Shadow AI agent discovery methods compared: A guide to finding and governing your agentic workforce

Agentic AI is driving experimentation across every industry. Every employee can now build agents inside familiar tools like Microsoft Copilot Studio, Salesforce Agentforce, ServiceNow, and automation platforms like N8N and Workato, and grant those agents broad, persistent access to business data and actions.

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The result is a fast-growing blind spot: shadow agents that IT and security often do not know exist, but that can read, move, and act on sensitive data.

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Agentic AI security solutions have emerged recently to discover shadow AI agents, yet it can be difficult to compare offerings in such a frenetic marketplace. A seemingly simple question becomes difficult to answer: how do you actually discover shadow AI agents in your environment?

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The short answer is that no single discovery method or tool is perfect. Each technical approach to shadow AI agent discovery reveals a fundamentally different picture of what agents exist and where, what it has access to, who controls it, and what it's doing.

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In this guide, we'll compare various approaches to shadow AI agent discovery, how they work, and where blindspots remain.

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What is an AI agent, anyway?

This sounds like a trick question, but it's fundamental.

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Agents can describe a wide range of systems with varying levels of autonomy, from generative AI assistants that simply recommend actions a human user can take to systems that execute complex, multi-step workflows. Agent-washing makes it even harder to distinguish traditional automation workflows from true agentic capabilities.

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In short, an AI agent is a software system that uses an AI model to determine its next action toward a goal and takes autonomous or semi-autonomous (with human-in-the-loop approvals) actions through tools or APIs to execute multi-step tasks, adjusting its approach based on results and new context. Agents can be embedded in SaaS and agentic AI platforms, run locally on machines, or operate entirely through APIs.

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The business appeal is obvious. AI agents promise to automate repetitive work, accelerate development cycles, and extend what small teams can accomplish. But unlike a SaaS app that sits behind an SSO login, agents actively do things independently: they make API calls, store and retrieve data, take actions in connected systems, and sometimes spawn other agents to get subtasks done. The security implications of an unknown, unmanaged agent are considerably higher than those of an unknown SaaS app.

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What is agent discovery?

AI agent discovery is the process of identifying and cataloging all AI agents operating within your organization. This includes agents that have been formally procured and deployed by IT as well as shadow agents, the growing wave of agents that employees and developers spin up without going through any formal review or approval process.

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Much like shadow AI discovery and SaaS discovery, the purpose of AI agent discovery is to build a comprehensive and continuously updated inventory of every agent in your environment: what it is, who owns it, what systems it connects to, what permissions it holds, and what data it can access or act on.

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Effective AI agent discovery helps you answer questions that matter:

• What agents exist in our environment?
• Who created them, and who is accountable for them?
• What systems and data can they access?
• Which agents are risky (public exposure, hardcoded secrets, excessive permissions)?
• Which agents are still running after their creators leave?

These answers are fundamental to agentic AI security and governance.

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Why is agent discovery important?

Agents introduce a category of security risk that's meaningfully different from standard SaaS risk. Instead of passive tools your employees log into, they're active participants in your environment that take actions, make decisions, and interact with sensitive systems on an ongoing basis. Here's why getting a handle on them matters:

• Data exposure and exfiltration risk: Agents with broad access to files, email, or databases can exfiltrate sensitive data intentionally (by a malicious actor) or accidentally (by a poorly configured prompt). Without discovery, you won't know which agents hold this access or whether it's appropriate.
• Non-human identity proliferation: Every agent needs credentials to operate: API keys, OAuth tokens, service accounts. These non-human identities are notoriously difficult to track and are a common vector for credential abuse and privilege escalation.
• Supply chain risk: Many agents depend on third-party LLM providers, cloud-hosted models, or external APIs. A compromise upstream can have downstream consequences for your data and workflows.
• Compliance requirements: Frameworks like SOC 2, HIPAA, GDPR, and the EU AI Act increasingly require organizations to document automated decision-making systems, data processors, and AI tools that handle regulated data. You can't document what you haven't found.
• Prompt injection and model manipulation: Agents that consume external content (web pages, documents, emails) are vulnerable to prompt injection attacks, where malicious instructions are embedded in that content to hijack agent behavior. Discovery is a prerequisite for assessing and mitigating this exposure.
• Orphaned agent access: Like orphaned SaaS accounts, agents deployed by employees who leave the organization can continue to operate with active credentials long after departure, especially if they weren't registered anywhere to begin with.

