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Find shadow AI by combining network and Microsoft 365 traffic analysis with cloud-app catalog matching, endpoint and identity attribution, browser-extension review, and SaaS/API permission audits. Then turn each finding into an owned inventory record and a control decision—approve, monitor, restrict, block, apply DLP, retain, or investigate. Looking only for visits to public chatbots misses browser plug-ins, embedded SaaS features, model APIs, local agents, and unmanaged agents running inside your environment.
Microsoft defines shadow AI as AI operating without enterprise governance. The shared risk is not simply that employees use AI; it is that the organization cannot account for the data an AI service accesses or the actions an agent takes. This guide lays out a repeatable discovery workflow, an inventory schema, comparison criteria for technical approaches, and a staged response that reduces exposure without stopping legitimate work.
What counts as shadow AI in an enterprise?
Shadow AI includes both unsanctioned applications adopted by employees and agents deployed without registration, ownership, or policy controls. The common characteristic is unaccounted-for access to business data or systems.
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- Consumer chatbots and writing, meeting, image, research, or translation services used with a corporate identity or device.
- Desktop assistants and browser extensions that can read page content, clipboard data, files, or webmail.
- AI features embedded in SaaS products that procurement or security has not reviewed.
- Code assistants and prompt-based tools used in repositories, notebooks, tickets, or production support.
Unmanaged agents and integrations
- Model-provider API keys in scripts, notebooks, CI pipelines, or serverless functions.
- Plugins, SaaS Model Context Protocol (MCP) servers, and connectors that can call business applications.
- Locally installed or internally deployed agents with no registered owner, purpose, data boundary, or retirement date.
- Service-account agents whose activity cannot be tied to an accountable person or team.
An application can be legitimate yet still be shadow AI if its AI function, data path, or agent identity is outside the approved control model. Inventory should therefore capture both the tool and the way it is configured and used.
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Why should you inventory shadow AI instead of simply blocking it?
An inventory shows where sensitive information may leave, which business need employees are trying to meet, and which controls are missing. It also separates a low-risk experiment from an agent with write access to production systems.
Microsoft Security reported in 2025 that 80% of leaders cited data leakage as a top concern, 88% worried about bad actors manipulating AI systems, and 52% were uncertain about navigating AI regulations. These figures describe why discovery matters, but a report alone does not reduce risk. Microsoft’s guidance is direct: “Visibility that ends in a report changes nothing; visibility that ends in a control does.”
Treat findings as both risk signals and demand signals. If teams repeatedly choose an unapproved assistant because the sanctioned option is unavailable or difficult to use, provide an approved alternative and a fast exception path rather than forcing users into covert workarounds.
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How do you define scope and status before scanning?
Write the scope and lifecycle states first; otherwise every scanner will produce a different definition of “found.” Include consumer services, embedded SaaS features, APIs, extensions, MCP servers, local software, and internal agents.
Minimum scope
- Public and private generative-AI applications.
- Code, meeting, support, marketing, analytics, and document assistants.
- Browser extensions, desktop clients, mobile apps, and command-line tools.
- Model-provider endpoints, SDKs, API keys, and hosted inference services.
- SaaS AI features, plugins, OAuth applications, and MCP servers.
- Agents running on employee devices, virtual machines, containers, automation platforms, and service accounts.
Use explicit lifecycle states
| State | Meaning | Required next action |
|---|---|---|
| Unknown | A signal exists but the application, owner, or purpose is unresolved. | Enrich the record and assign an investigator. |
| Observed | The tool or agent is confirmed in use, but governance has not been decided. | Collect data-flow, permission, and user evidence. |
| Under review | An owner and reviewer are evaluating business need and risk. | Set a decision date and temporary controls. |
| Approved | The use case, provider, configuration, and data boundary meet policy. | Monitor usage and review on schedule. |
| Monitored | Use is allowed with logging, retention, or DLP conditions. | Alert on policy violations and unusual behavior. |
| Restricted | Only named users, devices, data classes, or functions are permitted. | Enforce conditions and expire exceptions. |
| Blocked | Access or data transfer is denied. | Offer an approved alternative and record evidence. |
| Retired | The tool, integration, or agent is no longer authorized or active. | Revoke tokens, remove software, and preserve required evidence. |
Which telemetry finds shadow AI most reliably?
