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Shadow AI is an organization’s use of artificial-intelligence applications or agents without appropriate IT or security visibility, approval, ownership, or governance. It ranges from an employee pasting confidential text into an unapproved public chatbot to an unregistered software agent operating inside company systems. Agents raise the stakes because they can read data, call tools and services, and take actions with limited human intervention. The practical answer is not to ban every AI use: discover what exists, provide approved options, limit data and permissions, require people to approve consequential actions, and keep complete logs.
What is shadow AI?
Microsoft uses “shadow AI” for AI tools—especially generative-AI tools—used without the knowledge, approval, or governance of an organization’s IT or security teams. The term is vendor guidance rather than a universal legal or technical standard, but the shared idea is clear: AI is operating outside the controls the organization is supposed to apply.
Shadow AI has two overlapping forms. The first is ordinary employee use of a public or otherwise unsanctioned application. The second is an AI agent deployed in the organization’s environment without registration, an assigned owner, an approved identity, or policy controls.
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An employee who uses an unapproved writing assistant, coding service, image generator, transcription service, or chatbot may be creating shadow AI even if the service produces only text or images. Copying a customer record, source code, contract, incident report, health information, or internal strategy into a prompt can place that information under a service’s external processing, retention, and access rules. The actual treatment depends on the provider and configuration; it is not accurate to say that every provider trains on every submitted prompt.
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Unmanaged agents
An agent is shadow AI when it can operate in the organization but nobody has formally recorded what it does, which data it can reach, which tools it can invoke, who owns it, or how it is stopped. An agent created by a developer for a useful internal task can therefore become an unmanaged production dependency if it is never brought into identity, access, security review, and change-management processes.
Using an approved AI service under organizational policy is not automatically shadow AI merely because it is generative AI. The distinction is whether the organization can see, evaluate, control, and audit the use.
Why employee use creates organizational risk
Confidential information can leave intended controls
Prompts and uploaded files can contain information that the organization expected to remain in approved systems. Depending on the service’s settings, data may be processed, retained, or made available to administrators or connected features outside the organization’s intended boundary. A service-specific review should establish retention, deletion, training use, regional processing, subcontractors, and access terms before sensitive data is allowed.
Visibility and evidence disappear
When staff choose applications independently, security teams may not know which services are in use, which accounts hold the data, or how long interactions persist. That makes it harder to investigate an incident, reproduce an AI-assisted decision, satisfy a records request, or demonstrate compliance. Microsoft identifies sensitive-data leakage, regulatory noncompliance, and reputational harm as possible consequences.
Unreviewed decisions can affect customers and employees
An unsanctioned tool can influence a hiring recommendation, support response, financial analysis, code change, or eligibility decision without a documented model, review process, or reliable audit trail. The problem is not that an AI system helped; it is that the organization cannot account for the system it did not know was involved. Microsoft’s governance guidance puts the point this way: “The risk isn’t that employees use AI; it’s that an organization cannot account for the AI it doesn’t know about.”
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Policy alone will not reveal demand
Shadow use can indicate that employees have a legitimate need for faster drafting, analysis, translation, or automation and cannot find an approved route. Discovery and enforcement therefore need to be paired with usable sanctioned services, clear data rules, and a process for requesting an exception or new capability.
What are the risks of AI agents at work?
NIST describes an AI agent as software that uses data and algorithms to perform tasks autonomously. Unlike a chat-only application that normally waits for a person to copy an answer into another system, an agent can plan, execute, and adapt; invoke tools and APIs; access several datasets; and coordinate with services or other agents. Each connection adds another place where a mistake, malicious instruction, excessive permission, or compromised dependency can have an effect.
