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Find shadow AI by combining several evidence sources rather than relying on a single blocklist: analyze network and Microsoft 365 traffic, match destinations to a cloud-app catalog, attribute activity to users and devices, inspect browser extensions and installed agents, and audit OAuth, API, plugin and MCP permissions. Then put every observed chatbot, copilot, model API or agent into an inventory with an owner, purpose, usage, data-transfer evidence, approval state, risk and a dated disposition. Discovery only reduces risk when it leads to a control—an approved alternative, monitoring, restriction, blocking, data-loss prevention (DLP), retention or investigation.
What is shadow AI, and what belongs in the inventory?
Shadow AI is artificial intelligence operating in an enterprise without effective governance. That includes an employee adopting an unsanctioned chatbot or browser extension, as well as an agent deployed inside the company without registration, ownership or policy controls. The common problem is not the label on the product; it is that the organization cannot account for the data the tool can access or the actions it can take.
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- Consumer chatbots and desktop assistants, including personal accounts used for work.
- Code assistants and task-specific productivity tools.
- Browser extensions that send page content, prompts or credentials to an AI service.
- AI features inside otherwise approved SaaS applications.
- Direct model-provider APIs, SDKs, keys and service accounts.
- SaaS Model Context Protocol (MCP) servers, plugins and connectors.
- Locally installed agents and internally deployed agents that were never registered.
Use explicit lifecycle states so a finding cannot remain indefinitely ambiguous: unknown, observed, under review, approved, monitored, restricted, blocked or retired.
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How do I define the scope before looking for tools?
Write the scope and decision states first. Otherwise, teams tend to count only recognizable websites and miss APIs, extensions, internal agents and AI features hidden inside existing subscriptions.
Set the populations and access paths
- People: employees, contractors, interns, administrators and privileged developers.
- Devices: managed workstations, mobile devices, virtual desktops, servers and developer environments.
- Identities: named users, shared identities, service accounts and workload identities.
- Paths: web traffic, Microsoft 365 traffic, native applications, browser add-ons, OAuth grants, API calls and internal network connections.
Define what “found” means
An application is “observed” when you have reliable evidence that a user, device or service account reached it or installed it. It becomes “under review” when an owner, purpose, permissions and data-flow assessment are pending. Approval should mean that a named business owner accepts the use case and the security, privacy and retention requirements—not merely that the URL is familiar.
Include demand as well as risk
Shadow AI findings are also demand signals. If many people choose the same unapproved service, document the job it performs and assess whether a sanctioned alternative can meet it. A governed option that is easier to use is more durable than a policy that only says no.
How can network and Microsoft 365 telemetry reveal shadow AI?
Network evidence gives you breadth: it can show destinations, users, volumes and timing even when an application has not been declared. Microsoft Entra Global Secure Access can analyze internet and Microsoft 365 traffic, identify generative-AI applications, SaaS MCP servers and model-provider frameworks, and expose users, usage, bytes sent and received, and risk scores through Application Usage Analytics.
Collect the minimum useful evidence
- Destination or application name and any observed aliases.
- First and last observed time.
- Signed-in user, device and department where available.
- Usage frequency and bytes sent and received.
- Whether the connection came from a browser, native client, API, service account or another path.
- Any catalog risk factors or policy events associated with the destination.
Keep the evidence tied to a time window. A single connection may be a test; repeated activity by a privileged account is a different investigation. Volume is a clue, not proof of sensitive content: network metadata usually tells you that data moved, not exactly what a prompt contained.
Account for blind spots
Encrypted traffic, unmanaged devices, direct API calls and traffic routed through another service can limit application identification. A network record also may not reveal which browser extension generated a request or which internal system an agent reached. Use endpoint, identity and integration evidence to close those gaps rather than treating an empty network record as evidence of no use.
How do I normalize discoveries against a cloud-app catalog?
Normalize each destination against the Microsoft Defender for Cloud Apps cloud-app catalog. Preserve the provider, category, catalog risk factors and every observed alias instead of collapsing records into a vague label such as “AI.” Consistent names let you find the same service when users access different domains, mobile clients or API endpoints.
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Keep the raw and normalized names
Store the exact hostname, URL or API endpoint in the evidence record, then attach a normalized application name and provider. Record the date and source of the catalog match. If an endpoint cannot be matched confidently, keep it as an unknown application with an analyst note; do not force it into a familiar category.
Separate provider risk from enterprise risk
A catalog score is an input, not the final decision. Your enterprise risk also depends on the permissions granted, the data classes likely to be sent, the user’s privilege, provider terms, retention behavior and the model or agent’s actions. Keep those factors as separate fields so a low catalog score cannot hide a high-impact internal integration.
How do endpoint and identity data identify the person responsible?
Correlate network findings with device inventory, installed applications, browser extensions and the signed-in identity. This catches locally installed agents that have weak network signatures and makes remediation attributable to a user, team or service owner.
