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Enterprise AI adoption is becoming harder to inventory than traditional SaaS. An employee can install a coding agent locally, a developer can connect an MCP server to an IDE, a business team can build an agent in a low-code platform, and an engineering group can deploy a custom autonomous workflow
in the cloud. None of these activities necessarily passes through a centralized AI procurement process.
8 Shadow Agent Discovery Platforms
1. Dash Security
Dash Security is an agentic security platform designed to discover and govern the complete agentic layer across an organization. Its discovery model extends beyond browser-based AI usage to agents operating across endpoints, containers, cloud
environments, enterprise applications, IDEs, and command-line interfaces. Dash profiles agents alongside the infrastructure that extends their capabilities, including MCP servers, skills, plugins, extensions, and agent platforms. Importantly for shadow-agent discovery, the platform covers both
sanctioned and unsanctioned tools rather than requiring agents to be registered before they become visible.
Dash also identifies sanctioned tools being accessed through personal or otherwise untrusted accounts, as well as dormant installations that can remain part of the organization’s attack surface.
Key features
- Discovery of sanctioned and shadow AI agents
- Coverage across endpoints, browsers, IDEs, CLIs, containers, cloud, and enterprise applications
- Inventory of MCP servers, skills, plugins, and extensions
- Identification of personal or untrusted account usage
- Agentic session reconstruction across environments
- Attribution of activity to human and agent actors
- Agent, MCP, skill, and tool risk profiling
- Governance and enforcement after discovery
2. Noma Security
Noma Security provides AI asset discovery across cloud platforms, SaaS agent environments, developer endpoints, and code repositories. Its discovery coverage includes agents, models, MCP servers, skills, and data connections across environments such as AWS Bedrock, Azure AI Foundry, Databricks,
Vertex AI, Snowflake, Copilot Studio, and Agentforce. Noma also targets endpoint-based coding agents such as Claude Code, Cursor, and Codex and can examine development environments including GitHub, GitLab, and Azure DevOps.
This breadth addresses one of the central problems in shadow-agent discovery: agents created by business users, developers, and cloud teams frequently appear through entirely different technical surfaces.
Key features
- Cross-environment AI asset discovery
- Endpoint, cloud, SaaS, and repository visibility
- Agent and MCP registry
- Continuous detection of newly introduced agents
- Discovery of models, skills, tools, and data connections
- Agentic risk and relationship mapping
- Agent supply-chain discovery
- Support for homegrown and third-party agents
3. SentinelOne Prompt Security
SentinelOne Prompt Security provides discovery and governance across employee AI usage and agentic environments. Its Agentic AI Security capabilities are designed to map AI agents and MCP servers, including their connectors, tools, skills, and plugins. Multiple discovery mechanisms, including
browser, API, security integration, and MCP-related controls, help extend visibility beyond agents that have been formally registered with security teams.
The platform connects discovery with risk-based control. Security teams can examine who is operating an agent, understand which systems and capabilities it touches, risk-score agents and MCP servers, and establish policies governing access and actions. SentinelOne specifically addresses shadow MCP
servers and automatically discovered agent deployments, an increasingly important consideration because an approved agent can become substantially more powerful when connected to an unmanaged MCP server.
Key features
- AI agent and MCP server discovery
- Shadow agent and shadow MCP identification
- Visibility into tools, connectors, skills, and plugins
- Employee and developer AI usage discovery
- Agent relationship mapping
- Risk scoring for agents and MCP infrastructure
- Searchable agent action auditing
- Runtime policy enforcement
4. Palo Alto Networks Prisma AIRS
Palo Alto Networks Prisma AIRS provides AI discovery and security capabilities spanning agents, applications, models, data, and supporting infrastructure. Its Agent Discovery capabilities can inventory enterprise and SaaS agents and retrieve configuration information such as the model an agent
uses, its knowledge bases, and the tools available to it. For supported environments, Prisma AIRS can also analyze runtime interactions to identify relationships between agents, models, and tools.
This approach is particularly relevant to cloud-based shadow agents because it connects inventory with the configuration and dependency relationships that determine risk. Palo Alto Networks also extends discovery toward developer endpoints through Cortex Agentic Endpoint Security, addressing
coding assistants and agents with access to local terminals, repositories, and APIs.
