Shadow AI has climbed to the top of many security and governance risk concerns, and for good reason. But the phrase itself is slippery: different teams use it to mean different things, and the detection tools being marketed as ‘Shadow AI detectors’ often only catch a narrow slice of the problem. That mismatch creates confusion for security and compliance teams, and business leaders who only want one thing: reduce data leakage, regulatory exposure, and business risk without strangling the organization’s ability to innovate.

In this piece, we examine the common categories of Shadow AI, explain how organizations commonly try to detect each one, and evaluate how well those methods work and what tradeoffs they introduce. For the purposes of our breakdown, we’ll assume your organization already has an AI usage policy (or a list of approved and prohibited tools), though for many companies that policy ranges from “use at will” to “no AI allowed.”

Shadow AI categories

Grouping Shadow AI into clear buckets helps you understand the inherent risks of each and choose the right detection and remediation strategies. Each category has different technical footprints, detection surfaces, and governance levers.

Air-gapped Shadow AI

• What it is: Someone uses an AI tool on a non-company device or completely outside corporate systems, for example, running ChatGPT on a personal phone or pasting proprietary content into a consumer AI app from home. Even if they can’t directly connect their organization’s systems to the AI, they may still email data out of their company’s infrastructure (emailing, screen shots, manual transcription, etc.), to then feed it onto an AI system on another device.
• Why it’s dangerous: In this instance, they are using non-corporate systems, almost always against company policy, and are leaking data into various AI tools. This is both the most egregious type of Shadow AI, and the hardest to detect, with significant risks of data exfiltration and intellectual property leakage with a very low technical footprint on corporate systems. It’s the hardest to detect because the activity can occur entirely off your network, especially in remote and hybrid workplaces, and with non-company credentials.
• Typical examples: Recording a confidential call on a personal phone and using AI to transcribe and summarize it or copying sensitive text into a consumer chat window to summarize or rewrite it.

Shadow Vendors

• What it is: Unapproved third-party AI tools or models are used on corporate devices, corporate networks, or connected to corporate systems (e.g., tools that integrate with Google Drive, Slack, SharePoint). This includes unauthorized autonomous agents or self-serving integrations developers add without approval. A key difference here compared to ‘Air Gapped’ shadow AI is that the organization may have an expanded view and set of options for detecting this use, and for mitigating it. Organizations can often implement restrictions for directly integrating tools without permission, can scan network traffic, or scan single-sign on logs for traces of this shadow use.
• Why it’s dangerous: Direct integration can create continuous data flows out of corporate systems, introduce malicious agents, or create credentials/token theft attack vectors. 
• Typical examples: A marketing analyst installs a “productivity” plugin that has agentic access to the company’s GDrive and Slack; an engineer deploys a third-party model in the cloud that was not sanctioned.

Out-of-policy use

• What it is: Employees use approved AI platforms, but in ways that violate organizational policy. For instance, using a sanctioned enterprise chat tool to draft privileged legal documents, or inputting high-risk customer data into an otherwise approved assistant can introduce significant risk.
• Why it’s dangerous: The platform may be secure, but the use case still creates risk. These violations may be intentional (speed, convenience) or accidental (lack of awareness of subtle policy boundaries).
• Typical examples: Customer support agents pasting PII into a general assistant; legal teams asking an approved LLM to draft privileged client contracts.

Shadow content (undisclosed AI output)

• What it is: Deliverables or artifacts such as reports, client slides, legal briefs, and marketing copy are generated or materially produced by AI but not disclosed as such. Even when generated via approved tools, failure to disclose can create regulatory, contractual, or reputational issues and obscure accountability.
• Why it’s dangerous: Undisclosed AI use can obscure provenance, bypass necessary human review, and lead to inaccurate or non-compliant output reaching customers or regulators. Detection is technically hard because text can be human-edited and watermarking approaches are brittle.
• Typical examples: An analyst delivers an AI-generated financial model without flagging it for validation; consultants submit presentations substantially created by a chat model.

How to attempt to detect Shadow AI and the tradeoffs

Given the challenges with Shadow AI, many organizations have turned to various methods for trying to detect, and prevent use of, these AI tools. While many companies have various forms of tools, they often fall into a few broad categories. Below are the most common detection approaches, what they catch, and their blind spots.