Standalone tools and agents are not the same risk
The following comparison is a practical synthesis of NIST and Microsoft guidance, not an official numerical risk score. Actual exposure depends on the service, configuration, data, and business process.
| Risk axis | Standalone employee tool | Connected or autonomous agent |
|---|---|---|
| Data sensitivity and breadth | Usually receives the text, files, or images a person deliberately submits. | May retain ongoing access to datasets, messages, documents, or application records needed for its tasks. |
| Permissions and connected tools | Often limited to the application account and its built-in features. | Can invoke APIs, databases, mail, ticketing, cloud, or business applications; every connection expands the attack surface. |
| Autonomy and external effects | Typically returns an output for a person to review and use. | Can plan and execute steps, change records, send messages, trigger workflows, or otherwise affect the real world. |
| Human review and stopping | A person commonly remains in the interaction loop. | Review may be delayed or absent, so high-risk actions need explicit approval gates and a reliable pause or stop mechanism. |
| Identity, ownership, and lifecycle | Use may be associated mainly with an employee account or an external provider account. | Needs a distinct identity, named owner, permission review, change control, and retirement process. |
| Logging and auditability | Provider logs may be incomplete or outside organizational retention and investigation systems. | Actions, tool calls, approvals, errors, and relevant inputs should be centrally logged so investigators can reconstruct what happened. |
Task deviation and weak oversight
An agent can satisfy the literal wording of a task while violating its business intent, especially when a plan spans several tools. A small interpretation error can propagate through later steps. If nobody reviews intermediate decisions, the organization may discover the deviation only after a message is sent, a record is changed, or a transaction is initiated.
Limited intelligibility
Complex agent workflows can make it difficult to explain why a particular tool was called or why data was selected. Without recorded prompts, plans, tool arguments, results, and approvals, an organization may be unable to distinguish a model error from a user instruction, a permissions problem, or an attack.
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Sensitive-data leakage
An agent may combine information from sources that were separately protected, then place the result in a message, file, ticket, or external service. Least-privilege data access and data-loss-prevention controls should be applied to the agent’s reachable data and to every destination it can write to.
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Supply-chain compromise
Agents often depend on models, plugins, connectors, libraries, prompts, and hosted services. A compromised component can alter behavior or capture information without changing the organization’s business workflow. Components and updates need ownership, provenance checks, and review.
Agent sprawl and excessive permissions
Agents created for experiments can remain active after the original project ends. Duplicated identities, abandoned connectors, and broad permissions increase the chance that an old agent becomes an unnoticed route into sensitive systems. Inventory, ownership, expiry dates, and periodic access reviews limit this accumulation.
How indirect prompt injection hijacks an agent
Indirect prompt injection hides malicious instructions inside content an agent is asked to read. The content may look like an ordinary email, file, web page, support ticket, or document, but it contains text designed to redirect the agent. If the agent treats those instructions as authoritative, it may disclose data, call an unintended tool, alter records, or send a harmful message.
NIST’s Center for AI Standards and Innovation (CAISI) describes this as agent hijacking: “Currently, many AI agents are vulnerable to agent hijacking, a type of indirect prompt injection in which an attacker inserts malicious instructions into data that may be ingested by an AI agent, causing it to take unintended, harmful actions.”
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CAISI reported a bounded evaluation on January 17, 2025. In an upgraded Claude 3.5 Sonnet agent tested on selected AgentDojo Workspace tasks, the measured attack success rate ranged from 11% for the strongest baseline attack to 81% for the strongest new attack. These figures describe that particular model, task set, and evaluation; they are not an estimate of the proportion of deployed agents that will be hijacked.
Testing should therefore be adaptive and task-specific. CAISI recommends evaluating attack performance for the tasks an agent actually performs and using multiple attempts where appropriate, because a single pass can miss attacks that succeed intermittently.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A staged governance plan for shadow AI and agents
Governance works best as a sequence. Each stage produces information or control needed by the next, while allowing useful, low-risk AI adoption to continue.
1. Discover applications, agents, and data flows
- Inventory AI applications, browser extensions, integrations, agents, service accounts, users, devices, and environments.
- Use network, identity, endpoint, cloud, and application telemetry to find services that employees or scripts actually use; a policy document alone will miss unregistered activity.
