Detecting hidden AI use is an inventory-and-observability task: compare the AI tools and capabilities a firm has approved with the applications and activity visible in its technical records, investigate the gaps, and update governance and monitoring when a use is confirmed. No single log or discovery product can establish every AI interaction or prove that an observed service was used improperly.
What counts as hidden AI use?
It is not limited to staff opening a public chatbot. AI may be accessed through enterprise services, APIs, internally hosted models, vendor-operated tools, or features embedded in software already approved for other work. A workflow can use AI for customer service, research, document handling, communications, coding, surveillance, or back-office operations without employees recognizing the feature as a separate application.
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For FINRA member firms, FINRA’s Regulatory Notice 24-09, published June 27, 2024, says existing technology-neutral rules and securities laws continue to apply when firms use generative AI or similar tools. The notice does not create new requirements or interpretations, and it explicitly encompasses third-party and embedded tools. It is not a universal rule for every financial institution or jurisdiction.
Build a reliable baseline before searching for gaps
Collect the records that describe what should be in use, then make them comparable with technical observations. Useful sources include the firm’s approved AI and model inventory, vendor and SaaS register, procurement records, API and cloud-account records, identity groups, endpoint software records, and relevant policies.
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There is no single required inventory schema established by these sources. As a practical minimum, record the responsible owner, purpose, provider, access route, data sensitivity, business criticality, approval or validation status, and monitoring contact. FINRA’s AI materials for the securities industry discuss inventories, risk ratings, testing, and monitoring. Federal Reserve model-risk guidance also supports having enough inventory information to understand risks, within the scope described below.
Discover observed applications and activity
Use the telemetry already available to the firm, such as secure web gateway, firewall, endpoint, identity, cloud access security broker (CASB), and SaaS logs. Discovery platforms can classify observed cloud applications and may associate activity with users, IP addresses, devices, and transactions. Microsoft documents these capabilities for Defender for Cloud Apps, including discovery of generative AI applications and options to monitor or block them in Manage generative AI apps for your organization.
Coverage depends on which devices, networks, and traffic sources feed the discovery system. Browser traffic may be visible while an API call, mobile connection, remote device, or AI feature inside another SaaS product is not. Treat an application appearing in a discovery report as a lead to investigate—not proof that a particular employee used a generative AI feature, submitted sensitive information, or broke policy.
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Reconcile observations with approved use
Compare discovered AI-related services and API activity with approved tools, sanctioned accounts, vendor records, and the declared inventory. Prioritize signals that create a meaningful mismatch or warrant clarification:
- A newly observed AI service or a change in activity associated with a known service.
- Use from a personal account on a managed device, especially where firm or customer information could be involved.
- Unusual concentrations of activity or activity outside the expected workflow.
- Unreviewed OAuth access or an API pathway that is absent from the approved records.
- An AI feature newly enabled in a vendor product that the firm already uses.
Where the platform supports them, discovery policies can alert on new applications or unusual activity. Microsoft documents cloud discovery policies and anomaly detection in Create cloud discovery policies. An alert is still a prompt for review; its meaning depends on the available context and the firm’s policy.
Investigate an alert before classifying it
Establish what happened and in which account or workflow before deciding whether a signal indicates approved use, a needed exception, a policy violation, or a false positive. A domain match alone usually cannot answer whether someone used a specific AI feature or what information was sent.
- Identify the activity: confirm the user, device, time, application or feature, account or tenant, and whether the observed event was an API, browser, or embedded-product interaction.
- Establish purpose and context: ask the business owner and relevant manager what workflow the activity supported and whether the service was expected or approved.
- Determine data exposure: use available logs and records to establish what data may have been involved. Do not infer uploaded content from an app or domain signal alone.
- Preserve and route evidence: follow the firm’s logging and recordkeeping controls, and involve security, privacy, compliance, the manager, and the vendor owner as appropriate.
- Record the disposition: document whether the use is approved, needs an exception, violates policy, or is a false positive, along with the evidence and next action.
If the investigation indicates possible exposure of sensitive data, escalate it through the firm’s incident process. FINRA identifies privacy, data integrity, reliability, accuracy, supervision, and recordkeeping as relevant considerations; its notice reminds member firms that existing obligations continue to apply.
Remediate the use and update controls
When the use is legitimate
Assess and document the use case, provider, data involved, safeguards, and required review. Update the inventory and guidance so employees and control teams know the approved service, account, and workflow. A tool’s presence in the inventory should not be treated as evidence that every new feature or data use is covered.
When the use is unapproved or too risky
Choose proportionate controls based on the facts: clarify policy, provide an approved alternative, restrict access, apply data loss prevention (DLP), or block the application where appropriate. Define how employees can request an exception, and verify that the chosen control works without leaving an alternate API or embedded-feature route unaddressed. Consider privacy, employee-monitoring, and records-retention requirements when configuring controls.
FINRA’s materials discuss governance, model risk management, data privacy and integrity, reliability, and accuracy. Microsoft’s documentation describes monitoring and blocking options, but documents vendor capabilities rather than independent validation of their completeness or suitability for a particular firm.
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New services, vendor updates, changing workflows, and new account or data pathways can make a one-time scan stale. Monitor for newly discovered applications and material changes in activity, ownership, versions, and risk; review the inventory and controls when products, vendors, or business processes change. Monitor approved systems too: authorization does not eliminate the need to oversee performance and changing exposure.
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How to evaluate discovery controls
There is no regulator-mandated scorecard in the cited materials. When comparing approaches, assess whether they fit the firm’s actual workflows and evidence needs:
- Coverage: managed and unmanaged endpoints, office and remote networks, browser and API activity, mobile access, and embedded SaaS features.
- Attribution: whether activity can be tied to a user, device, account or tenant, and business owner.
- Context: whether the system identifies the application and activity type and distinguishes a corporate tenant from a personal account.
- Content controls: whether data classifications and DLP rules can be applied, consistent with privacy and labor requirements.
- Evidence and records: available logs, retention, auditability, export, and integration with incident and compliance workflows.
- Operational fit: false positives, review workload, exception handling, deployment dependencies, and how new applications enter the catalog.
Microsoft Defender for Cloud Apps is one documented example of a product category, not an endorsement or a required solution. Its documentation does not independently establish detection quality, complete coverage, cost, or suitability for a financial firm.
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