Inspect managed endpoints
- List installed applications, package identifiers, executable paths and installation times.
- Review browser extension names, publishers, permissions, update channels and installation scope.
- Record the device owner, operating-system account and management status.
- Check developer environments for SDKs, model-provider keys, agent packages and configuration files.
Resolve identity and privilege
Map activity to a named user where possible, then add department, manager and privileged status. For service accounts, identify the owning team and the workload that uses the credential. A shared account without an owner is itself an inventory finding because you cannot reliably assign purpose or approve an exception.
Use the least intrusive source that answers the question. Identity and installation metadata may establish ownership without collecting message content. Escalate to deeper inspection only when the risk and legal basis justify it.
How do I audit OAuth, API keys, plugins and MCP permissions?
Integrations often create more exposure than a browser session because they can operate continuously and reach other systems. Review OAuth grants, API keys, model-provider accounts, plugins, MCP servers and service accounts, and record the external systems each can reach.
Build an integration map
- Export current OAuth applications, grants, consent records and token owners.
- Inventory model-provider accounts, API keys, SDK configurations and automated jobs.
- List plugins, connectors and SaaS MCP servers, including the tenant or workspace where each is enabled.
- For every integration, record scopes, data stores, write actions, network destinations and credential rotation or expiry.
- Link each credential or connector to a named owner and a business purpose; flag orphaned or shared credentials for immediate review.
Assess what an agent can do
Ask whether the agent can read files, send messages, create records, change code, execute commands or invoke another agent. An apparently low-risk model becomes consequential when its connector can modify a production system. Capture permissions as concrete actions rather than a single “AI risk” label.
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How can I determine what data is leaving the business?
Record the data classification likely to reach each service, bytes transferred and any prompt or payload inspection that is legally and technically appropriate. Also record whether enterprise retention exists for the interaction. Application-level usage metadata can establish that a service was used; it cannot by itself establish the content of a prompt.
Use a graduated evidence model
- Level 1—metadata: destination, identity, device, timestamps, frequency and byte counts.
- Level 2—context: application, extension, API, connector, permission scopes and business purpose.
- Level 3—content controls: DLP events or prompt and payload inspection where law, policy and technology permit.
Classify what could be sent—public, internal, confidential, regulated or secret—and distinguish likely exposure from confirmed exposure. Do not copy sensitive prompts into an inventory merely to prove a point; retain the minimum evidence needed for the decision and investigation.
What fields should every shadow-AI inventory record contain?
A useful record lets a reviewer answer five questions: what is the tool, who uses it, what can it reach, what data moves, and what decision governs it. Include these fields:
- Application or agent name, provider, URL or API endpoint and category.
- First and last observed time, access path and evidence links.
- Users, devices, department, service account and named owner.
- Usage frequency and bytes sent and received.
- Data classes likely to be sent and whether prompt or payload inspection produced an event.
- External services, integrations and permission scopes.
- Business purpose and whether an approved alternative meets the need.
- Approval status, catalog risk, enterprise risk and current control state.
- Exception expiry, reviewer and next review date.
Keep an event history when a record changes from observed to approved, restricted or blocked. That history is essential for audits and for explaining why a user’s access changed.
Which discovery approaches should I combine?
No single source covers every tool or answers every governance question. Compare approaches by the evidence they produce and the effort and privacy impact they impose.
| Approach | Coverage | Attribution and exposure detail | Typical action and trade-off |
|---|---|---|---|
| Network and Microsoft 365 traffic analysis | Internet and Microsoft 365 destinations, generative-AI applications, SaaS MCP servers and model-provider frameworks | User, usage and byte counts; usually application-level rather than prompt-level content | Fast broad discovery and traffic-based controls; encrypted, unmanaged or indirect traffic can reduce certainty |
| Cloud-app catalog matching | Known providers, categories, aliases and catalog risk factors | Normalizes names and adds provider context; does not prove your tenant’s permissions or data use | Consistent risk triage; unknown or newly launched services still require analyst review |
| Endpoint and browser inspection | Installed agents, native clients, extensions and local developer packages | Device, signed-in identity and installation evidence; content visibility depends on the endpoint control | Finds tools with weak network signatures; requires endpoint coverage and careful privacy boundaries |
| Identity and access review | Users, departments, privileged accounts, service accounts, OAuth grants and consent | Strong ownership and privilege context; does not alone show what data was sent | Enables attributable remediation; shared or orphaned identities remain difficult to govern |
| API, plugin and MCP audit | Model keys, connectors, plugins, MCP servers and external systems | Scopes, actions and reachable systems; payload visibility requires additional logging or DLP | Best for agentic and automated risk; integration inventories demand continuing maintenance |
Deployment choices affect privacy and effort. TLS inspection may be required for some traffic visibility; endpoint agents add administrative overhead; data residency and retention rules constrain where telemetry can be stored. Document those costs in the design rather than discovering them during an incident.
