Shadow AI detection means systematically finding AI tools employees use without IT approval, across network traffic, browser activity, endpoints, and identity logs. Shadow AI Detection: How to Find Unauthorized Models is essential for organisations concerned about data security. Because personal accounts and local models can bypass network filters, effective detection requires a layered approach: map traffic, audit endpoints, review identity events, and pair every finding with a governance response.
Standard perimeter controls catch only part of the problem. A structured, multi-layer detection process is the only way to see what is actually running in your environment.
What Is Shadow AI and Why Does It Keep Spreading? (Shadow AI Detection Starts Here)
Shadow AI refers to any AI tool, model, or agent that employees use for work without explicit IT or security approval. It is the AI-specific subset of shadow IT, but it carries higher stakes because AI tools process language, code, and data in ways that are hard to reverse once information leaves the organization.
The organizational pressure driving shadow AI is straightforward. Employees face productivity demands, approved tools are sometimes slow to procure or too limited for specific tasks, and free or low-cost AI services are a browser tab away. According to IDC’s 2025 report, 39% of employees in the EMEA region use free AI tools for work, and another 17% pay for their own AI subscriptions out of pocket.
Common shadow AI examples include consumer chatbots used to draft internal memos, code assistants installed as IDE plugins without security review, image generators processing proprietary design assets, and browser-based summarization tools that receive pasted contract text. Each of these represents a data flow that security teams cannot see, log, or control.
In practical terms, shadow AI is the gap between the tools your organization has sanctioned and the tools your workforce is actually using. That gap is where data leaks, compliance violations, and unauthorized automation live.
What Are the Real Risks of Shadow AI Security Gaps?
Shadow AI security failures tend to cluster around two problems: data leaving the organization and regulatory exposure from undocumented AI use.
On the data side, the risk has grown faster than most security teams anticipated. IDC reports that the proportion of sensitive corporate data uploaded to AI tools grew from 10% to more than 25% within a single year. Employees paste customer records, source code, financial projections, and legal documents into AI prompts without realizing those inputs may be used to train external models or stored on servers outside corporate control.
The compliance picture is equally serious. Regulations like the EU AI Act require organizations to maintain an inventory of AI systems they deploy or use. HIPAA requires covered entities to ensure that any tool handling protected health information meets specific safeguards. An employee using an unsanctioned AI tool to summarize patient notes or process claims data creates a potential HIPAA violation that the organization cannot remediate if it does not know the activity occurred.
Agentic risk adds another dimension. Shadow AI is no longer limited to chat interfaces. Employees now deploy autonomous agents that connect to APIs, browse the web, and execute multi-step workflows. These agents take actions, not just produce text, so the blast radius of an unauthorized model extends well beyond a single data disclosure.
The sections below walk through a four-layer detection process that covers each of these risk areas.
How to Detect Shadow AI: A Four-Layer Framework
No single detection method covers the full surface area of shadow AI. A complete approach spans four layers, each catching what the others miss.
The four layers work as follows:
- Network traffic analysis. Inspect DNS queries, TLS Server Name Indication (SNI) fields, and traffic volume patterns to identify connections to known AI service domains. This catches most cloud-based tools employees use on managed devices and corporate networks.
- Browser and endpoint telemetry. Browser extensions and endpoint detection and response (EDR) agents surface AI tool usage that occurs through personal accounts or HTTPS tunnels that network inspection cannot decrypt. This layer catches what network scanning misses on managed devices.
- Identity and SSO log review. OAuth grants, single sign-on (SSO) authentication events, and third-party app authorization records reveal AI tools employees have connected to their corporate identity, even when those tools are accessed outside the corporate network.
- Governance and remediation. Detection without a response plan produces a list, not a program. Each finding needs a triage decision: block, sanction, or monitor. This step converts raw detection data into a managed inventory.
The sections below walk through each layer in the order you would execute it.
Step 1: Map Network Traffic to Identify AI Service Connections
Network traffic analysis is the fastest way to get an initial picture of AI service usage across your environment. It works by inspecting the metadata of outbound connections rather than their content, so it operates without breaking encrypted sessions.
Start with DNS query logs. AI services like OpenAI, Anthropic, Cohere, and dozens of smaller providers use consistent domain patterns. Pulling DNS logs from your recursive resolvers and filtering for known AI service domains gives you a list of which services are being reached and from which internal IP ranges. This takes minutes to set up if you already collect DNS telemetry.
Next, review TLS SNI fields in your firewall or network detection logs. When a device opens an HTTPS connection, it announces the destination hostname in the SNI field before the session is encrypted. That hostname is visible in plaintext and tells you exactly which AI endpoint a device is contacting, even if you cannot read the payload. [S1]
Traffic volume and timing patterns add a third signal. AI inference calls tend to produce bursty, asymmetric traffic: small uploads (the prompt) followed by larger downloads (the response). For example, in a SIEM you might flag any internal host that makes more than 20 outbound connections to an AI service domain within a single hour as a threshold worth investigating.