Network scanning (VPN / proxy / packet inspection)

• What it does: Monitors corporate network traffic or VPN logs to flag connections to known AI service domains (e.g., chat.openai.com) or detect AI-like traffic patterns via deep packet inspection (DPI.)
• Strengths: Good at finding web-based and cloud vendor access from company devices and networks. Relatively straightforward to integrate into existing security stacks.
• Limitations: Cannot see activity on personal devices or when employees use personal networks. Domain-based detection is brittle (subdomains, CDNs, new vendors, or encrypted traffic can evade it). DPI raises privacy concerns and may require complex rules to limit false positives.

MLOps/code repo scanning

• What it does: Integrates with MLOps platforms (AWS Bedrock, Azure AI, etc.) and code repositories (GitHub) to find unapproved models, indicators like specific libraries or API calls, and model artifacts in cloud environments.
• Strengths: Effective for detecting embedded/unauthorized AI within engineered systems or development pipelines. Very useful for developer and engineering teams.
• Limitations: Doesn’t catch non-engineered uses (e.g., business users copying text into a consumer tool), and it requires coverage of all cloud accounts and repositories. Also needs policy logic to differentiate legitimate experimental projects from risky production systems.

SSO/login logs

• What it does: Uses identity provider logs to see third-party services users logged into with corporate credentials.
• Strengths: Clear visibility when enterprise credentials are used to access an AI platform; low effort if SSO is enforced.
• Limitations: Won’t catch personal logins or vendor access using non-corporate emails. Can be evaded when users use alternative authentication flows or bypass SSO. Requires strict SSO enforcement to be reliable.

Content scanning (watermarks/statistical detectors)

• What it does: Scans deliverables for telltale statistical patterns or embedded watermarks that indicate AI generation.
• Strengths: When effective, can directly flag outputs that require review, regardless of where the original generation occurred.
• Limitations: Current detectors are imperfect — false positives can be disruptive and false negatives are easy to craft with human edits or paraphrasing. Watermarking is an evolving standard and is inconsistent across modalities and vendors.

AI firewalls (input/output scanning)

• What it does: Proxies or guards that sit in front of approved AI systems to inspect, block, or redact high-risk inputs and outputs (e.g., prevent PII or intellectual property from leaving the environment).
• Strengths: Highly effective for preventing out-of-policy usage when the user is routed through managed models; can block prompt injection and other misuse.
• Limitations: Only helps for sanctioned systems. It won’t catch off-platform or air-gapped usage. It also requires ongoing tuning of detection rules and may break legitimate workflows if too strict.

Device scanning and MDM

• What it does: Mobile Device Management (MDM) and endpoint monitoring inspect company devices for indicators (browser history, installed apps, downloaded model files) or enforce controls.
• Strengths: Can detect multiple forms of Shadow AI when devices are corporate-owned and centrally managed. Useful for identifying local model downloads or suspicious application installs.
• Limitations: Cannot detect actions on personal devices. More invasive device scans raise privacy and legal concerns; aggressive scanning can harm morale and attract compliance scrutiny.

Physical surveillance

• What it does: Cameras, screen monitoring, or extreme remote monitoring to catch people using personal phones or external devices.
• Strengths: One of the few ways to identify true air-gapped activity in certain high-risk environments.
• Limitations: Highly invasive, controversial, and only appropriate in narrow, legally justified contexts (e.g., certain regulated labs, some government or defense operations). Not a scalable or desirable solution for most workplaces.

Matching the mitigation approach to the threat

Shadow AI is a problem that won’t be solved any time soon; if anything, the volume is likely increasing as AI proliferates across more tools and form factors. There is no one fool proof solution or platform able to detect all forms of shadow AI, and oftentimes the best approach requires deep access to systems, creating significant privacy and security risks. Most organizations will need to deploy a variety of tools and approaches to detect each type of AI use, often extending existing security platforms to do so. 

