Shadow AI is not limited to an employee opening a public chatbot. It also includes browser extensions, desktop copilots, model APIs, SaaS connectors and internally deployed agents that operate without registration, an owner or security review. A CISO needs a repeatable inventory-and-control loop: collect telemetry, normalize every tool and agent into a registry, assess data and permissions, investigate the business need, then approve, replace, monitor, restrict or block each use.
The inventory must be continuous. AI applications add connectors, change retention and training terms, and gain new agent capabilities faster than a quarterly spreadsheet can track.
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What shadow AI includes
Shadow AI is the use of artificial-intelligence software or agents outside the organization’s approved governance process. Microsoft describes two related populations: unsanctioned tools employees adopt themselves, such as desktop assistants, browser extensions and task-specific SaaS, and unmanaged agents deployed inside the enterprise without registration, ownership or policy. An unmanaged agent can be invisible to the compliance and security stack; an unsanctioned service can receive corporate data without an audit trail or an incident-response record.
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The practical definition is broader than a list of chatbot domains. Include model-provider APIs, meeting and transcription bots, coding assistants, document plug-ins, workflow automations, retrieval systems, browser add-ons, local models, mobile apps and agents that can call business systems. Microsoft’s warning is concise: “You can’t govern agents you don’t know exist.”
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Why CISOs should treat discovery as a security control
Unapproved AI creates several risks at once. Sensitive prompts or uploads may be retained, reviewed by people, used to improve a service or transferred to a different jurisdiction. A browser extension may read every page an employee visits. An agent may inherit a user’s permissions and take actions in email, storage, ticketing or finance systems. If the tool is not registered, responders may not know what data left, which identity authorized it or how to revoke access.
Microsoft Defender for Cloud Apps documentation states that 80% of employees use non-sanctioned apps that no one has reviewed. The page also contrasts an average IT estimate of 30–40 cloud applications with an actual average of more than 1,000. These are page-stated Microsoft figures; the underlying survey years are not given on that page, so use them to illustrate discovery difficulty rather than as independently verified current prevalence rates.
Build a discovery program that reaches users and agents
1. Start with broad network and identity telemetry
Begin with internet, Microsoft 365 and identity traffic. Microsoft Entra Global Secure Access can analyze this traffic to identify AI chatbots, model-provider APIs and SaaS MCP servers, then show users, usage statistics, risk scores and bytes sent or received. Export the results into a central work queue rather than treating a dashboard as the inventory itself.
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2. Add endpoint, firewall and proxy data
Use endpoint and cloud-traffic sources together. Defender for Cloud Apps documentation describes collecting data from Defender for Endpoint, firewalls, proxies and log collectors, including devices on and off the corporate network. Endpoint telemetry can reveal installed desktop clients, local model runtimes, executable names, browser extensions and processes that never appear in a DNS report.
Normalize device identifiers and users across sources. An employee working from home may bypass an office proxy but still generate endpoint, identity or secure-access events. Record the coverage boundary for every source so that an apparent absence is not mistaken for proof that a tool is unused.
3. Inspect application catalogs, then handle uncatalogued services
A catalog accelerates triage by supplying publisher, category, risk factors and known controls. Microsoft says its Defender for Cloud Apps catalog contains more than 31,000 applications assessed against more than 90 risk factors in 2024 documentation. Catalog coverage is not complete, however. Microsoft advises checking its roadmap or creating a custom application for services that are not catalogued.
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When an unknown destination appears, preserve the raw hostname, certificate information, application identifiers and observed URLs before assigning a friendly name. New AI startups frequently use shared hosting, changing domains or an API endpoint that does not reveal the consumer-facing product.
4. Discover internally deployed agents
Network discovery will miss an agent that runs inside a cloud subscription and calls an approved model through an internal gateway. Search cloud inventories, source repositories, CI/CD pipelines, container registries, automation platforms, service principals and API-gateway logs. Require teams to declare agents during architecture review, but do not rely on declarations alone.
Every agent needs a distinct organizational identity. Capture its owner, purpose, platform, deployment environment, version, connected tools, data sources, permissions, approval points and lifecycle status. A shared administrator credential or an agent that runs as the deploying engineer prevents reliable attribution and should be treated as a control failure.
Create an authoritative shadow-AI registry
Use one registry for discovered applications, APIs, extensions, desktop software and agents. Deduplicate vendor names, product names, domains and personal versus corporate accounts. Link each record to evidence, an accountable owner and a review date.
