How to Detect Shadow AI and Enforce Governance for NHIs

Securing AI integrations is critical because a single well-intentioned deployment can create an unreviewed access path into your core systems.

Until recently, shadow AI was most often used to characterize employees pasting sensitive data into public chatbots. While that risk still exists, it no longer drives the most serious shadow AI incidents. Today’s real impact is operational as agentic AI systems replace passive chatbots. Teams are embedding AI agents directly into workflows, granting them credentials, and allowing them to act autonomously on production systems.

Consider a ticket triage agent introduced to reduce a backlog. It begins by summarizing requests, but quickly expands to resetting passwords, updating multifactor authentication (MFA), or closing tickets after validation. To move fast, the team reuses a service account or OAuth app with pre-existing access to the directory or ITSM APIs.

The agent inherits these accumulated permissions. On the surface, nothing appears wrong. There is no new server to patch. No obvious misconfiguration. Productivity improves, and the identity looks like another integration.

Yet, a non-human actor now operates with standing privilege and no defined lifecycle. It authenticates using existing credentials, calls downstream services, and makes changes across ITSM, IAM, directories, and SaaS platforms. Because its activity appears as routine API traffic rather than an interactive privileged session, it often falls outside controls designed for human access. If a credential / token leaks or the agent is manipulated, the damage is determined by its permissions—not by the AI model itself.

This blog explains how to find these identities, assess their risk, and bring them under practical privileged access governance.

Shadow AI is a type of shadow IT that specifically involves any AI capability—tools, models, APIs, agents, plugins, connectors, or workflows. Shadow AI operates outside approved governance for data access, identity, privilege, ownership, or lifecycle management.

The concern isn’t the AI model, but how it’s introduced and controlled. What matters is whether its access paths, credentials, and behavior are visible, owned, and governed like any other system acting inside the environment.

Shadow AI Examples

Examples of shadow AI manifest in four distinct forms, each introducing access that must be governed:

• Shadow Tools: End-user interfaces (chat applications, browser extensions, or local assistants) that process business information without organizational AI governance.
• Shadow Integrations: Direct connections to external or embedded AI services, including LLM API calls, copilots, or SaaS features, granted OAuth scopes or API access to enterprise systems.
• Shadow Workloads: Self-hosted or developer-managed components, including models, vector databases, or inference services, deployed as AI workloads outside standard infrastructure reviews.
• Shadow Agents and Automations: Workflows or services that use delegated credentials to query systems, modify records, or trigger operational processes.

In practice, risk rarely resides in the model itself. It resides in the components that make AI operational: embedded API keys, connectors with broad OAuth scopes, vector stores holding sensitive data, and orchestration layers that carry credentials across enterprise systems. These elements function as non-human Identities (NHIs) with delegated authority, yet they are typically introduced outside identity governance and are rarely subject to access reviews.

The goal is not to catalog every AI experiment, but to classify what exists so it can be assigned ownership and governed according to the level of access it holds.

Evaluating shadow AI risks requires teams to understand that not every AI integration constitutes a crisis. Some are low-risk productivity experiments. Others are admin-equivalent automation in disguise. Treating all AI activity as equally dangerous leads to noise rather than control.

Use this simple model to help your teams evaluate what actually matters:

1) Data Exposure: What can it read? If the identity accesses sensitive customer records, HR data, source code, or directory attributes, organizations face a material confidentiality impact and an increased risk of AI-driven insider threats. If the identity can access sensitive or regulated data, the confidentiality impact is already material.

2) Action Authority: What can it change? (e.g., reset passwords, modify group membership, deploy code, alter configurations, trigger SOAR playbooks). Read-only agents pose a data risk. Write-capable agents are an operational risk.

3) Privilege Depth: What level of authority does it operate under? Are permissions narrowly scoped, or does it inherit broad delegated permissions or administrative rights?

4) Execution Scope: How far can those permissions reach in practice? Can it act on a single object, or enumerate and operate across entire datasets, directories, or environments without additional approval?

5) Auditability: Can its actions be traced and reconstructed? Logs should show which identity acted, what it accessed or changed, which inputs drove the action, and when it occurred. If an activity cannot be tied to a specific identity and transaction, investigation, rollback, and accountability will become difficult.

This model turns shadow AI risk into measurable exposure. A summarization tool with read-only access to low sensitivity data has limited impact, while an automated agent running under an over-permissioned identity can create enterprise-wide risk, even if its original purpose seemed routine.