Here are a few techniques and considerations you should consider to help address each category of Shadow AI:

• Air-gapped Shadow AI: Network tools and device scanning have limited reach. The only reliable detection methods are behavior and process controls (policies, training, physical controls in high-risk contexts), or extreme surveillance — none of which are perfect. Mitigation is mostly about reducing incentives and enabling safe, convenient alternatives.
• Shadow vendors: Network scanning, SSO logs, and MLOps/repo scanning are strong here. If you enforce SSO and monitor cloud/MLOps inventories, you can surface most unauthorized integrations.
• Out-of-policy use: AI firewalls and content/input scanning are effective when users operate through sanctioned platforms. Pair this with strong logging and automated rule-based enforcement.
• Shadow content: Content scanning + provenance metadata (signed artifacts, traceable watermarks) plus process controls (mandatory disclosure fields in deliverables) is the best approach. But technical detection will miss many cases; governance and auditability matter most.

Governance and mitigation: an operational playbook

Because Shadow AI is a people problem as much as a technical one, the most effective programs combine policy, people, and layered technical controls.

Define risk tiers and map controls to risk

Classify AI use cases (low, medium, high risk) based on data sensitivity, regulatory exposure, decision criticality, and potential for automation of privileged actions. Apply stricter monitoring and onboarding requirements to higher-risk tiers.

Make governance frictionless

A common cause of Shadow AI is slow, onerous approval processes. Create a fast, three-tier approval workflow (e.g., auto-approved for low risk, light review for medium risk, formal review for high risk). Provide templates and guardrails so teams can comply quickly.

Provide sanctioned alternatives

Offer a safe, easy-to-use sandbox with preapproved tools, templates, and data handling rules. If the sanctioned option is convenient and fast, people are far less likely to go outside it.

Layered technical controls

Apply defense in depth:

• Enforce SSO for enterprise tools and integrate login monitoring.
• Use AI firewalls and redacters for sanctioned systems.
• Scan code repos and MLOps environments for shadow models.
• Use device management for corporate-owned endpoints with transparent policies.
• Deploy rights-based access to sensitive data and token-rotation for API keys.

Education, incentives, and culture

Train employees on what constitutes risky AI use and why. Communicate quickly when policies change. Reward teams for following the approval path and highlight success stories to position governance as enabler, not a veto authority.

Audit, detection, and response

Log everything you can from SSO, API calls, model queries (where allowed), all the way to content provenance, and use analytics to detect anomalies. Create an incident playbook for confirmed Shadow AI discoveries: contain, assess data exposure, notify stakeholders, remediate, and iterate policies.

Measure what matters

Track leading and lagging metrics: number of sanctioned vs. unsanctioned AI requests, average approval time, number of data-leak incidents, volumes of high-risk inputs blocked by the AI firewall, and employee satisfaction with the approval process.

Privacy, legal, and cultural tradeoffs

• Privacy vs visibility: Increasing detection (DPI, device scans, content inspection) raises employee privacy concerns and regulatory constraints (GDPR, local privacy laws). Define narrow legal bases for monitoring, minimize data retention, and apply role-based access to logs.
• Trust vs enforcement: Heavy-handed monitoring erodes trust. Position governance as safety enablement: fast approvals, well-documented exceptions, and clear rationales.
• Operational cost: Comprehensive detection and response is expensive and requires ongoing tuning. Prioritize controls against your highest-risk use cases.

Key takeaways

• Shadow AI is not a single problem: Treat it as a set of related but distinct risks like air-gapped use, shadow vendors, out-of-policy use, and undisclosed AI content, with each needing different controls.
• There’s no silver bullet: Detection will require a layered approach: identity and network controls plus MLOps visibility, AI firewalls, and most critically, process and cultural change.
• Make compliance easy: Low-friction, business-friendly approval and sanctioned sandbox environments reduce the incentive for Shadow AI. Most users will follow the sanctioned path if it’s fast and useful.
• Balance is essential: Aggressive surveillance may detect more Shadow AI but will damage trust, raise privacy risks, and invite legal scrutiny. Governance should reduce risk while preserving innovation.
• Measure and iterate: Build KPIs, run periodic audits, and refine your technical rules and processes as AI capabilities and vendor ecosystems evolve.

Shadow AI exists because people and teams see tangible productivity gains from AI, and they’re willing to take shortcuts to realize them. That drive to experiment is not inherently bad. The governance challenge is to channel that energy: provide fast, safe, and well-instrumented ways to experiment and scale, while applying stronger safeguards where the stakes are high. When governance becomes an enabler rather than a blocker, organizations dramatically reduce Shadow AI risk and unlock AI’s potential in a sustainable, scalable manner.