Detect hidden AI use by comparing what employees and vendors actually use with the firm’s approved AI and vendor inventory. Use network, endpoint, identity, and cloud-application telemetry to find leads, then verify the account, purpose, feature, and data involved before treating activity as a policy violation. No single discovery tool sees every route into an AI service.
What counts as hidden AI use?
It is not limited to an employee opening a public chatbot. AI may be built into an existing SaaS product, supplied by a vendor, accessed through an API, hosted internally, or enabled in an enterprise AI tenant. A business process can use AI without employees recognizing the feature as a separate tool.
Set scope around workflows as well as named models: customer service, research, document processing, communications, surveillance, coding, and back-office operations may all involve AI. FINRA says its existing obligations apply to member firms’ direct development and third-party use, including embedded features; its notice is specifically about FINRA members, not a universal rule for every financial institution or jurisdiction. FINRA Regulatory Notice 24-09
Build the declared baseline before searching
Gather the records that show what the firm knows and approves. A useful baseline brings together the AI or model inventory, vendor and SaaS register, procurement records, API and cloud accounts, identity groups, endpoint software records, and relevant policies. Compare observed activity against that combined view rather than relying on a single list.
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For each known use, record enough information to assign responsibility and assess risk. The exact schema will vary, but practical fields include:
- Responsible business owner and monitoring contact
- Purpose and workflow
- Provider, product, and access route, such as a browser app, API, or embedded feature
- Data sensitivity and business criticality
- Approval or validation state
FINRA’s securities-industry material discusses detailed AI model inventories and risk ratings. Federal Reserve model-risk guidance likewise calls for sufficient inventory information to understand model risks, but its stated principles are limited to traditional statistical and quantitative models and non-generative, non-agentic AI models; it should not be treated by itself as guidance governing generative AI. FINRA: Key Challenges and Regulatory Considerations Federal Reserve: Supervisory Guidance on Model Risk Management
Use telemetry to discover applications and activity
Review available secure web gateway, firewall, endpoint, identity, cloud access security broker (CASB), and SaaS logs. These sources can help identify services, users, devices, IP addresses, and transactions, depending on what traffic and devices feed them. Cloud-app discovery can classify observed applications from traffic logs; it does not automatically reveal what content a person submitted or whether the use was unauthorized.
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Microsoft documents Defender for Cloud Apps capabilities for discovering generative AI apps, reviewing catalog risk information, monitoring usage, and blocking apps. Its cloud discovery documentation describes policies that can alert on newly discovered apps and anomalies in discovery logs. These are vendor-described functions, not independent evidence that detection is complete, accurate, suitable for a particular firm, or cost-effective. Microsoft Learn: Manage generative AI apps for your organization Microsoft Learn: Create cloud discovery policies
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Compare observed AI-related apps and API activity with approved services, sanctioned accounts, and the records in your baseline. Prioritize leads that may indicate a meaningful gap:
- A newly observed AI service or an unreviewed OAuth connection
- Use of a personal account on a managed device, where the account context can be established
- Unusual concentrations or patterns of activity
- An AI capability newly enabled inside an otherwise approved vendor product
- Activity through a route, such as an API or embedded feature, that the inventory does not describe
Configure new-app and anomaly alerts where your discovery platform supports them. Treat these as prompts for review, not proof of prohibited use: a domain or application signal alone does not establish that someone used a generative AI feature or uploaded sensitive data.
Investigate each lead before classifying it
Establish what happened and who is responsible before labeling a finding a violation. Confirm the user, device, business purpose, account or tenant type, application feature, data sent, and relevant vendor settings. Record the evidence under the firm’s existing logging and records controls, and involve the appropriate manager, security, privacy, compliance, and vendor owner.
Document the disposition in a way that can feed back into governance: approved use, exception needed, policy violation, or false positive. If the investigation indicates possible exposure of sensitive data, escalate through the firm’s incident process. FINRA identifies privacy, data integrity, reliability, accuracy, supervision, and recordkeeping considerations in its discussion of generative AI and existing obligations. FINRA Regulatory Notice 24-09
Remediate and update the inventory
For a legitimate use that was missing from the records, assess and document its purpose, data, provider, controls, and required review; then update the inventory and approved-tool guidance. For unapproved or risky activity, choose a proportionate response based on the findings:
- Explain the policy and provide an approved alternative
- Restrict access or apply data-loss-prevention controls where appropriate
- Block an application if the risk and business impact justify it
- Provide a documented exception path for justified business needs
After a change, check that the control works and consider whether another route remains, such as an API or AI capability embedded in a permitted vendor product. FINRA discusses governance, model risk management, privacy and data integrity, reliability, and accuracy; Microsoft documents monitoring and blocking options for AI applications. Those sources describe considerations and product functions, not a single required remediation recipe. FINRA Regulatory Notice 24-09 Microsoft Learn: Manage generative AI apps for your organization
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New services, vendor updates, changes in ownership, and shifts in workflow can make a previously accurate inventory stale. Review newly discovered apps and changes in use, and revisit approved systems as well as unapproved ones: authorization does not remove the need to monitor performance and risk.
FINRA’s securities-industry materials discuss ongoing testing, benchmarks, inventories, and monitoring. Federal Reserve model-risk guidance describes ongoing monitoring as products, exposures, activities, clients, data relevance, or market conditions change, subject to its stated scope limitations for generative AI. FINRA: Key Challenges and Regulatory Considerations Federal Reserve: Supervisory Guidance on Model Risk Management
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How to evaluate discovery controls
When assessing a CASB, endpoint, or other discovery approach, test whether its coverage and workflow fit the firm rather than assuming a product sees everything. Useful evaluation questions include:
- Coverage: Which managed and unmanaged endpoints, office and remote networks, browsers, APIs, mobile devices, and embedded SaaS features are visible?
- Attribution: Can activity be tied to a user, device, account or tenant, and business owner?
- Context: Can the control distinguish app identity and activity type, including corporate versus personal accounts?
- Content controls: Can the firm apply its data classifications and DLP rules, subject to privacy and labor requirements?
- Evidence and records: What logs, retention, auditability, export, and incident or compliance integrations are available?
- Operational fit: How are false positives, exceptions, review workload, deployment dependencies, and new app catalog entries handled?
These are practical evaluation axes, not a standardized regulator-mandated scorecard. FINRA Regulatory Notice 24-09 reminds FINRA member firms that existing technology-neutral rules and securities laws continue to apply when they use generative AI or similar tools; it does not announce a new AI-specific requirement. Other financial firms should assess the rules and supervisory expectations applicable to their own jurisdiction and business.
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