No single data source has complete coverage. Combine the following evidence and reconcile it to a user, device, service account, or internal owner.
1. Network and Microsoft 365 traffic
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. Use this as a high-volume discovery layer, not as the entire inventory.
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Network evidence is strongest when a managed device reaches a recognizable destination. Record first and last observed times, volume, direction, and the signed-in identity. A destination alone does not prove what data was submitted, so preserve the limits of the signal.
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Normalize destinations against the Microsoft Defender for Cloud Apps cloud-app catalog. Preserve the provider, category, catalog risk factors, and any observed aliases. Catalog matching prevents duplicate records when the same provider appears under a web domain, API hostname, mobile endpoint, or regional alias.
3. Endpoint and browser evidence
Correlate endpoint inventory with installed applications, executable paths, package identifiers, browser extensions, extension permissions, and version numbers. This catches local assistants and extensions that use short-lived or encrypted connections that are difficult to identify from a domain alone.
Review extension permissions for page-read, clipboard, downloads, cookies, history, and native-messaging access. Attribute each installation to a device and signed-in user, then check whether it is installed in a managed browser profile or a personal profile on a corporate device.
4. Identity and directory records
Join sign-in logs, department, manager, privileged status, group membership, and service-account ownership to each finding. Identity attribution enables proportionate remediation: educate a user experimenting with public content, but escalate an agent used by a privileged administrator or connected to production data.
5. SaaS, OAuth, API, and agent permissions
Audit OAuth grants, API keys, model-provider accounts, plugins, MCP servers, and service accounts. Record every external system an agent can reach and the permissions it holds, including read, create, modify, delete, impersonation, repository, mailbox, ticketing, or deployment rights. A dormant token with broad write access is a finding even when traffic is currently low.
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What fields should every shadow-AI inventory record contain?
Use one canonical record per application, agent, integration, or materially different configuration. Link duplicate observations to the same record while retaining the evidence that proves each observation.
| Field group | Fields to capture | Why it matters |
|---|---|---|
| Identity | Application or agent name, provider, URL or API endpoint, category, observed aliases | Prevents duplicate records and supports catalog matching. |
| Timeline | First observed time, last observed time, review date, exception expiry | Distinguishes active use from stale artifacts and forces re-evaluation. |
| Attribution | Users, devices, department, service account, technical and business owner, privileged status | Makes education, remediation, and accountability possible. |
| Access path | Web, desktop, browser extension, mobile, API, plugin, MCP server, internal deployment | Identifies the control plane that can enforce a decision. |
| Usage | Frequency, sessions or calls, bytes sent and received, active devices | Provides scale and helps prioritize investigations. |
| Data exposure | Likely data classes, payload or prompt inspection where lawful, destinations, retention terms, DLP events | Shows what could leave and whether enterprise retention exists. |
| Permissions | OAuth scopes, API-key capabilities, connected systems, agent tools, service-account rights | Measures blast radius beyond the AI interface. |
| Governance | Business purpose, approved alternative, approval state, catalog risk, control state, reviewer, evidence links | Connects discovery to a defensible decision. |
Where prompt or payload inspection is not legally, technically, or contractually appropriate, record that limitation and use metadata, DLP events, classifications, and permission data instead. Do not imply content visibility that your controls do not actually provide.
How do you run a step-by-step discovery and inventory workflow?
- Define the population and states. Publish the scope, lifecycle states, owners, retention rules, and decision time limits. Include subsidiaries, contractors, unmanaged devices, and non-human identities where they fall within your policy.
- Collect network evidence. Export generative-AI, model-provider, and MCP observations from your secure-access or proxy telemetry. Preserve identity, device, timestamps, bytes, destination, and risk score.
- Normalize names. Match domains, API endpoints, package names, and aliases to a cloud-app catalog. Keep the raw indicator alongside the normalized provider so later investigations can reproduce the match.