In short, agents need the same rigor that IT and security teams have (slowly, painfully) built around human users, SaaS apps, and shadow AI tools. Discovery is where that work begins.

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Understanding different types of AI agents

Discovery method selection is driven by where an agent lives and how it was built. Different agent types inhabit fundamentally different surfaces, and the method that finds one type is often completely blind to another.

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The practical question is: given what you know about your environment, which surfaces are most likely to have agents on them? For example:

• If your organization is heavily invested in a major SaaS platform, such as Microsoft 365, Salesforce, or ServiceNow, embedded agent features are almost certainly already active, whether IT provisioned them intentionally or not.
• If your organization has active software development teams building on AI APIs or LLM platforms, custom-built agents are already being created.
• If your organization has empowered business users, meaning non-technical staff who routinely adopt cloud tools without IT involvement, shadow agents are likely in use across a variety of SaaS apps and automation tools, many of which don't expose agents through their APIs.

Most organizations need to run several methods in parallel, because agents exist across all of these surfaces at once. Instead of thinking about which single method to use, consider which combination reflects your actual agent risk surface and what you have the infrastructure and organizational bandwidth to run.

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One other factor shapes the decision: not every agent you can find is worth finding with equal urgency. The methods that surface high-sensitivity, high-agency agents first (tracking backward from sensitive APIs and data stores, reviewing enterprise platform configurations) will usually deliver more security value early than a broad sweep designed to find every agent regardless of risk.

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Comparing common agent discovery methods

No single discovery method provides full coverage across all agent types and environments. Here are the most common discovery layers and what they really tell you.

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Network traffic analysis (including SASE/SSE)

Network-based methods detect agents by inspecting traffic between your environment and external AI services. SASE/SSE vendors have a natural adjacency here because they're already inline for access decisions.

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Agent signal: Traffic flowed to an AI service endpoint. Destination domains, DNS patterns, volume, and timing.

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Agent context it provides: Minimal. Network analysis can tell you that something in your environment called an AI service, but it can't distinguish an autonomous agent from a human using a chatbot. It can't tell you who owns the agent, what permissions it holds, what data it accesses, or what tools it invokes. Most agent traffic runs over encrypted HTTPS, so payloads are opaque without decryption keys. Agents that operate locally, server-side, or offline generate no network traffic at all.

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What it tells you about governance: Network analysis can detect anomalous data flows and flag potential exfiltration paths. It can enforce policy at the network boundary through blocking or filtering. But it can't tell you the semantic meaning of the data in transit, the internal application context for why data was accessed, or the full permission model of the agent that generated the traffic.

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Strengths:

• Can surface active agents making external calls even if they've never been formally registered or reported to IT
• No per-app integrations required; works across agent types that make observable external calls
• Can catch rogue or unauthorized agent behavior in transit

Limitations:

• Can't see dormant agents or agents operating entirely within internal systems
• Can't distinguish an agent from a human using a chatbot
• Doesn't reveal who owns an agent, what it's authorized to do, or why it's accessing what it's accessing

Where it fits: Catching previously unknown connections to AI service providers. Best suited as an early warning signal ("something is talking to Anthropic's API") rather than a source of agent-level governance context.

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Browser extensions

Browser extensions observe web-based activity: URLs visited, pages loaded, session timing. In the context of agents, they can catch employees interacting with web-based agent builders or running browser-based AI assistants.

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Agent signal: A user visited or interacted with a web-based AI agent tool.

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Agent context it provides: Which user, which web app, and session activity. More capable implementations go further: the browser extension can capture agent-level data at the moment an employee creates or lists an agent on a supported platform, including creator, components, permissions, and risk signals, without requiring an API integration into that platform. This is the most practical way to discover agents on platforms that don't expose public APIs, which is where most shadow agent activity actually lives.

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What it tells you about governance: Browser-based discovery supports user-level governance, including acceptable use policies, data loss prevention rules, and detection of risky behaviors like pasting sensitive data into AI tools. It catches early-stage shadow AI exposure. But it has no visibility into backend or system-to-system data flows, data accessed by autonomous agents without user interaction, or full permission and infrastructure context.