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| Registry field | What to record | Why it matters |
|---|---|---|
| Identity and ownership | Business owner, technical owner, users, service identity and department | Enables accountability, access review and rapid revocation |
| Purpose and workflow | Specific task, business justification, approved alternative and criticality | Distinguishes a legitimate unmet need from convenience use |
| Data handling | Prompt, upload, retrieval and output types; retention; training use; deletion process; sensitivity labels | Shows what could leave the organization and for how long |
| Permissions | Scopes, inherited user rights, service principals, connected tools and write actions | Determines blast radius if the account or agent is abused |
| Security and compliance | Publisher, encryption, audit logging, certifications, incident history, jurisdiction, contract terms and risk score | Supports procurement, privacy and regulatory decisions |
| Usage evidence | Active users, departments, frequency, traffic, bytes and sensitive-data events | Prioritizes high-exposure records and measures controls |
| Decision and review | Approve, replace, monitor, restrict or block; policy owner; decision date; next review | Prevents indefinite exceptions and undocumented blocking |
For agents, add prompt and context sources, action limits, human approval checkpoints, model and tool versions, and the logs required to reconstruct a run. Register a new version or connector as a change that can trigger reassessment.
Assess each tool with the same risk questions
Business purpose and accountability
Ask which team uses the tool, what measurable workflow it supports and who accepts responsibility for the result. An owner who cannot explain the purpose, users or data path is a signal to suspend access while the record is clarified. Interview the department before blocking a high-use tool; the usage may reveal a capability that the sanctioned portfolio does not provide.
Data exposure and retention
Classify information entering, retrieved by or produced through the service as public, internal, confidential, regulated or source code. Determine whether prompts and files are retained, reviewed by humans, used for model training, shared with subprocessors or stored in a different region. Check deletion and export procedures rather than accepting a generic privacy statement. Treat retrieval from internal repositories as exposure even when employees do not manually upload a file.
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Identity and permissions
Prefer a distinct identity for every agent and least-privilege scopes for every integration. Determine whether the application inherits a user’s permissions, uses a broad service account or can create, send, delete or approve records. Require reauthentication and conditional access for sensitive actions, and make revocation testable. A read-only summarizer and an agent that can issue payments should not receive the same risk rating.
Security, legal and compliance posture
Review the publisher, encryption in transit and at rest, independent assurance, vulnerability process, audit-log quality, incident history, legal jurisdiction, contract terms, retention commitments and subprocessors. Map the processing to privacy, sector and records-retention requirements that apply to your organization. A high catalog risk score is a triage input, not a substitute for contract and architecture review.
Usage and exposure magnitude
Measure active users, departments, frequency, bytes transferred, destinations and sensitive-data events. A rarely used tool with access to a regulated repository may outrank a heavily used tool limited to public text. Separate personal accounts from corporate tenants because the organization may have no contractual or administrative control over the former.
Control feasibility and alternatives
Document whether you can monitor, warn, restrict or block the service, and how quickly each action takes effect. Identify a sanctioned alternative that meets the same workflow. Blocking without an alternative often moves use to a personal device or an even less visible service.
Use a documented risk score, not an intuition-only verdict
A simple scoring model makes decisions comparable. Score data sensitivity, permission breadth, provider assurance, usage volume, control weakness and business criticality on a defined scale such as one to five, then apply weights approved by legal, privacy, security and business owners. Record the evidence and confidence for every score; “unknown” should increase investigation priority rather than silently receive a low value.
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- High: confidential data or meaningful integrations with weak contractual, identity or monitoring controls. Restrict data types and actions while an approved replacement or remediation plan is completed.
- Moderate: limited internal data, narrow permissions and adequate logging, but incomplete review or retention clarity. Monitor, educate users and set a dated reassessment.
- Low: public-data use, no sensitive connectors, clear ownership and controls that meet policy. Approve with normal monitoring.
Do not let a score hide a non-negotiable condition. A service that cannot meet a legal residency requirement, or an agent that cannot produce an audit trail for privileged actions, may be unacceptable regardless of its aggregate score.
Turn findings into a governance decision
- Discover: collect applications, APIs, extensions, desktop agents and internally deployed agents from every available telemetry source.
- Normalize: merge aliases, domains, vendors, personal accounts and corporate tenants; retain the raw evidence.
- Assign: name a business owner and technical owner, and record the workflow and data classes.
- Assess: review permissions, retention, training use, jurisdiction, security evidence, usage and agent actions.
- Investigate need: interview users or departments when activity indicates a legitimate capability gap.
- Decide: approve, replace, monitor, restrict or block; document the rationale, controls, exception owner and review date.
- Enforce: apply DLP, sensitivity labels, identity controls, conditional access, browser or endpoint policy and network controls.