Network detection and response (NDR) tools, secure web gateways (SWGs), and cloud access security brokers (CASBs) all operate at this layer. Each covers a different slice: NDRs focus on east-west and north-south traffic patterns, SWGs intercept outbound web traffic, and CASBs specialize in cloud application visibility and policy enforcement. Using more than one category gives you broader coverage.
One purpose-built option at this layer is Corelight, which identifies over 80 distinct AI services using more than 100 domain-pattern rules and enriches Zeek logs with metadata to flag unauthorized data movement. Per corelight.com, it can detect AI traffic even when routed through aggregation layers like OpenRouter, though it relies on network-level signals and will not catch local AI usage that does not communicate externally.
Network scanning gives you the broadest initial sweep, but it has a hard ceiling. Move to the next layer before drawing conclusions about total shadow AI exposure.
Step 2: Extend Detection to the Browser, Endpoint, and Identity Layer
Network monitoring covers managed devices on the corporate network. It does not cover employees who use personal accounts, connect through a mobile hotspot, or run a locally installed model that never makes an external call. Netskope Threat Labs found that 60% of users continue to use personal, unmanaged AI applications rather than enterprise-sanctioned tools. That majority is largely invisible to network-only detection.
Three additional layers close those gaps.
Browser telemetry. A managed browser policy or a browser extension deployed through your endpoint management platform can log which web-based AI tools employees interact with, regardless of the account they use. This catches personal-account usage of tools like ChatGPT or Claude that would appear as legitimate HTTPS traffic to a network sensor. Some organizations use data loss prevention (DLP) rules at the browser layer to flag when sensitive content is pasted into an AI prompt field.
Endpoint detection and response (EDR) telemetry. EDR agents record process execution, file access, and network socket activity at the device level. They surface locally installed AI tools, IDE plugins with embedded model calls, and desktop applications that bundle AI inference. A developer running a local large language model (LLM) on their workstation produces no external network traffic, but the EDR agent sees the process and the GPU utilization. Review your EDR for processes associated with known local model runtimes such as Ollama or LM Studio.
Identity and SSO logs. When employees connect an AI tool to their corporate identity, they generate an OAuth grant or an SSO authentication event. Reviewing your identity provider’s application authorization records reveals which third-party AI tools have been granted access to corporate accounts, calendars, email, or documents. This is particularly relevant for shadow AI in Microsoft environments, where tools integrated through Microsoft 365 OAuth scopes appear in the Azure AD enterprise application list. Auditing that list regularly surfaces AI integrations that bypassed formal procurement.
Run all three layers in parallel with your network sweep. Together, they give you coverage across managed devices, unmanaged devices on the corporate network, and corporate identity connections made from anywhere.
Step 3: Prioritize and Triage What You Find
After running all four detection layers, you will have a list of unauthorized AI tools. Not every finding carries equal risk, and trying to remediate everything at once stalls the process. Triage by three factors: the sensitivity of data the tool can access, the size of the user population using it, and whether it falls within a regulated scope such as HIPAA or the EU AI Act.
The table below gives you a practical decision framework.
| Risk Factor | Low | Medium | High |
|---|---|---|---|
| Data sensitivity | Public or internal-only data | Confidential business data | PII, PHI, financial records, source code |
| User population | One or two individuals | A team or department | Cross-functional or org-wide |
| Regulatory scope | No regulated data involved | Adjacent to regulated workflows | Directly handles regulated data |
| Recommended action | Monitor and log | Assess for sanctioning or block | Block immediately, investigate, notify compliance |
A shadow AI tool used by one employee to summarize public news articles sits at the low end of every dimension. An unsanctioned AI tool that a billing team uses to process patient records sits at the high end of all three and warrants immediate action.
When you identify a high-risk finding, document the tool name, the user population, the data types involved, and the detection method before taking any remediation step. That documentation becomes your compliance evidence and your starting point for the governance response in the next step. For tools in the medium range, check whether a sanctioned alternative already exists or whether the tool could be approved with appropriate controls. Many shadow AI tools become sanctioned tools once security reviews them and employees agree to use corporate accounts instead of personal ones.
Step 4: Build a Governance Response That Doesn’t Push Users Underground
A governance response turns your detection findings into a durable program by pairing each block or sanction decision with a workable alternative for employees. Without that pairing, employees who lose access to a blocked tool simply switch to a personal device or a different unsanctioned service. Governance turns that list into a durable program. Without a clear response, employees who lose access to a blocked tool simply switch to a personal device or a different unsanctioned service, and you are back where you started.