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Strengths:

• Fast path to detecting shadow AI before it becomes a formal deployment
• Works without requiring API integrations into the platforms being used
• Some extensions can capture prompt-level detail

Limitations:

• Only covers managed browsers on managed devices
• Blind to desktop agents, mobile agents, API-based agents, and server-side SaaS agents
• Can't reveal what an agent is authorized to do, what data it accesses, or how it connects to other systems

Where it fits: Detecting employees using web-based agent builders like Copilot Studio, Agentforce, or similar no-code platforms through a browser. Particularly valuable for surfacing shadow AI agents on platforms that don't expose public APIs, which is the category of agent risk that most discovery tools miss entirely.

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Endpoint agents

Endpoint agents run on managed workstations and monitor local application behavior, including AI-related desktop apps, IDE extensions, and local model execution.

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Agent signal: AI-related software or agent tooling is running on a managed device.

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Agent context it provides: Which local AI tools are installed and running, and on whose machine. This catches agent development activity (ex, an engineer building agents via Cursor, VS Code extensions, or local frameworks like LangGraph) that browser extensions and network tools miss. But endpoint agents can't see server-side agents, SaaS-embedded agents, or agents in cloud environments. They also can't tell you what permissions the agent holds in downstream systems.

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What it tells you about governance: Endpoint discovery gives you visibility into the supply side of shadow agent adoption: who is building what, on which devices. But it's limited to managed devices, creates coverage gaps on unmanaged or personal devices, and provides no insight into what those agents do once they leave the developer's machine.

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Strengths:

• Catches agent development and adoption activity that network tools and browser extensions miss
• Surfaces the supply side of shadow AI: who is building what, on which devices
• Integrates with existing endpoint management infrastructure

Limitations:

• Only covers managed devices; bypassed entirely on personal or unmanaged hardware
• Can't see server-side agents, SaaS-embedded agents, or anything running in the cloud
• No visibility into permissions or what agents do once deployed

Where it fits: Catching local agent development and experimentation on managed devices. Important for understanding the origin of agents, but not their operational footprint.

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CI/CD and source code scanning

These tools scan code repositories and build pipelines for agent framework references, SDK imports, orchestration code, and API calls to AI services.

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Agent signal: Agent-related code, frameworks, or model references exist in the codebase.

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Agent context it provides: Which agent frameworks developers are using (LangGraph, CrewAI, AutoGen), which AI services they're calling, and where agent logic lives in the code. When extended to cloud platform scanning, these tools can also identify agent workloads deployed on platforms like Bedrock, Azure AI, or Vertex AI.

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But code scanning tells you about agent design, not agent behavior. It can't reveal what permissions an agent has been granted in production, what data it actually accesses at runtime, or who authorized its deployment. Agents built outside the pipeline through no-code tools or SaaS-native agent builders are invisible.

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What it tells you about governance: Code scanning supports AI Bill of Materials (AIBOM) efforts and supply chain governance. It answers "what did developers intend to build?" But it can't confirm whether production behavior matches design, whether permissions have drifted, or who is accountable for the running agent.

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Strengths:

• Finds agents before they reach production, while they're still being built
• Supports AI Bill of Materials (AIBOM) and supply chain governance efforts
• Identifies which AI services and frameworks your development teams depend on

Limitations:

• Shows design intent, not production behavior; what's in the code may not match what's deployed
• Completely blind to no-code and low-code agents built in SaaS platforms
• Can't reveal runtime permissions, actual data access, or who authorized deployment

Where it fits: Inventorying developer-built agents during development, before and during deployment. Pairs well with AIBOM efforts but doesn't replace runtime or identity-based discovery.

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Runtime and observability-based discovery

If you want to understand what agents are actually doing, you have to move closer to execution.

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Runtime-based discovery captures activity at the point where agents interact with models, call tools, and execute workflows. This is typically done through inline proxies, SDK instrumentation, or integrations with observability pipelines.

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Agent signal: Prompts, responses, tool calls, execution chains, and decision flows captured at the point of execution.

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Agent context it provides: This is the most direct way to see agent behavior. You can observe what data an agent retrieves, processes, or generates. You can see prompts and outputs involving sensitive data. You can trace tool-level data access ("queried CRM," "wrote to database," etc) and detect anomalies, misuse, or policy violations in real time. This is where discovery shifts from inference to reality.