Many detection efforts start in the wrong place. Teams search network logs for AI domains or ask employees which tools they use. That adds context, but rarely exposes risk. Shadow AI becomes risky when it creates or expands identities with access. Detection should start in the identity security control plane, where credentials, permissions, and delegated authority are created and enforced.

1. Start with Identity Graph Analysis

Map non-human identities to close the agentic AI security gap. Security teams must review how developers introduced these identities, the privileges they inherited, their baseline behavior, and whether they underwent a formal access review process.

Specifically, monitor:

• Newly created service accounts or service principals outside normal provisioning
• OAuth applications with broad or mismatched scopes
• Programmatic identities without a clear business owner
• Automation accounts calling IAM, directory, or security APIs

Look for tokens without clear owners, layered or inherited roles, and spikes in API activity that don’t align with known workflows. Shadow AI often appears as a new identity pattern rather than a new application.

2. Correlate Secrets Telemetry

AI integrations rely entirely on credentials, making secrets telemetry an essential anchor to discover and govern AI agent identities. Security teams should look for indicators of AI workload activity, such as:

• Sudden API key creation
• Credentials added to repositories or CI/CD variables
• Vault access anomalies tied to automation identities
• Long-lived tokens reused across environments

Every workload calling an external model or embedding service is powered by a credential. That credential should be traceable.

3. Inspect Development Artifacts

Some shadow AI instances never appear as deployed services. It enters through code repositories, build pipelines, or packaged dependencies. Look for external model SDKs, embedding libraries, or direct API integrations added without identity review. These integrations often introduce new credentials or reuse existing service identities long before security teams see runtime activity.

4. Analyze Authorization Patterns, Not Just Presence

The question is not only which identities exist, but how they act. Look out for:

• High-frequency privilege use
• Cross-domain invocation chains (e.g., an agent moving from ITSM to IAM to cloud APIs)
• Automation identities modifying directories or access controls
• Runtime permission elevation tied to workflow execution

Shadow AI frequently chains actions across systems, and those behavioral links are detectable even when the tooling isn’t.

5. Map SaaS-to-System Connectivity

Because many AI capabilities are delivered through the cloud, visibility must extend to include the interconnected web of SaaS permissions. Inventory third-party integrations and delegated access paths by reviewing:

• Granted OAuth scopes compared to the actual business function
• Offline or persistent access that avoids reauthentication controls
• Integrations operating without a defined business or technical owner

Many shadow AI exposures originate within SaaS platforms, where activity is trusted by default and rarely visible to endpoint or network monitoring tools. New AI capabilities are often enabled by reusing existing OAuth grants rather than introducing new identities, allowing them to bypass provisioning, access review, and risk assessment.

6. Align Identity Discovery with IT Asset Management

Shadow AI tends to proliferate where identity governance is mature, but asset governance is fragmented. Security teams may track credentials precisely, but still lack clarity on the workloads or services those credentials support. ITAM solutions close this gap by mapping identities to accountable systems, lifecycle status, and ownership. This connection helps organizations distinguish sanctioned automation from orphaned experimentation before risk accumulates.

7. Build a Minimum Viable Inventory

For each discovered AI-related identity, record only the attributes required to govern it, such as:

• Accountable owner
• Systems and data it can access
• Identity type such as an OAuth app, service account, or API key
• Permission scope and actions it can perform
• Logging coverage and ability to revoke or rotate credentials

Effective shadow AI management requires security programs to turn inventory building into actionable control. Triaging AI capabilities is what turns visibility into control. Use this framework to focus on what drives risk:

Block: No accountable owner, access to sensitive data, broad privileges, or weak visibility. Revoke tokens, disable the integration, rotate secrets, and remove access until it can be properly evaluated.

Contain: Clear business value exists, but access is excessive or poorly governed. Reduce scopes, separate identities by function, enforce logging, and apply just-in-time (JIT) access for sensitive operations. Containment is not approval; it is risk reduction while governance catches up.

Sanction: Named owner, least privilege, observable activity, and a defined lifecycle.
Formally register the capability, assign review cycles, and monitor continuously. Sanctioned does not mean permanent; it means governed.

Capabilities should be prioritized based on whether they can change the system state. Tools that only generate or summarize information present data exposure risk. Anything that can read internal systems introduces credential risk. Anything that can modify identity, access, or configuration must be treated as privileged automation.