- Enrich with endpoint data. Add installed applications, browser extensions, versions, device ownership, and management state. Flag software present on a device even when no network event has yet appeared.
- Resolve identity and ownership. Join directory and HR attributes, then ask the user or team to name a business owner and technical owner. For service accounts, require a human accountable for the credential and agent configuration.
- Audit integrations. Inventory OAuth grants, API keys, plugins, MCP servers, repositories, mailboxes, ticket systems, storage, and deployment targets. Test whether permissions are still needed and whether they are broader than the stated purpose.
- Assess data exposure. Map likely inputs to your data-classification scheme, note bytes transferred, identify external services, and document whether provider retention, training use, regional processing, or enterprise logging is established for the configured plan.
- Validate the use case. Ask what job the tool performs, what approved alternative was considered, what users and devices need access, and what would break if the tool were removed.
- Score and decide. Combine catalog factors, permissions, data sensitivity, provider terms, model behavior, user population, and observed activity. Assign a lifecycle state, owner, controls, decision date, and exception expiry.
- Enforce and preserve evidence. Apply monitor, educate, require approval, restrict, block, quarantine, or DLP controls. Retain the observations, approval, reviewer, and change record so an investigation can reconstruct what happened.
- Re-scan and close the loop. Compare new observations with the inventory, detect retired tools returning under aliases, and review exceptions before they expire. A finding is not closed until tokens, extensions, software, or access paths are removed or a new decision is recorded.
How should you compare discovery approaches?
Evaluate tools and operating models against the same dimensions. A network-only approach may deploy quickly but cannot reliably attribute local agents or reveal permissions; an endpoint-only approach sees software but may miss browserless APIs and cloud-to-cloud integrations.
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|---|---|---|---|
| Network and secure-access analytics | Internet, Microsoft 365, recognizable SaaS, model and MCP destinations | Fast population-wide visibility; user, usage, bytes, and risk metadata | Limited content and permission detail; encrypted or novel destinations may be ambiguous; TLS inspection can raise privacy and deployment issues. |
| Cloud-app catalog matching | Known providers, categories, aliases, and published risk factors | Consistent naming and baseline risk context | Catalog may lag new tools; does not prove configuration or actual data use. |
| Endpoint and browser inventory | Installed clients, extensions, versions, local agents, device state | Catches tools without clear network signatures and supports removal | Requires agents or management coverage; personal profiles and unmanaged devices remain difficult. |
| Identity and SaaS audit | Sign-ins, OAuth, API keys, service accounts, connected applications | Strong attribution and permission visibility | Every SaaS provider exposes different logs; dormant credentials can be missed without periodic review. |
| Content, DLP, and investigation telemetry | Prompts, payloads, files, classifications, policy events where supported | Best exposure detail and audit evidence | Higher privacy, legal, retention, data-residency, and storage obligations; not available for every service. |
| Internal-agent registry and review | Agents deployed in company environments, tools, owners, and change history | Captures systems that never traverse a public SaaS boundary | Depends on engineering adoption and accurate registration; requires ongoing configuration review. |
Score each option on coverage, attribution, exposure detail, risk quality, actionability, auditability, deployment effort, and privacy impact. The practical target is a layered control set rather than a single “shadow AI detector.”
What controls should follow discovery?
Use a staged response that preserves useful work while reducing the highest-risk paths. Microsoft describes the sequence as discover AI applications, block unsanctioned access, block sensitive data going to sanctioned applications, then govern, audit, retain, and investigate interactions.
Approve and provide a safer route
For a valid purpose, approve a defined provider, tenant, configuration, user group, data boundary, retention setting, and owner. Publish the approved alternative in the same workflow where users request access. Record what data is prohibited even when the application itself is sanctioned.
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Monitor and educate
Use monitoring for low or moderate risk where the business need is real but evidence is incomplete. Warn users at upload or prompt time, provide examples of restricted data, and alert on unusual volume, new destinations, privilege changes, or use outside the approved group.
Restrict or apply DLP
Limit access by identity, device compliance, network location, browser profile, data classification, or feature. Block sensitive data from reaching even approved applications when the provider, region, retention, or model-use terms do not meet policy.
