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Shadow AI is not one app to block; it is an inventory and control problem. Employees may use unapproved chatbots, browser extensions, desktop assistants, model APIs, SaaS connectors or internally deployed agents that can move company data without an owner or audit trail. A CISO needs a continuous loop: discover activity, create an authoritative inventory, assess data and access risk, understand the business need, provide a safer approved option, and then monitor, warn, restrict or block according to the evidence.

What shadow AI includes

Microsoft uses “shadow AI” for two related populations. The first is unsanctioned software employees adopt themselves, such as AI chatbots, browser extensions and task-specific tools. The second is unmanaged AI agents deployed inside the enterprise without registration, ownership or policy controls. An agent may call other tools, retrieve corporate information or take actions on a user’s behalf. If it has no recorded owner or identity, security and compliance teams cannot reliably determine what it can access or what it did.

Unreviewed tools can pull corporate information into services that were never assessed, leaving no dependable audit or incident-response record. As Microsoft puts it: “You can’t govern agents you don’t know exist.” Treat that as an operating requirement, not a slogan: governance starts with knowing which applications, accounts, APIs, extensions and agents are present.

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Why a CISO should find shadow AI before deciding to block it

A blanket ban can drive use to personal accounts, mobile devices or unsanctioned workarounds while removing the visibility needed to manage the risk. Discovery shows whether the issue is a small number of high-risk uploads, widespread use of a legitimate productivity service, an unmanaged agent with excessive permissions, or an unmet business need for an approved capability.

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Microsoft Defender for Cloud Apps documentation cites two page-stated figures that illustrate why assumptions are unreliable: employees use an average of more than 1,000 cloud apps while IT estimates 30–40, and 80% use non-sanctioned apps that nobody has reviewed. Microsoft presents these as documentation statistics; the pages do not identify the underlying study year, so they should not be treated as independently verified survey results.

Build discovery from several telemetry sources

No single data source sees every form of AI use. Start broad, then add the endpoint, identity and cloud context needed to identify users, data and permissions.

Source What it can reveal Important limitation
Secure web gateway, firewall and proxy logs AI domains, model-provider APIs, SaaS traffic, volume and destination Usually cannot see local desktop agents, encrypted prompt content or activity outside managed network paths.
Endpoint telemetry Installed desktop assistants, browser extensions, processes, devices on and off the corporate network Coverage depends on managed devices and the endpoint sensor’s scope.
Identity and access logs Users, service principals, OAuth grants, corporate versus personal accounts and sign-in patterns A tool may be used without corporate single sign-on, leaving an account-ownership gap.
Cloud-app discovery Application catalog matches, users, departments, usage frequency, risk scores and data-transfer volume Catalog-based matching can miss uncatalogued or newly launched services.
Developer and platform logs Model APIs, keys, agents, connected tools, prompts or retrieved context, depending on configuration Teams may deploy agents in subscriptions or projects that central IT does not yet inventory.

Microsoft Entra Global Secure Access can analyze internet and Microsoft 365 traffic to identify AI chatbots, model-provider APIs and SaaS MCP servers. Its discovery view is designed to show users, usage statistics, risk scores and bytes sent or received. Defender for Cloud Apps describes ingesting cloud-traffic data from Defender for Endpoint, firewalls, proxies and log collectors, including devices on and off the corporate network.

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Practical discovery sequence

  1. Export the broadest available traffic view. Search for known AI categories, model-provider domains, API endpoints and SaaS MCP services. Preserve timestamps, user or device identifiers, bytes transferred and the account used.
  2. Join traffic to endpoint records. Identify installed applications, browser extensions, executable names and unmanaged devices. Record whether the same service appears through a corporate account, a personal account or both.
  3. Join activity to identity data. Resolve aliases, service principals, OAuth consent and group membership. A domain name alone is not an accountable inventory record.
  4. Inspect cloud-app catalog matches. Capture the vendor, product variant, risk rating and available controls. For an uncatalogued service, check whether a custom application definition is available and document the matching logic.
  5. Look for internal agents and APIs. Search cloud subscriptions, repositories, automation platforms and model-provider consoles for deployments that have no registration, owner or lifecycle record.

Discovery is a recurring process. New applications appear, vendors add agent features and existing tools change retention or training terms. Schedule collection and review rather than treating a one-time scan as an inventory.

Create an authoritative inventory, not a list of domains

Use one registry for both employee-adopted applications and internally deployed agents. Deduplicate vendor names, product editions, aliases and personal-versus-corporate accounts. Give every record a status such as discovered, under review, approved, restricted, replaced, blocked or retired, together with an owner and next review date.