- Record what data each tool receives, where outputs go, which tools an agent can call, and whether a human is present for each action.
Microsoft’s discovery guidance treats application discovery as a way to surface shadow use that ordinary approval lists do not show.
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2. Assess the use case and service
For each discovered tool or agent, document the business purpose, data categories, users, countries or regions involved, retention and data-use terms, access controls, audit capabilities, compliance obligations, and expected business value. Consider security, legal exposure, data sovereignty, and the availability of company information to the service. A low-sensitivity drafting task and an agent that can modify payroll records should not receive the same review.
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3. Provide an approved route and clear rules
Publish a short list of approved services and the tasks they support. State which information may be entered, which information must be excluded or de-identified, whether outputs require human verification, and which actions are prohibited. Give employees a way to request a tool or agent that is not yet approved. A sanctioned option that meets real needs reduces the incentive to work around controls.
4. Constrain data, tools, and identity
- Apply data-loss-prevention and access policies to prompts, uploads, agent memory, outputs, and destinations.
- Give each agent only the minimum datasets, tools, and operations required for its stated task.
- Assign a durable, identifiable agent identity and a named human owner; do not rely on an individual’s personal credentials for unattended work.
- Review permissions when the agent, connector, model, or business process changes, and retire identities that no longer have a valid purpose.
NIST’s National Cybersecurity Center of Excellence announced a concept-paper and project direction on February 5, 2026, focused on software-agent identity and authorization, including identification, authorization, auditing, non-repudiation, and prompt-injection mitigation. It is not a completed binding standard, but it highlights the controls an organization must design for.
5. Keep people in control of consequential actions
Require explicit human approval before an agent performs an irreversible, high-impact, external, or financially significant action. Approval screens should show the proposed operation, data used, destination, and relevant tool arguments rather than asking for a vague “continue.” Provide a dependable pause and stop mechanism that does not depend on the agent cooperating.
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Log agent identity, owner, model or version, inputs that matter to the decision, plans, tool calls, results, approvals, failures, and final effects. Alert on unusual data volume, new connectors, privilege changes, repeated failed actions, or attempts to override instructions. Retain records according to organizational policy and applicable requirements, and make them available for incident investigation and compliance review.
7. Reassess continuously
Models, connectors, agent instructions, business data, and attack techniques change. Recheck permissions and ownership on a defined schedule and after material changes. Re-run task-specific security evaluations when an agent gains a tool, changes model, or moves into a higher-impact workflow. Remove or suspend agents that cannot meet the organization’s evidence, approval, or containment requirements.
Implementing controls without locking into one vendor
The underlying controls—discovery, data-loss prevention, identity, least privilege, approval gates, monitoring, and retention—can be implemented with different products and architectures. Microsoft’s April 3, 2026 deployment guidance names Microsoft Purview, Microsoft Defender for Cloud Apps, Microsoft Entra, and Microsoft Intune as components of its own recommended model. Treat those names as vendor-specific examples, not universal requirements, and verify current licensing and capabilities before deployment.
For smaller teams, a spreadsheet-backed inventory, approved-service standard, separate agent identities, narrowly scoped API credentials, manual approval for external actions, and centralized logs can establish the same control objectives before a larger platform is available. The important test is whether the organization can answer who uses an AI system, what it can see, what it can do, who approved it, and what happened when it acted.
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- Purpose: the business task and the owner accountable for outcomes.
- Scope: users, systems, datasets, connectors, regions, and environments.
- Permissions: read and write operations, approval thresholds, and expiration dates.
- Data handling: retention, deletion, training-use terms, destinations, and sensitivity restrictions.
- Human controls: review points, escalation contacts, and an emergency stop procedure.
- Evidence: interaction and action logs, evaluation results, incidents, changes, and retirement date.
This record turns an unknown AI dependency into a governed system that can be reviewed, limited, and removed when its risk exceeds its value.
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