- Educate: explain permitted data classes, reporting channels and the sanctioned path for requesting a new tool.
- Recheck: review new applications, catalog changes, usage trends, incidents, provider changes and agent versions continuously.
Controls that reduce unsafe use
Data controls
Use DLP rules and sensitivity labels to detect or prevent regulated data, credentials, source code and confidential documents from entering unapproved AI services. Apply controls at upload, prompt, retrieval and output stages where the technology supports them. Define an explicit policy for generated content that contains copied confidential material or personal data.
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Identity and session controls
Require corporate sign-in for approved services, enforce multifactor authentication, restrict unmanaged devices and review OAuth grants. Limit agent scopes and require human approval for external communication, deletion, financial actions, privilege changes and other irreversible operations.
Network and endpoint controls
Use secure-access, proxy, firewall and endpoint policies to warn or block known destinations, extensions and executables. Logging should capture enough context to investigate without collecting more employee content than law and policy allow. Deep prompt inspection can improve exposure analysis, but it raises privacy, legal and performance questions that require local review and transparent notice.
Sanctioned alternatives and training
Publish an easy request path, approved tools, allowed data classes and examples of prohibited uploads. Offer a sanctioned tool that solves the common workflow, with documented support and predictable access. Training should show how to report a new AI service and how to recognize an agent requesting excessive permissions.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Monitor agents differently from ordinary SaaS
An agent can change behavior when its prompt, model, retrieval source or connected tool changes. Monitor versions, tool calls, context sources, denied actions, human approvals, output destinations and unusual volume. Revalidate permissions after every connector or deployment change. Keep immutable logs long enough to investigate incidents and map each action to a user or service identity.
Apply sensitivity labels to data an agent retrieves and require policy checks before the agent sends information outside the approved boundary. Test prompt-injection and data-exfiltration scenarios in a controlled environment, especially when an agent reads web pages, email or documents supplied by untrusted parties.
Should you block a public chatbot?
Blocking is appropriate when a service accepts prohibited data, has dangerous permissions, lacks a required contractual safeguard or cannot be monitored. It is not a complete strategy for a high-demand workflow. A network block may leave local desktop use, mobile access, personal accounts or another provider undiscovered.
For lower-risk use, monitor and warn first: show the data rule at the point of upload, record the event, contact the owner and offer an approved alternative. Escalate to restriction or blocking when users ignore warnings, sensitive-data events continue or the provider changes its terms or capabilities. Every exception needs an owner, compensating controls and an expiry date.
Measure whether the program works
- Percentage of discovered tools and agents with an owner, purpose and review date.
- Time from first detection to triage and from triage to a documented decision.
- Number and severity of sensitive-data events, blocked uploads and unresolved exceptions.
- Percentage of agents with unique identities, least-privilege scopes and reconstructable logs.
- Usage of sanctioned alternatives compared with recurring unsanctioned activity.
- Coverage of off-network devices, endpoints, proxies, cloud accounts and API gateways.
- Age of catalog records and the number of uncatalogued services awaiting assessment.
Review these measures with security, privacy, legal, procurement, IT and business leaders. NIST CSF 2.0 provides organizational profiles, tiers, enterprise-risk-management, workforce and AI-analysis resources; its resource page records an update on August 25, 2026. Use that framework to align shadow-AI work with the wider risk program rather than creating an isolated inventory.
Common failure modes and fixes
Relying on domain blocking alone
Domain lists find known traffic but miss local agents, encrypted prompt content, API aliases and cloud-hosted internal deployments. Combine network, endpoint, identity, cloud and source-control evidence.
Counting applications without recording data paths
A product name does not reveal what employees upload or what an agent can retrieve. Capture data classes, connectors, retention, training use and output destinations for each record.
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Assuming catalog scores are final
Catalogs change and uncatalogued services exist. Preserve evidence, verify current terms and create a custom record when no catalog entry is available.
Blocking before understanding the workflow
Users may move to personal devices or unsanctioned substitutes. Interview high-use departments, document the legitimate requirement and provide a safer path before enforcement where risk permits.
Allowing shared agent identities
Shared credentials destroy attribution and complicate revocation. Issue a unique identity, narrow scopes and log every consequential action.
A practical operating cadence
Run discovery continuously and perform a formal review at a defined interval suited to your risk. Trigger an immediate reassessment when a tool adds a connector, changes retention or training terms, moves data regions, suffers an incident, changes ownership or deploys a new agent version. Keep the registry decision, evidence and review date together so an auditor or incident responder can reconstruct why access was allowed.
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