Key shadow AI detection strategies

Shadow AI detection is becoming an essential part of managing the rapidly expanding use of AI tools in the workplace. As this trend accelerates and employees make these tools an integral part of their daily workflows, many are also turning to options outside of approved company platforms.

A recent IBM-sponsored study found that 80% of US office employees already use AI tools in their roles, yet only 22% rely solely on company-provided options. The gap is even clearer among the younger generation: 35% of Gen Z employees said they would be likely to use only personal AI tools, compared to 14% of other age groups. These numbers demonstrate the quick emergence of shadow AI and the importance of detecting unapproved AI tools.

What is shadow AI?

Shadow AI is similar to shadow IT, which refers to the use of tools, including SaaS applications and web-based platforms, without the approval of an organization’s IT department. In the same way, shadow AI refers to the use of unsanctioned AI technologies in the workplace. Employees may see company-provided AI solutions as insufficient, too slow, or simply unavailable, and instead turn to personal AI services or other unapproved tools.

How does shadow AI happen?

AI offers clear productivity benefits, so employees are naturally quick to adopt it in their daily work. An IBM survey found that 75% of US office workers report moderate to significant productivity gains from AI tools, while nearly one-third say these tools save them up to 6 hours per week.

That is why shadow AI often begins with everyday tasks. One employee may use a personal ChatGPT or Claude account to summarize documents, draft emails, or generate ideas. Another might paste sensitive data into a chatbot while preparing a financial report. These scenarios are becoming increasingly common as AI tools are often free, browser-based, and embedded into existing platforms.

The challenge is that such solutions rarely go through procurement, security, or compliance reviews. As a result, they can quickly create blind spots for the organization.

Some examples of shadow AI

Even though we touched on possible examples of shadow AI in the above section, let’s discuss a few more that you can expect to see in the office:

• A sales manager uses a personal ChatGPT account to refine a proposal before sending it to a prospective client. To make the draft more relevant, they paste in pricing details, discount strategies, and even notes from previous conversations. The task feels routine, but the company has no visibility into how that information is processed or stored.
• An HR specialist turns to a public AI assistant to help draft performance feedback and summarize interview notes. In doing so, they include employee names, candidate details, and internal assessments. The tool speeds up the work, but the data never passes through approved internal systems.
• A finance employee uses an AI chatbot to help summarize quarterly results and prepare commentary for leadership. To save time, they enter figures from private files and ask the tool to turn them into a polished narrative. Because the tool is not company-approved, sensitive business information is exposed outside governed channels.
• The development team adds a generative AI feature to an internal workflow tool to automate support responses or documentation. The update is pushed quickly because it does not require major infrastructure changes, but it also bypasses formal security, compliance, and procurement review.
• A marketing team enables an AI-powered writing assistant built into their project management platform to generate campaign briefs and client summaries. The feature requires no installation, just a toggle, so it skips security and procurement review. Behind the scenes, project data, client names, and strategic notes are processed through third-party AI models the organization has never evaluated.

Each of these examples feels practical and productivity-driven. That is exactly why shadow AI is so easy to miss: its usage often looks harmless in the moment, even as it creates visibility, governance, and data protection risks behind the scenes.

Why is it hard to detect shadow AI?

In many cases, shadow AI detection is difficult because unauthorized AI activity looks almost identical to legitimate day-to-day work, especially as AI capabilities become embedded across SaaS platforms, apps, APIs, and browser extensions. What appears to be a normal workflow, app interaction, or API call may in fact involve an unapproved model or external inference service operating behind the scenes. Because these interactions often pass through trusted browser sessions, encrypted SaaS traffic, API gateways, and third-party integrations, security teams may have limited visibility into where data is processed, stored, or retransmitted.

For instance, traditional data loss prevention (DLP) tools are designed to detect and prevent sensitive data from being exposed, exfiltrated, or shared in unauthorized ways. However, shadow AI often operates in environments where traditional DLP has limited visibility. These tools are typically better suited to more obvious events, such as file transfers, large data movements, or bulk downloads, than to small but sensitive pieces of information entered into AI prompts. A few lines of source code, customer data, financial figures, or internal strategy notes pasted into a chatbot may not trigger conventional thresholds, even though the risk can be significant.

This makes unsanctioned AI use much harder to spot than other forms of unauthorized software activity. Static rules, known indicators, and allow-or-block lists often lack the behavioral and contextual insight needed to determine whether AI usage is approved, risky, or even visible at all. As AI becomes more deeply woven into ordinary business operations, organizations need more dynamic detection strategies that can identify AI-related activity across users, applications, services, and infrastructure.

The risks of undetected shadow AI

As AI becomes more deeply embedded in everyday work, the need for shadow AI detection is growing along with the risks these undetected tools can introduce. Here are some of the key risks associated with shadow AI:

• Data leakage and exposure of sensitive data. Employees may paste confidential information into AI tools without realizing how that data will be stored, processed, reused, or passed to other systems. The same risk applies to AI features embedded in SaaS environments, where sensitive prompts, files, or outputs may move outside approved controls.
• Unauthorized actions through connected systems. The risk of shadow AI agents goes beyond data exposure. These agents often have permissions to summarize, send, update, or move data through connected apps and APIs. When deployed without approval, they can take actions on behalf of users, such as forwarding sensitive documents, modifying records, or triggering workflows, all outside the organization’s control. This turns a data-sharing concern into a broader operational risk where unauthorized actions may go unnoticed until real damage is done.
• Compliance and regulatory violations. If personal, financial, healthcare, or other regulated data is processed through unapproved AI tools or AI agents, organizations may face violations of frameworks such as GDPR, HIPAA, or industry-specific requirements. This can lead to legal exposure, regulatory scrutiny, and financial penalties.
• Expanded attack surface. Every unsanctioned AI tool introduces another potential point of exposure. These systems may not meet internal security standards, creating blind spots, hidden dependencies, and new pathways for attackers.
• Financial and reputational damage. A single incident involving shadow AI, such as a leaked client document or exposed internal strategy, can result in direct financial loss and long-term reputational harm. Trust from customers, partners, and stakeholders can be difficult to rebuild.
• Operational inconsistency and unreliable outputs. When different teams rely on different unapproved AI solutions, the quality and accuracy of outputs can vary widely. This increases the risk of decisions being made based on incomplete, inaccurate, or noncompliant information.
• Loss of governance and oversight. Undetected shadow AI makes it harder for organizations to know which AI tools or AI agents are being used and what data is being shared. Without that visibility, effective governance becomes much more difficult.

How to detect shadow AI

Detecting shadow AI requires more than looking for well-known tools or blocking a list of domains. In many cases, the real signals appear in the identities, credentials, integrations, and behaviors behind everyday workflows.

1. Analyze identity patterns

Start by mapping non-human identities. This means that security teams have to review introduced identities, the privileges they inherited, and whether they went through a formal access review process.

Some of the clearest indicators that something is up include newly created service accounts or service principals outside of standard provisioning, OAuth apps with excessive scopes, programmatic identities that lack clear ownership, automation accounts interacting with IAM or directory APIs, and unusual surges in API activity. In many environments, shadow AI is more likely to reveal itself through unexpected identity behavior than through a clearly visible application.

2. Correlate secrets and credential activity

AI integrations typically rely on credentials to access models, APIs, SaaS platforms, and internal systems. Because of that, credential-related activity can be a useful signal for detecting shadow AI. Warning signs may include newly created API keys, credentials added to code repositories or CI/CD variables, long-lived tokens reused across environments, or unusual vault access tied to automation. If an AI-related workload is running in the environment, the credentials supporting it should be visible and linked to a clear owner and legitimate business need.

3. Inspect development artifacts

Not all shadow AI first appears in runtime environments. In many cases, it enters through code repositories, build pipelines, packaged dependencies, or developer workflows long before security teams see it in production. Reviewing these development artifacts can reveal external model SDKs, embedding libraries, direct API integrations, and other AI-related components added without formal approval. This helps organizations identify shadow AI earlier, before it becomes deeply embedded in applications or internal services.

4. Monitor authorization behavior

Detection should focus not only on whether an AI-related identity exists, but also on its behavior. Shadow AI often reveals itself through unusual authorization patterns, such as high-frequency privilege use, runtime permission elevation, automation identities modifying directories or access controls, or chained actions across systems. For example, an AI-driven workflow might move from an IT service platform to identity systems and then to cloud APIs in a single sequence. These behavioral links can expose shadow AI even when the tool itself is hard to see directly.

5. Map SaaS and third-party integrations

Shadow AI often takes hold in SaaS environments where AI features are embedded in trusted applications or connected through delegated access. That is why organizations should take inventory of third-party integrations, monitor persistent offline access, and identify integrations with unclear ownership. Because these capabilities are commonly enabled through existing SaaS permissions, they can blend into normal application activity and bypass traditional provisioning and review processes.

How to govern shadow AI after detection

Detecting shadow AI is only the beginning, as organizations must then decide how to govern unapproved AI tools based on both their business value and their potential risk.

Some cases should be blocked immediately, especially if they involve broad privileges, sensitive data access, weak visibility, or no clear owner. That may require revoking tokens, disabling integrations, rotating secrets, and removing access paths created outside of approved processes. Other cases may be better suited for containment, where access is restricted, identities are separated by function, and permissions are reduced to only what is necessary. Lastly, if an AI solution has a clear owner, a legitimate business purpose, appropriate access, and sufficient visibility, then it can be formally sanctioned and monitored through an established review process.

Effective governance also relies on strong access and credential controls, as well as reliable audit trails that show which identity acted, what was accessed, and what was changed. Without clear ownership, continuous visibility, and lifecycle management, shadow AI can remain a risk long after it has been detected.

How NordLayer can help

NordLayer can help reduce shadow AI risk by giving organizations more visibility into and control over access to company systems, apps, APIs, and data. With ZTNA and identity-based access controls, access can be granted based on verified identity rather than broad network access, helping ensure that only trusted users and devices can reach internal resources.

It can also help limit data exposure by restricting access to only the resources required for a given user or workflow. Through network segmentation, organizations can isolate critical systems, define access at both the user and resource levels, and prevent lateral movement by default, making it harder for unauthorized AI activity to spread.

To reduce the risk of data leakage through AI tools or AI agents, NordLayer supports DNS filtering, which blocks categories of AI services or restricts access to trusted ones only. Combined with the NordLayer Browser, this helps create a more secure SaaS tool usage and adds controls, such as copy-and-paste restrictions, to protect against unauthorized data transfers.

Additionally, NordLayer can help organizations stay ahead of AI-related risks by reducing unknown external exposure and securing access to company resources. Together with NordLayer Intelligence by NordStellar, it can help identify exposed assets, avoid unnecessary public exposure, and validate security posture before misconfigurations turn into incidents.

Lastly, if an account, system, or AI workflow is compromised, NordLayer also helps contain the impact quickly. Organizations can isolate affected systems, revoke access immediately, and manage least-privilege controls from a centralized point, making response faster and limiting the spread of risk across the environment.