TL;DR: AI agents are proliferating inside SaaS tools and autonomous workflows faster than identity teams can provision, review, or even detect them, leaving shadow agents outside conventional IdP, inventory, and access governance processes, according to AuthMind. The core problem is that existing identity controls assume an agent will be registered before it matters, but many now appear only in network traffic and runtime behaviour.
At a glance
What this is: AuthMind argues that AI agents embedded in SaaS and created at runtime are escaping conventional identity discovery, leaving shadow workflows outside normal governance and inventory controls.
Why it matters: IAM, IGA and security teams need to treat agent discovery as a runtime visibility problem, because provisioning-led controls miss identities that never appear in IdP records.
Context
Shadow AI agents are non-human identities that appear through SaaS features, personal credentials, or autonomous workflows without passing through the usual provisioning and review path. That breaks the basic assumption that every meaningful identity will first show up in an IdP or inventory record.
The issue is not just that more agents exist. It is that agentic workflows can spawn sub-agents, chain actions across systems, and create new runtime identities that identity teams never formally registered. For IAM and governance programmes, that means discovery is no longer an admin task at setup time; it is a continuous visibility problem.
AuthMind’s article focuses on the detection gap between what identity systems know and what is actually operating in the environment. The starting position is increasingly atypical only if an organisation already watches network behaviour alongside identity records.
Key questions
Q: Where do shadow AI agents fail identity governance first?
A: They fail at discovery, because they can be created inside SaaS tools, personal accounts, or autonomous workflows without a provisioning record or IdP event. That leaves no normal joiner artefact for IAM or IGA tools to reconcile, so the agent exists operationally before it exists formally.
Q: Why do identity tools miss AI agents embedded in SaaS?
A: Because most identity tools wait for a declared account, application inventory entry, or lifecycle event before they can classify something as real. SaaS-native agents can appear only in runtime behaviour and network traffic, which means the source of truth has to move beyond registration data.
Q: How can security teams tell a user AI tool from an autonomous agent?
A: Look at behaviour, not branding. User AI tools usually show bounded, session-oriented communication, while autonomous agents generate broader east-west activity, call APIs, access secrets, and may spawn sub-agents during execution.
Q: How should organizations approach the governance of AI agents?
A: Organizations should adopt a governance framework that incorporates continuous visibility, adaptive IAM practices, and stringent policy-based controls. This ensures that all agent actions are tracked, authorized appropriately, and assessed for compliance.
Technical breakdown
Why IdP-centric discovery misses shadow AI agents
Identity systems are designed to recognise provisioned subjects, not runtime-created ones. If an agent is created inside a SaaS platform, enabled through a personal account, or spawned by another agent during execution, there may be no joiner event, no inventory record, and no explicit identity object to reconcile. That is why provisioning records and IdP logs can be accurate yet still incomplete. The discovery problem shifts from administration to observation, because the identity exists operationally before it exists formally. In practical terms, this is a visibility failure at the control plane, not a policy failure in the workflow itself.
Practical implication: treat SaaS-native and runtime-created agents as discoverability gaps, not just offboarding gaps.
How network traffic reveals agentic AI identity behaviour
Network-level observation sees communication patterns, not declared intent. An AI agent must talk to an LLM, touch APIs, or call internal services to do useful work, and those flows create a behavioural signature even when the identity is unnamed. That makes traffic analysis useful for discovery, classification, and ownership mapping because it captures what the system actually does rather than what the platform says was provisioned. For identity governance, this is the difference between assumed inventory and observed inventory. The value is not packet inspection alone, but continuous classification of runtime behaviour across sessions and east-west paths.
Practical implication: extend discovery to runtime telemetry so AI agent inventory reflects actual behaviour, not platform declarations.
Why autonomous agents create a different discovery problem than user AI tools
Not every AI tool is operating with the same degree of independence. A user AI assistant tends to show session-oriented, bounded interaction, while an autonomous agent can delegate, spawn sub-agents, retrieve secrets, and spread activity across multiple systems in one execution chain. That matters because discovery based on human-style sessions can misclassify an agent as an ordinary user workflow. Once sub-agents and delegated actions enter the picture, the identity surface grows during execution rather than before it. The governance challenge is therefore behavioural classification at runtime, not simple device or account enumeration.
Practical implication: classify agents by observed autonomy and delegation patterns, not by the interface they were enabled through.
Threat narrative
Attacker objective: The objective is to operate AI-driven workflows invisibly inside the enterprise so they evade identity governance, ownership mapping, and security oversight.
- Entry occurs when an employee enables an AI agent inside a SaaS platform or through a personal account, often without a formal provisioning event or security review.
- Credential or identity ambiguity follows when the agent operates under a human identity or creates a new runtime identity that conventional tools do not register as distinct.
- Escalation happens as the agent chains actions, spawns sub-agents, and reaches APIs, secrets, and internal systems beyond the original user session.
- Impact is the expansion of unmanaged shadow workflows that increase attack surface, obscure ownership, and weaken governance over what is actually running in the environment.
Breaches seen in the wild
- CoPhish OAuth phishing via Copilot Studio: Datadog showed Copilot Studio agents on a Microsoft domain can front OAuth consent phishing and forward stolen tokens; no victims reported.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Discovery is becoming a runtime governance problem, not a provisioning problem: Shadow AI agents expose the limit of IdP-first thinking because some identities only become visible after they start communicating. Provisioning records can no longer be treated as the authoritative source of what exists in the environment. Practitioners need to recognise that inventory based only on registration is structurally incomplete.