Key features
- Enterprise and SaaS agent discovery
- Agent configuration inventory
- Model, knowledge-base, and tool visibility
- Agent interaction mapping in supported environments
- Cloud AI asset discovery
- AI agent identity and permission controls
- MCP and agent supply-chain security
- Runtime AI protection
5. Microsoft Defender
Microsoft Defender AI agent inventory provides a centralized inventory for discovering AI agents and assessing their security posture. Microsoft documents coverage for agents created through Copilot Studio, Microsoft Foundry, Microsoft 365, supported third-party environments, and local agents
discovered on endpoints. The inventory exposes information including agent configurations, risk indicators, associated tools, identities, security recommendations, and alerts.
Microsoft’s broader architecture is particularly relevant to organizations where agents increasingly behave like identities rather than conventional applications. Microsoft recommends bringing discovered shadow agents under management by registering them, assigning ownership, reducing unnecessary
access, and integrating them into a standard lifecycle.
Key features
- Centralized AI agent inventory
- Discovery of local endpoint agents
- Copilot Studio, Foundry, and Microsoft 365 agent visibility
- Support for selected third-party agent environments
- Agent identities, tools, and configuration context
- Agent risk indicators and security recommendations
- Advanced Hunting for agent inventory
- Network-based shadow AI and MCP discovery
6. Check Point AI Security
Check Point AI Agent Security combines agent discovery, posture assessment, and runtime protection. Its discovery layer connects directly to platforms where agents are created and operated, building a continuously updated inventory containing agents, their tools, and connected MCP servers.
Documented integrations include Amazon Bedrock and AgentCore, Google Cloud, Microsoft Copilot Studio, Salesforce Agentforce, n8n, and Relevance AI.
The resulting inventory feeds a posture layer that assigns per-agent risk ratings and explains the contributing factors, including relationships to tools and MCP infrastructure. Check Point’s broader Workforce AI Security capabilities add discovery across public AI applications, browser and
desktop usage, developer tools, coding agents, and MCP-connected workflows.
Key features
- Continuously updated agent inventory
- Agent platform and cloud integrations
- Tool and MCP server discovery
- Per-agent posture and risk assessment
- Shadow AI visibility across workforce environments
- Developer and coding-agent coverage
- OWASP and MITRE ATLAS risk mapping
- Runtime protection for agent interactions
7. Cisco AI Defense
Cisco AI Defense approaches AI security through discovery, validation, runtime protection, and policy enforcement across enterprise AI environments. For shadow AI use cases, network-level visibility can help identify AI services and applications being accessed outside formally approved workflows.
Its broader AI security capabilities are intended to give security teams visibility into AI applications and associated risks while providing controls over how enterprise users and applications interact with AI services. For agent discovery specifically, organizations should evaluate the depth of
visibility available for autonomous agents, their connected tools, and local developer agents rather than assuming conventional shadow-AI detection provides a complete agent inventory.
Key features
- Enterprise AI visibility
- Shadow AI discovery
- Network-level AI activity identification
- AI application risk assessment
- Runtime AI protection
- Security policy enforcement
- Integration with broader enterprise security infrastructure
8. Netskope One
Netskope One provides shadow AI visibility through its security service edge and cloud application security capabilities. Its established discovery model analyzes employee access to cloud and generative AI services, helping organizations identify which AI applications are being used even when
those applications have not been formally approved. T
Netskope is therefore most relevant when organizations want agent discovery to connect with broader controls for SaaS usage, sensitive data movement, and employee AI adoption. Enterprises with extensive coding agents, custom cloud agents, or MCP infrastructure should evaluate what complementary
endpoint or agent-specific visibility is required.
Key features
- Shadow AI and cloud application discovery
- Visibility into employee AI adoption
- SaaS application risk assessment
- Sensitive data protection
- Generative AI usage controls
- Network and cloud security telemetry
- Enterprise policy enforcement
The Four Places Shadow Agents Hide
Shadow agents do not enter enterprises through one consistent channel. Discovery strategies need to account for four distinct surfaces.
Developer Endpoints
Coding agents can run in IDEs, terminals, desktop applications, and local background processes. Developers may install them independently because adoption can require little more than an extension or CLI command.
