Shadow AI & AI Inventory for Financial Institutions
Uncover the AI You Didn’t Approve
Confidence for examiners. Clarity for the board. Control over what’s actually running.
What Shadow AI Looks Like at Financial Institutions
When financial institutions block AI tools, ChatGPT, Microsoft Copilot, Google Gemini, and others, they rarely eliminate the behavior. Employees find alternatives. Personal devices. Personal accounts. Browser extensions. AI embedded in products the institution approved for entirely different purposes.
Shadow AI is the AI your institution is running that isn’t in your inventory, isn’t covered by your policy, and isn’t on any examiner’s radar yet. The risk is concrete: employees are uploading customer data, loan files, internal documents, and proprietary information to AI tools the institution has never reviewed, approved, or secured.
Blocked Enterprise Tools, Personal Workarounds
Institutions that block ChatGPT or Copilot at the network level often find employees using personal phones or home networks to accomplish the same tasks, with no oversight, logging, data controls, or institutional awareness that customer information is being processed externally.
Vendor-Embedded AI
Core banking systems, document management platforms, and CRM products are adding AI features on their standard update cycles. Institutions accept the updated terms, use the product, and may not realize AI is now involved in summarizing customer data, flagging transactions, or generating communications.
Browser Extensions and Productivity Tools
AI writing assistants, email summarizers, meeting transcription services, and grammar tools are commonly installed by individual employees without IT review. These tools often process the full content of emails, documents, and meeting recordings, which may include customer information, loan details, or confidential communications.
AI Features in Approved Tools
A vendor tool approved for one specific function has been updated to offer AI features in adjacent areas. Employees are using them because they are convenient and already installed. The institution’s original due diligence covered the original tool, not the AI that was added later.
Third-Party Data Integrations
Data analytics platforms, credit decisioning tools, and marketing systems may be processing institution or customer data through AI models as part of their standard service. The AI component may not have been disclosed in the original vendor agreement or due diligence process.
Our Shadow AI & Inventory Services
Shadow AI Assessment
A structured assessment of unapproved AI tool use across the institution. Includes structured interviews with business line leaders, IT and network review, endpoint data analysis where available, and vendor contract review for AI provisions. Delivers a risk-rated findings report with recommended disposition for each identified tool or exposure area.
AI Inventory Build
Working with management to construct a complete, defensible AI inventory covering internally developed tools, vendor-embedded AI, and employee-facing applications. Includes an inventory template, risk classification framework, documentation standards, and an initial population of the inventory based on our assessment findings.
AI Inventory Audit
Independent audit of an existing AI inventory for completeness, accuracy, and governance. Includes verification procedures, gap assessment against the institution’s AI policy, and a review of the process for keeping the inventory current as new tools are added or vendor products change.
Ongoing Inventory Program Support
Advisory services, delivered through Insight Risk Consulting, for institutions that want to build a sustainable, repeatable inventory management process, including program design, quarterly review procedures, vendor notification protocols, and employee training on AI tool approval requirements.
Frequently Asked Questions
If we have an AI acceptable use policy, do we still have shadow AI risk?
Yes. A policy defines what is permitted, it does not prevent employees from using unpermitted tools, and it does not identify what tools are currently in use. Most institutions with an AI use policy find, when they look, that employees are using tools the policy prohibits. The policy is necessary but not sufficient.
How do vendors add AI to products without institutions knowing?
Vendors update their terms of service and product features on their own release cycles. AI features are often introduced as enhancements to existing functionality, without a separate disclosure or notification that requires institutional acknowledgment. Regular vendor contract and product review is the only reliable way to identify these changes.
What is the regulatory risk of shadow AI at a bank or credit union?
Shadow AI creates multiple exposure areas: data security risk from customer information processed through unapproved tools, fair lending risk if AI tools are influencing credit decisions outside of approved processes, BSA/AML risk if AI tools are used in transaction monitoring without oversight, and basic governance risk if examiners find that leadership did not know what AI was running across the institution.