Shadow agent sprawl creates a new kind of identity blind spot: The key issue is not that agents are multiplying, but that SaaS-native and runtime-created agents can exist without a durable identity record. That means governance processes built around known accounts, known owners and known lifecycle events are missing the thing they are meant to govern. The result is an inventory gap that becomes an accountability gap.
Behavioural discovery is the right control plane for agentic environments: When agents can spawn sub-agents and generate east-west traffic on their own, the important question is no longer whether a system was provisioned. It is what it actually did, what it touched, and who can be held responsible for it. The governance model has to move from declared identity to observed execution.
Runtime-created AI identities force teams to rethink ownership mapping: An agent that appears only in traffic still has a human sponsor, but the relationship is no longer expressed cleanly through traditional lifecycle records. That creates a mismatch between operational ownership and identity evidence. The practical conclusion is that accountability for AI agents must be derived from runtime evidence as much as from admin records.
Shadow AI agents sharpen the case for continuous identity observability: The article points to a broader market shift in which discovery, classification and governance converge at runtime. That is exactly where NHI programmes, agentic AI oversight and SaaS governance now overlap. Teams that still separate those disciplines will keep missing the same identities from three different angles.
From our research library:
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
- Read next: AI Agent Identity Security Buyer's Guide
What this signals
Shadow AI agent discovery is now a continuous observability problem: If agents can appear through SaaS features, personal credentials, or runtime delegation, then the inventory problem cannot be solved by recertification alone. Identity teams need evidence from traffic, not just from provisioning systems, because the latter only records what someone chose to register.
AI agent behaviour is the new boundary for governance: The important distinction is not whether a workflow uses AI, but whether it can create, delegate, and act without being fully visible in the identity stack. That is where hidden privilege, ambiguous ownership, and unmanaged access begin to overlap.
70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey: that gap becomes harder to manage when some agents never enter the inventory at all. The practical response is to align discovery, classification, and ownership mapping around runtime evidence rather than assumed registration.
For practitioners
- Extend discovery beyond provisioning records Correlate SaaS activity, IdP records, and runtime telemetry so agents that never appeared in onboarding workflows still become visible.
- Classify AI agents by observed behaviour Use behavioural signals such as east-west traffic, API calls, secret access, and sub-agent spawning to distinguish user AI tools from autonomous workflows.
- Map every discovered agent to a human owner Require an accountable sponsor for each agentic workflow, including those created inside SaaS tools or by downstream delegation chains.
- Review SaaS integrations for hidden agent creation paths Look for features that let employees enable AI functions with corporate credentials, because those paths can create unmanaged identities outside normal approval flow.
- Continuously reconcile inventory against network observations Treat mismatches between observed traffic and registered identities as investigation triggers, not as harmless noise.
Key takeaways
- Shadow AI agents are creating a visibility gap that conventional identity inventories were not built to close.
- The core weakness is not only provisioning, but the assumption that every meaningful identity will first appear in IdP records.
- Practical control now depends on runtime observability, behavioural classification, and clear human ownership for every discovered agent.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10, OWASP Agentic AI Top 10 and MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-10 — Human Use of NHI | Employees enabling AI agents through personal or corporate accounts creates ungoverned human-mediated NHIs. |
| Recommendation — Restrict human-triggered creation paths for AI agents and require explicit ownership before activation. | ||
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The article centres on agents operating under human identities and extending privilege at runtime. |
| Recommendation — Detect when agents inherit or misuse human identity context during execution and flag privilege drift. | ||
| MITRE ATT&CK | TA0007;TA0008 — Discovery; Lateral Movement | Runtime agent sprawl is discovered through behavioural traffic and can expand access across systems. |
| Recommendation — Map observed agent movement to discovery and lateral movement techniques to prioritise monitoring. | ||
| NIST CSF 2.0 | ID.AM-01 — Physical devices and systems are inventoried | The article describes a gap in complete inventory of active AI identities and workflows. |
| Recommendation — Expand inventory practices so active AI agents and their runtime behaviours are included in asset and identity maps. | ||
| NIST Zero Trust (SP 800-207) | Continuous monitoring — Continuous monitoring | Shadow agents require runtime verification rather than one-time registration checks. |
| Recommendation — Use continuous monitoring to validate identity behaviour against expected access and activity patterns. | ||
Key terms
- Shadow AI Agent: A Shadow AI Agent is an AI-driven software entity that operates outside approved governance, visibility, or control. It may access data, call tools, or make decisions without formal registration, policy enforcement, or security review. In practice, it creates hidden identity, access, data, and audit risk across enterprise environments.
- Runtime Identity: Runtime identity is the practice of making identity and authorization decisions at the moment an action occurs. For agents and workloads, it means access is validated against live context, not only against the identity state set during onboarding or provisioning. That makes accountability and scope enforcement possible inside fast-moving workflows.
- Behavioural Classification: A method of deciding whether an automated actor is allowed to perform an action by evaluating patterns of use, not just the presence of automation. It uses sequencing, consistency, and business correlation to separate approved workflows from suspicious activity.
- Identity Observability: Identity observability is a continuous governance approach that correlates identity activity with business context, telemetry, and policy state. Instead of checking access at a single point in time, it tracks what an identity can do, what it did, and why that action matters to the business.
Deepen your knowledge
NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on June 24, 2026.
Updated on October 11, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org