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What it tells you about governance: Runtime observability shows whether policies are actually being enforced during execution, meaning PII redaction, prompt filtering, access controls. It detects misuse like data exfiltration or prompt injection impacts. It answers the question that most other methods can't: is the agent doing what it's supposed to?

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What it can't tell you: Full inventory of all possible data access outside instrumented environments. Ownership or permission context without correlation to identity data. And it only covers what's been instrumented, which means anything running outside observed surfaces is invisible.

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Strengths:

• Deepest visibility into what agents actually do, not just that they exist
• Best method for verifying whether an agent is behaving as intended
• Can surface misuse and policy violations that no inventory-based method would catch

Limitations:

• Only covers what's been instrumented; agents outside instrumented environments are invisible
• Requires upfront setup: SDK integration, proxy deployment, or observability pipeline configuration
• Comes with performance and privacy tradeoffs that require organizational buy-in

Where it fits: Essential when you have real agent runtime risk concerns and need step-level accountability. Best paired with identity and control plane data to close inventory and attribution gaps.

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Third-party risk management (TPRM)

TPRM systems rely on vendors to self-report their AI agent capabilities through questionnaires and contract disclosures.

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Agent signal: A vendor claims their product includes agent functionality.

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Agent context it provides: Whatever the vendor discloses: stated agent capabilities, data handling terms, and contractual commitments. But TPRM can't verify those claims, can't detect agents embedded without disclosure, and can't identify agents that vendors have added between questionnaire cycles.

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Strengths:

• Creates contractual accountability for third-party agents
• Supports supply chain governance at the policy and documentation layer
• Establishes a paper trail for compliance and audit purposes

Limitations:

• Entirely self-reported; there's no way to verify vendor claims through this method
• Point-in-time by nature; agents added between assessment cycles go undetected
• No visibility into how third-party agents actually behave in your environment

Where it fits: Contractual accountability for third-party agents. Not a substitute for technical discovery, but an important complement for supply chain governance.

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Identity-centric discovery

Identity-centric discovery finds agents by examining the credentials they use to operate: OAuth grants, API keys, service accounts, delegated tokens, and MCP connections. Rather than observing agents indirectly through traffic patterns or code references, this approach discovers evidence of agents through the access infrastructure that makes their activity possible.

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Agent signal: An agent-associated identity, such as an OAuth token, API key, service account, or MCP connection, exists with active or dormant access to enterprise systems.

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Agent context it provides: This is the method that most directly answers the questions agent governance requires. Specifically: who authorized the agent (human owner), what systems and data it can access (permission scope), whether those permissions are current, excessive, or orphaned, the delegation chains it participates in (where an agent inherits or passes credentials to other agents), and whether the agent has been properly deactivated when no longer needed.

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What it tells you about governance: Identity-centric discovery maps overprivileged access, detects orphaned agents (still running after their creator leaves), identifies stale or long-lived credentials, and enforces least-privilege policies. It directly supports the lifecycle management that governance programs require: who created this, who approved it, does it still need this access, and is anyone still accountable for it?

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What it misses: Agents that don't use identity infrastructure, such as local tools on personal devices, unauthenticated AI activity, agents that bypass standard OAuth or API-key patterns. It also depends on how well each platform's APIs expose agent identity and permission data.

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Strengths:

• Finds hidden agents through the access infrastructure they depend on
• Maps blast radius: what could this agent access if compromised?
• Most directly answers governance questions: who authorized it, what can it reach, is it still needed

Limitations:

• Assumes agents use identifiable, managed credentials; misses agents running under shared or human identities
• Many agents embedded inside SaaS platforms never create their own credentials and are invisible here
• Can't confirm actual agent behavior or whether access is used appropriately

Where it fits: The richest source of agent governance context for building an agent catalog, including owner, permissions, access scope, and lifecycle status.

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SaaS API / control plane discovery

This is worth separating from other methods because it bridges two things that are often discussed independently: agent platform integrations and identity-centric discovery.

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There's an important distinction between two types of platforms:

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Dedicated agent development platforms such as Copilot Studio, Agentforce, Bedrock Agents, Vertex AI Agent Builder, and ServiceNow Now Assist Studio are purpose-built for creating and deploying agents. Integrating with their APIs gives you agent configuration, tool access, and platform-specific metadata.

