, and the average enterprise now uses dozens of AI applications, many of which were never approved by IT. AI tools actively consume and process the data fed into them, which makes shadow AI a more direct data risk than most shadow IT. GenAI-related DLP incidents have grown in step, now representing a measurable share of all enterprise DLP alerts.
Effective detection combines SaaS discovery tools, DNS and network traffic analysis, endpoint telemetry, and access log review. Inline security platforms that inspect encrypted traffic can identify connections to known AI provider infrastructure, including embedded AI features within approved SaaS tools that may not appear in standard application inventories. Employee surveys and clear acceptable use policies also surface usage that technical tools may miss.
Most security practitioners advise against blanket bans. Outright prohibition tends to push usage underground, where it is harder to detect and manage. A more effective approach combines clear acceptable use policies, approved AI alternatives that meet employee needs, a lightweight tool-request intake process, and technical inline controls that enforce policy without eliminating legitimate productivity use.
Shadow AI can trigger violations across multiple data privacy and security frameworks, including GDPR, HIPAA, CCPA, the EU AI Act, the NIST AI Risk Management Framework, and regional laws such as India’s DPDP Act. Organizations in heavily regulated sectors (financial services, healthcare, legal) face the greatest compliance exposure from unmanaged employee AI use.
Shadow GPT is an informal term for the unauthorized use of ChatGPT or similar large language models in a workplace setting. It is one of the most common forms of shadow AI: an employee using a publicly available AI chatbot to process work-related data without IT approval or security review.
Akamai Workforce Protector discovers shadow AI usage across web, browser, and desktop tools, then governs the interaction in real time: controlling prompts, blocking sensitive data from reaching unapproved AI platforms, and capturing a full audit trail with identity attribution. It deploys as a browser extension with no network redesign required and covers managed and unmanaged devices.
Why customers choose Akamai
Akamai is the cybersecurity and cloud computing company that powers and protects business online. Our market-leading security solutions, superior threat intelligence, and global operations team provide defense in depth to safeguard enterprise data and applications everywhere. Akamai’s full-stack cloud computing solutions deliver performance and affordability on the world’s most distributed platform. Global enterprises trust Akamai to provide the industry-leading reliability, scale, and expertise they need to grow their business with confidence.