Minimum record for an AI application

  • Canonical product and publisher name, account type, first and last observed use, active users, departments and usage frequency.
  • Business purpose, accountable owner and the workflow the tool supports.
  • Data entered, uploaded, retrieved, retained and potentially used for model training.
  • Data classes involved: public, internal, confidential, regulated or source code.
  • Authentication method, OAuth grants, administrator roles and whether access follows least privilege.
  • Hosting and legal jurisdiction, encryption, certifications, contractual terms, retention and known incident history.
  • Audit-log availability, export method, DLP compatibility, sensitivity-label support and policy actions available.
  • Approved alternative, decision rationale, decision date and reassessment trigger.

Minimum record for an agent

In addition to the application fields, record the agent’s unique identity, owner, purpose, platform, deployment environment, version, connected tools, data sources, actions it may take, prompt or context sources, inherited user permissions and human approval points. Register service accounts and secrets separately from the agent’s display name. Microsoft recommends a single organizational inventory and a unique identity for every agent; without that separation, disabling a user may not remove the agent’s ability to act.

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Assess security, data and business risk consistently

Apply the same review record to every discovered tool, even when the initial evidence is incomplete. A high user count does not make a service safe, and a low user count does not make an agent harmless: privilege, data sensitivity and action capability matter more than popularity.

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Business purpose and accountability

Ask the using team what task the service performs, which outcome it improves and who accepts responsibility for the result. If nobody can explain the workflow or own the risk, classify the record as unmanaged until someone does. Interview users when usage suggests a legitimate unmet need; the answer may be a sanctioned tool, a configuration change or a process that does not require AI.

Data exposure

Determine whether prompts, files, retrieved records, telemetry and generated outputs are retained, shared with subprocessors or used to train a model. Separate public, internal, confidential, regulated and source-code data. Verify deletion and retention behavior in the contract or administrator settings rather than relying on a marketing statement. A tool that is acceptable for public text may be prohibited for customer records or unreleased code.

Identity and permissions

Check for a distinct application or agent identity, least-privilege scopes, short-lived credentials and user-inherited permissions. Review OAuth grants and connected tools for read, write, send, purchase, delete or administrative actions. An agent that can retrieve sensitive records and execute changes needs stronger approval gates than a chatbot that only answers questions from public material.

Security, compliance and legal terms

Review the publisher, encryption in transit and at rest, audit logging, certifications, incident history, legal jurisdiction, contractual commitments, retention controls and support for regulatory obligations. Defender for Cloud Apps describes a catalog of more than 31,000 apps assessed against more than 90 risk factors in Microsoft’s 2024 documentation. A catalog score is a screening input, not a substitute for reviewing the exact plan, tenant configuration and data contract your organization will use.

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Usage and exposure evidence

Measure active users, departments, frequency, traffic, bytes transferred and sensitive-data events. Look for spikes, unusual destinations, use outside working accounts and repeated uploads of the same data class. Preserve enough context to investigate an incident without collecting more employee content than local privacy and employment rules allow.

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Control fit and alternatives

Document whether you can monitor, warn, restrict or block the service; distinguish domain blocking from account, feature or data-policy controls. Identify a safer sanctioned tool that meets the same need and compare user friction, functionality and data handling. A control that users can bypass easily is a weak control, even if its policy setting is technically enabled.

Use a risk decision that leads to action

Translate the assessment into a documented disposition. The following pattern keeps the decision understandable to security, legal, procurement and the business owner.

Disposition When it fits Typical controls
Approve Purpose and owner are clear; data, permissions and contract meet requirements; monitoring is available. Allow listed identities, DLP and sensitivity labels, logging, periodic review and change notification.
Approve with restrictions The tool is useful but only for defined data classes, users, regions or features. Conditional access, tenant settings, upload limits, connector restrictions, warnings and enhanced logging.
Monitor while remediating Use is material, the need is legitimate, but ownership, terms or technical controls are incomplete. Notify users, assign an owner and deadline, apply temporary DLP or read-only controls, and track evidence.
Replace A sanctioned alternative can meet the workflow with lower exposure or better auditability. Provide migration help, preserve required records and retire the unsanctioned account or connector.
Block There is unacceptable data, legal, identity or action risk, or the service refuses required controls. Block domains or applications, revoke tokens, remove extensions, disable accounts and investigate prior exposure.

Record the decision, owner, scope, effective date, review date and trigger for reassessment. Reopen it when the vendor changes model training or retention, adds agent capabilities, changes jurisdiction, suffers an incident or materially changes usage.