Endpoint visibility matters because network-based discovery alone may identify calls to an AI model without revealing which local agent initiated them, which tools it can execute, or which MCP servers are configured.
Cloud and Agent Platforms
Engineering and data teams can create agents directly through cloud platforms, while business users increasingly build them using low-code agent platforms.
These agents may never appear as software installed on an employee device. Discovery instead requires access to platform configurations, cloud APIs, activity logs, or other control-plane information.
SaaS Applications
Enterprise applications are increasingly adding embedded agent-building capabilities. A department may therefore create autonomous workflows inside an already approved SaaS platform without introducing a new vendor.
Traditional SaaS discovery may report the application as sanctioned while missing the new agents created inside it.
MCP and Agent Extensions
An approved agent can acquire shadow capabilities after deployment.
A developer might connect an unsanctioned MCP server to an approved coding assistant. A new skill or plugin can similarly expand what the agent can read or modify.
The agent itself has not changed from sanctioned to unsanctioned. Its effective capability has.
This makes continuous discovery of agent extensions just as important as identifying the original agent.
A Better Agent Inventory Maps Capability, Not Just Assets
Traditional asset inventories are built around relatively stable objects: a laptop, server, SaaS application, workload, or identity.
Agentic systems are more dynamic.
An agent may be safe when initially registered and become risky when someone grants it another credential. A new MCP connection can expose sensitive data. A plugin can introduce write capabilities. A model change can alter behavior while the agent retains the same name.
A useful shadow-agent inventory therefore needs several layers of context.
|
Inventory layer |
What security needs to know |
| Agent | What is running and where? |
| Owner | Which employee, team, or service is responsible? |
| Identity | Which credentials or service identities does it use? |
| Model | Which model or provider powers it? |
| Tools | What operations can it perform? |
| MCP/skills | Which extensions expand its capabilities? |
| Data | Which sensitive information can it access? |
| Connections | Which applications, agents, APIs, and infrastructure can it reach? |
| Runtime | What does it actually do when operating? |
The last distinction is particularly important.
Configuration describes capability. Runtime describes behavior.
Security teams need both.
An agent may technically possess permission to delete cloud resources but never use it. Another may have relatively limited permissions yet execute thousands of automated actions every day. Understanding risk requires knowing both the theoretical blast radius and the observed behavior.
FAQs
How are shadow agents different from shadow AI applications?
Shadow AI applications are unapproved or unmanaged AI services used by employees. Shadow agents introduce an additional concern because they can autonomously invoke tools, access systems, execute commands, or communicate with other agents. Discovering the application being used therefore may not
reveal the agent’s complete risk. Security teams also need visibility into identities, permissions, MCP servers, tools, and runtime actions.
Why are MCP servers important for shadow agent discovery?
MCP servers can give agents access to external tools, enterprise applications, data, and actions. An approved agent connected to an unknown MCP server can therefore gain capabilities security teams never reviewed. Discovery programs should inventory MCP servers alongside agents and determine which
tools they expose, which agents use them, and what systems those tools can access.
Can CASB or SSE platforms discover shadow agents?
They can provide an important part of the picture by detecting traffic to AI services and identifying employee use of unapproved applications. However, network visibility may not reveal every locally installed agent, agent configuration, tool, or MCP server. Organizations with significant
autonomous agent adoption may need endpoint, cloud-control-plane, SaaS, and agent-specific discovery in addition to network-based shadow AI monitoring.
What information should a shadow agent inventory contain?
At minimum, organizations should identify the agent, owner, environment, model, identities, permissions, tools, and connected systems. Mature inventories can also map MCP servers, skills, plugins, data access, agent-to-agent relationships, and observed runtime behavior. This context helps security
teams prioritize agents according to their actual capabilities and potential blast radius rather than treating every discovered agent as equally risky.
Should organizations automatically block every shadow agent?
Not necessarily. Unknown agents should be assessed before they are trusted, but indiscriminate blocking can interfere with legitimate AI adoption. Security teams can classify discovered agents, assign ownership, evaluate permissions, remove unsafe connections, and establish appropriate controls.
The goal is to convert unmanaged agent adoption into visible and governed usage while preventing deployments whose capabilities create unacceptable risk.