Block and investigate
Block clearly unauthorized or high-risk tools, revoke OAuth grants and API keys, remove extensions and local software, and preserve evidence before deletion when an incident is possible. Investigate agents with broad permissions, unexplained data transfer, impersonation capability, or activity tied to privileged accounts.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you handle privacy, legal, and operational limits?
Discovery can expose employee behavior and potentially sensitive prompts. Define a legitimate purpose, minimum necessary collection, role-based access to telemetry, retention periods, regional processing rules, and an escalation path for legal or works-council review. Separate security metadata from content wherever metadata is sufficient.
Document whether TLS inspection, endpoint agents, browser management, or provider-side audit APIs are required. The Microsoft 365 admin-center Shadow AI page is documented as a public preview, so feature availability and behavior may change; validate the controls available in your tenant before making them a mandatory dependency.
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What common discovery failures should you avoid?
- Searching only for famous chatbot domains: include APIs, extensions, embedded features, MCP servers, local agents, and aliases.
- Creating a list without owners: an owner, purpose, reviewer, and decision date are required for action.
- Confusing traffic with content proof: bytes and destinations show activity, not necessarily the exact files or prompts.
- Ignoring permissions: an agent’s connected systems and OAuth scopes can create more risk than the model interface.
- Blocking before understanding demand: users may move to less visible tools; offer an approved alternative and a documented exception path.
- Leaving exceptions open-ended: set an expiry date, compensating controls, and a re-review trigger.
- Failing to remove credentials: uninstalling a client does not revoke API keys, OAuth grants, tokens, or service-account access.
- Over-collecting prompts: content capture without a defined legal and security purpose increases privacy and retention risk.
How do you measure whether the program is working?
Track operational measures that connect visibility to reduced exposure:
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- Percentage of AI observations matched to an owner, purpose, identity, and device.
- Time from first observation to risk decision, and percentage of records past their review date.
- Number of unknown or unregistered agents, OAuth grants, API keys, and MCP servers.
- Volume of sensitive-data transfer attempts blocked or redirected to an approved service.
- Percentage of high-risk findings with a verified owner, compensating control, and evidence trail.
- Rate at which blocked tools reappear under new domains, personal accounts, or alternate endpoints.
- Adoption and user satisfaction for approved alternatives, which indicates whether controls are driving safer behavior.
A mature inventory is continuously reconciled with network, endpoint, identity, SaaS, and agent telemetry. Its value is demonstrated by faster decisions, narrower permissions, fewer unowned integrations, and provable controls—not by the number of rows in a spreadsheet.
Frequently Asked Questions
Can a sanctioned AI application still create shadow-AI risk?
Yes. A sanctioned provider can become shadow AI when an unapproved feature, connector, tenant, plugin, or agent configuration bypasses the reviewed data boundary. Inventory the actual account, permissions, integrations, and settings rather than treating the provider name as approval.
How can I find AI use on unmanaged or personal devices?
Use identity-provider sign-ins, SaaS audit logs, secure-access telemetry, email or collaboration controls, and data-loss-prevention events to identify activity that does not appear in endpoint inventory. Apply policy to corporate identities and data, and handle personal-device monitoring under your organization’s legal and privacy rules.
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What should I do when an employee cannot identify the owner of an AI agent?
Place the record in an under-review or restricted state, stop privilege expansion, and set a short decision deadline. If no accountable owner accepts responsibility by that date, revoke credentials, remove integrations, and retain the evidence required by your incident and records policies.
How often should a shadow-AI inventory be reviewed?
Review high-risk agents, privileged integrations, exceptions, and sensitive-data paths on a shorter, risk-based cadence; review low-risk approved tools when ownership, provider terms, permissions, or major versions change. Trigger an immediate review after a new connector, data-classification change, security incident, or provider-retention change.
What is the difference between an AI tool inventory and an AI asset register?
An inventory records observed applications, agents, users, devices, traffic, permissions, and exposure evidence, including unknown items. An asset register usually contains only approved, owned systems; it should be populated from the broader inventory after review, not used as the discovery source.
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