Start by offering a sanctioned alternative before you block anything. If employees are using an unapproved AI writing tool, find out why the approved option is not meeting their needs. Procurement friction, feature gaps, and slow approval cycles are the most common reasons shadow AI spreads in the first place. IDC’s 2025 report found that 39% of EMEA employees use free AI tools for work and 17% pay out of pocket, which signals that demand is real and will not disappear because you blocked a domain. Give people a path to the capability they need, and most will take it.
Next, publish an acceptable-use policy that names specific categories of AI tools, not just a blanket prohibition on “unapproved software.” Employees need to know what they can use freely, what requires approval, and what is off-limits regardless of use case. A policy that is too vague gets ignored; one that is too restrictive gets routed around.
Then run a short training session for the teams where shadow AI use was highest. Frame it around data risk, not punishment. Show employees what happens when customer records or source code enter an external model’s training pipeline. Concrete examples change behavior more reliably than policy documents alone.
Finally, set a review cadence. Shadow AI is not a one-time problem. New tools appear weekly, and employee behavior shifts. Schedule a detection sweep every 90 days and revisit your sanctioned tool list at the same interval. Governance that does not refresh becomes irrelevant within a quarter.
When You Need Continuous Agent Monitoring Beyond Point-in-Time Detection
Continuous agent monitoring closes the gap that point-in-time detection leaves open: it watches what AI agents do after you find them, not just whether they exist.
Autonomous AI agents present a harder problem. An agent built on LangChain, Copilot Studio, or OpenAI can call external APIs, read files, and take multi-step actions across systems. A quarterly sweep will not catch an agent that exfiltrates data between scans or drifts from its original purpose over time.
Trust3.ai is an enterprise AI security platform that finds every AI agent running in your environment, including agents no one approved, and traces every prompt, tool call, and decision those agents make in real time. It enforces purpose-based access controls and generates audit trails for regulations like the EU AI Act and HIPAA. For environments where agents are already running, agent discovery is what closes the gap that network scans and identity logs cannot reach.
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FAQs
What is the difference between shadow IT and shadow AI?
Shadow IT covers any technology employees use without IT approval: SaaS apps, storage services, communication tools. Shadow AI is a subset of that category, limited to AI tools, models, and agents. The distinction matters because AI tools process and sometimes retain the content you give them, creating data-leakage risks that most traditional shadow IT controls were not designed to address. Sanctioning a file-sharing app is also simpler than evaluating whether an AI model’s training data policies meet your compliance requirements.
Can shadow AI exist on-premises or only in the cloud?
Shadow AI can exist entirely on-premises. Employees can download and run local large language models using tools like Ollama or LM Studio on a workstation or a server inside your network. These models produce no external network traffic, so they are invisible to network-layer detection. Endpoint detection and response (EDR) telemetry is the primary way to find them: look for known model runtime processes, unusual GPU utilization, and local model file paths in your EDR data.
How often should organizations re-run a shadow AI detection sweep?
Run a full detection sweep at least every 90 days. New AI services launch constantly, and employee behavior shifts as tools gain popularity. Between scheduled sweeps, keep continuous monitoring active at the network and identity layers so that new connections to AI services surface in near real time rather than waiting for the next quarterly review. Organizations in regulated industries should align their sweep cadence with their existing audit cycles.
Does blocking unauthorized AI tools reduce employee productivity?
Blocking without offering an alternative does reduce productivity, and it often pushes employees to personal devices where you have even less visibility. The more effective approach is to triage findings, sanction tools that pass a security review, and block only those that present unacceptable risk. When you do block a tool, communicate why and name the approved alternative. Employees who understand the risk and have a workable substitute are far less likely to route around controls.
What regulations specifically require organizations to inventory AI systems?
The EU AI Act requires organizations that deploy or use AI systems to maintain documentation of those systems, including their purpose, risk classification, and the controls applied to them. In the United States, HIPAA requires covered entities and business associates to ensure that any tool handling protected health information meets specific technical and administrative safeguards, which presupposes knowing which tools are in use. Sector-specific frameworks such as NIST AI RMF and the FTC’s guidance on AI also treat inventory and documentation as foundational governance requirements.
Conclusion
Shadow AI detection is a multi-layer process, not a single scan. Network traffic analysis gives you the broadest initial view, but browser telemetry, endpoint data, and identity logs are what close the gaps that personal accounts and local models create. Once you have findings, triage by data sensitivity and regulatory scope, then pair every block or sanction decision with a governance response that gives employees a workable alternative.
The practical next step is to pull your DNS query logs and your identity provider’s OAuth grant list this week. Those two sources alone will surface most cloud-based shadow AI in your environment. If autonomous agents are already running on agentic frameworks in your environment, add continuous agent monitoring to your detection stack before the next quarterly sweep.
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