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SaaS applications with agent capabilities are a different problem. Platforms like Salesforce, ServiceNow, and Microsoft 365 are business applications that now support agent creation and operation within them. Agents embedded in these platforms can directly change systems and data, so clearly mapping their permissions and operational scope matters.

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What makes SaaS APIs a distinct discovery source is that they can surface agent-related signals even when nobody deliberately registered an agent in a governance system. SaaS platforms expose OAuth grants, third-party app integrations, API connector configurations, and service account activity through their APIs. That means querying SaaS APIs can reveal things like: an OAuth grant giving a third-party AI tool access to your SharePoint sites, a sync connector copying M365 data to an external AI service's vector database, an Agentforce agent with CRM access created by a business user without security review, or a Copilot Studio agent with broad permissions that was never offboarded after its creator left.

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Agent signal: OAuth grants, third-party app registrations, API connectors, service account configurations, and registered agents within SaaS platforms.

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Agent context it provides: This is the method that ties together "what exists" and "who authorized it and what can it do." Through SaaS APIs, you can identify not just that an agent-related integration exists, but the human user who authorized the OAuth grant, the specific data scopes the integration can access, and whether the connection is actively syncing data to an external AI service. For agents built natively within SaaS platforms, the APIs expose agent configuration, tool access, and operational scope directly.

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What it misses: Agents and AI integrations on platforms whose APIs you haven't connected to. Agents that bypass standard OAuth or API-key patterns. Shadow agents built entirely outside SaaS platforms: in custom code, local environments, or on unmanaged devices.

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Strengths:

• Fast, broad reach, low friction
• Works well for enterprise-managed agents
• Provides rich metadata (permissions, configs)

Limitations:

• Misses shadow / local / custom agents
• Only sees what platforms expose
• Weak visibility into runtime behavior

Where it fits: The connective tissue between identity-centric discovery and platform-specific agent discovery. SaaS APIs are where you find both the agents that platforms know about (registered agents) and the agent-associated identity signals that platforms expose whether or not anyone thought of them as "agents."

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How to evaluate AI agent discovery solutions

When evaluating vendors, don't just ask whether or not a provider can discover AI agents. Instead, get more specific:

1. What is your primary discovery mechanism? API, identity, runtime, network, endpoint?
2. What do you actually enumerate? Agents, identities, tools, data flows, sessions?
3. What environments do you cover? SaaS, cloud, local, browser, code?
4. What governance context do you provide for each agent? Detection without context is just an alert. Can the vendor tell you who owns it, what it can access, whether it's been approved, and whether it's still needed?
5. What do you miss? Every vendor has blind spots, and the best ones are explicit about them.

AI agent discovery with Nudge Security

Nudge Security's approach to AI agent discovery combines multiple methods, giving you the coverage and context to answer the questions security teams face first: what agents exist in your environment, who created them, what can they access, and is anyone still accountable for them? Here's what sets our approach apart:

• Shadow agent discovery from multiple vantage points: API-based discovery continuously pulls agent data from platforms that expose a public API, surfacing agent name, creator, creation date, status, components, and metadata. The Nudge Security browser extension covers the platforms that don't expose APIs at all, observing when an employee views, lists, or creates an agent in a supported platform. Meanwhile, identity-centric signals surface evidence of agents through the credentials they use to operate: OAuth grants, API keys, service accounts, and MCP connections.
• Creator-level accountability, not just platform-level visibility. Every discovered agent is mapped to the human who built it. That's what makes governance actionable rather than informational. When a creator leaves your organization, you know. When something needs remediation, you know exactly who to nudge.
• Risk insights and posture findings built for agentic AI. Nudge Security surfaces the signals that matter specifically for agents: publicly accessible agents, hardcoded credentials, unauthenticated MCP connections, integrations to high-risk apps, and agents still running after their creators have left.
• Discovery that connects directly to governance. Finding agents is only the starting point. From the same inventory, security teams can set approval status, assign a technical contact, and trigger automated nudges to creators to confirm intent and request remediation, closing the loop without becoming a bottleneck for AI adoption.
• Coverage that grows without new infrastructure. Browser-based discovery uses the Nudge Security extension most customers already deploy for SaaS and AI governance. API-based discovery connects through existing app integrations. A comprehensive agent inventory doesn't require new tools, endpoint agents, or network monitoring.