How do I turn an observed tool into a decision?
Route every material finding to a dated disposition. Microsoft describes a staged model: discover AI applications, block access to unsanctioned applications, block sensitive data going to sanctioned applications, then govern, audit, retain and investigate AI interactions.
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Use a decision sequence
- Validate the finding. Confirm the destination, identity, device and access path; remove duplicate aliases.
- Understand the job. Ask the user or team what work the tool performs, what data it needs and who owns the result.
- Check for a sanctioned alternative. If one meets the need, migrate the use case and set a retirement date for the shadow tool.
- Assess exposure. Combine data classes, permissions, provider terms, model behavior, privilege and usage volume.
- Choose a control state. Approve, monitor, restrict, block or retire; record the reason, owner and review date.
- Apply data controls. Use DLP or equivalent controls to stop sensitive data reaching even sanctioned applications when policy requires it.
- Preserve evidence. Retain the logs, consent, review notes and exception expiry needed for audit or investigation.
When monitoring is the right interim choice
Monitoring can be appropriate while a legitimate use case is evaluated, an approved alternative is prepared or a control is being tested. It is not an indefinite parking state: assign an owner, a decision date and the conditions that would trigger restriction or blocking.
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Escalate quickly when an agent has excessive write permissions, a credential is orphaned, regulated or secret data is confirmed in transit, or activity conflicts with a documented legal or security requirement. Preserve evidence before revoking access when doing so is safe and lawful.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should the inventory support audits and investigations?
Make the register auditable by linking each decision to evidence, reviewer and next review date. Retain the original observation, normalized catalog match, identity resolution, permissions snapshot and control change. This allows an investigator to reconstruct what the organization knew and which control was active at the time.
Separate routine governance from incident response. Routine reviews can use aggregate usage and risk data; an investigation may require detailed interaction or DLP records under approved procedures. Limit access to sensitive content and record who viewed or exported it.
What privacy and legal safeguards should accompany discovery?
Shadow-AI discovery can expose employee identities, browsing patterns and potentially sensitive prompts. State the purpose of collection, limit fields to what the decision requires, define retention and restrict analyst access. Prompt or payload inspection should occur only where legally and technically appropriate, with applicable labor, privacy, sector and cross-border requirements considered.
Explain to users how approved AI services handle retention and what monitoring applies. A transparent, predictable program is more likely to produce voluntary reporting than a hidden-content surveillance approach. Record data residency constraints before selecting telemetry storage or inspection methods.
What does a practical rollout look like?
Start with visibility, then add attribution and controls in stages so each phase produces a usable result.
First phase: establish the baseline
- Publish the scope, lifecycle states and ownership model.
- Collect network and Microsoft 365 application usage data.
- Normalize destinations against the cloud-app catalog.
- Open records for unknown, high-volume and high-privilege findings.
Second phase: close attribution gaps
- Correlate users and devices with endpoint and browser-extension inventories.
- Resolve departments, privileged status, service-account owners and business purposes.
- Audit OAuth grants, keys, plugins, MCP servers and reachable systems.
Third phase: measure exposure and act
- Add data-classification, byte-transfer and permitted DLP or payload evidence.
- Offer sanctioned alternatives for recurring legitimate use cases.
- Apply monitor, restrict, block, retention and investigation controls according to risk.
- Set exception expiries and next review dates, then report overdue decisions.
Track outcomes that demonstrate control rather than activity alone: the percentage of records with owners, the number of unknowns resolved, sensitive-data events prevented, orphaned credentials removed, exceptions expired on time and shadow tools replaced by approved options.
Why is visibility without a control not enough?
A report of unapproved AI domains does not change data flows, permissions or accountability. Microsoft’s guidance puts the distinction plainly: “Visibility that ends in a report changes nothing; visibility that ends in a control does.” The inventory is valuable when it drives a specific owner, purpose, risk decision, expiry and enforcement action.
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Frequently Asked Questions
Does an AI feature inside an approved SaaS product count as shadow AI?
It can. If the feature is enabled or used without registration, ownership, data-flow review or policy controls, treat it as an observed AI capability even though the parent SaaS application is approved. Record the specific feature, permissions and data path rather than assuming the parent approval covers it.
Can network logs prove exactly what an employee typed into an AI chatbot?
Usually not. Network telemetry can show the application, identity, timing and bytes transferred; prompt or payload content requires separate inspection or DLP evidence where legally and technically appropriate. Keep confirmed content exposure distinct from an inference based only on traffic volume.
What should we do when there is no approved alternative for a legitimate use case?
Keep the use case under review with a named owner, a defined purpose and an expiry date. Monitor or restrict the service while governance evaluates an alternative, and apply data controls so sensitive information cannot leave through the interim path.
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They can run continuously without a person present and may have broad permissions or access to production systems. Linking each key or account to an owner, workload, scopes, reachable systems and rotation status exposes orphaned automation that a browser-focused review would miss.
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