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Enforce controls without losing visibility

Data controls

Use DLP policies and sensitivity labels to distinguish allowed public or internal content from confidential, regulated and source-code data. Where inspection of prompts or uploads is technically possible, define what is inspected, who can access the evidence, how long it is retained and which local privacy requirements apply. Start with warnings and user justification for lower-risk cases; block high-confidence violations involving protected data.

Identity and access controls

Require corporate sign-in where the service is approved, limit OAuth consent, remove unused grants and use conditional access for unmanaged devices, risky locations or sensitive groups. Give agents unique identities and narrow scopes. Separate development, test and production credentials, rotate secrets and require human approval for consequential actions.

Network and endpoint controls

Use secure web gateways, firewalls and proxy policies for known destinations, while endpoint management removes unapproved extensions and detects desktop agents. Network blocking alone will not cover local software, personal devices or newly registered domains; combine it with endpoint and identity telemetry.

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People and process

Publish a short acceptable-use rule, explain which data classes may be entered into which approved tools and provide an easy request path. Train employees to recognize retention, training and sharing settings. Make the sanctioned option faster to obtain than an exception, and give departments a way to report a legitimate need.

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Measure whether the program is working

Track inventory coverage and control outcomes, not just the number of blocked domains. Useful measures include:

  • Percentage of discovered applications with a named owner, purpose, data classification and review date.
  • Percentage of agents with unique identities, documented connected tools and tested approval points.
  • Time from first observation to triage, owner assignment and final disposition.
  • Users, departments, bytes transferred and sensitive-data events by tool and risk tier.
  • Number of personal-account uses, unmanaged devices, revoked OAuth grants and unresolved exceptions.
  • Adoption of the sanctioned alternative and repeat attempts to use a blocked service.
  • Coverage of endpoint, off-network, proxy, identity and cloud logs, including gaps caused by uncatalogued applications.

Review trends with security, privacy, legal, procurement, IT and business owners. The objective is controlled, accountable use of AI—not a permanently frozen list of products.

Common blind spots and recovery steps

“The catalog found nothing”

Catalog discovery can miss services that are not yet listed. Check the catalog roadmap and create a custom application definition when supported. Correlate domains with DNS, proxy, endpoint and identity data, then add the confirmed service to the registry.

“We blocked the domain, but use continues”

Look for alternate domains, mobile or personal devices, browser extensions, direct model-provider APIs and cached or locally installed software. Revoke tokens, remove extensions, tighten endpoint policy and address the business requirement with an approved option.

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“An approved tool became risky”

Reassess when a vendor adds connectors, agent actions, new training terms or a different hosting region. Freeze newly introduced features if necessary, compare the changed contract with your data classes and update the disposition.

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“We discovered an agent after an incident”

Preserve identity, access, prompt, tool-call and deployment logs; disable credentials or connectors according to the incident plan; determine what data and actions were reachable; and record the agent and owner in the inventory before restoring service.

How to compare discovery and governance approaches

When evaluating products or an internal program, compare the dimensions that affect actual coverage and response:

  • Telemetry coverage across endpoints, off-network devices, proxies, firewalls, identity systems, SaaS and model APIs.
  • Catalog breadth, custom-app support and speed of adding new services.
  • Ability to inspect prompts or uploads, apply DLP and sensitivity labels, and preserve an audit trail.
  • Risk and compliance scoring, ownership and identity management, and agent-specific inventory fields.
  • Policy-action latency, regional coverage, integrations, user friction and operating cost.
  • Quality and availability of sanctioned alternatives that users will actually adopt.

A network-only approach is quick for domain and traffic discovery but weak for local agents and content exposure. Endpoint plus cloud-app telemetry improves coverage. Deep inspection can improve data analysis, but it also creates privacy, legal and performance questions that must be reviewed for each jurisdiction and workforce.

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Keep Microsoft and NIST capabilities current

Microsoft’s Shadow AI, Entra Global Secure Access, Defender for Cloud Apps and agent-governance features are living product documentation. Preview status, supported agents, prerequisites, licensing and enforcement behavior can change; validate the current service documentation and your tenant’s capabilities before procurement or a control commitment.

NIST CSF 2.0 resources provide organizational profiles, tiers, enterprise-risk-management, workforce and AI-analysis guidance. The NIST page records an update on August 25, 2026. Use that framework to align shadow-AI discovery and response with the organization’s broader governance and risk process, while keeping the operational inventory and controls specific to your own data, identities and agents.

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