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Shadow AI is a practical term for AI tools or systems used for work without a firm’s approval, inventory or governance. In financial services, the problem is not simply that an employee uses AI: it is that the firm may not know what information the tool receives, what provider handles it, what systems it can access or how its output affects customers and markets. Firms can manage the risk by making use visible, providing a workable approval route and applying controls proportionate to each use case.
What does shadow AI mean in financial services?
There is no single settled statutory definition of “shadow AI” established by the financial-sector sources discussed here. Use it as an operational description, not as the name of a specific legal category. It can include a staff member entering work information into an unapproved public AI service, a team adopting an embedded assistant without review, an employee-built script using an AI model, or an autonomous agent connected to internal systems.
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The defining issue is a gap in oversight: the organization has not approved, inventoried or governed the tool for the relevant work. A tool can be shadow AI even if it is used for a seemingly routine task, and an AI system is not necessarily shadow AI merely because it is new or generative. Its status depends on the firm’s approval and control arrangements.
The Financial Stability Board used the phrase directly in its 2026 consultation report, recommending measures to “monitor, prevent, and remedy” shadow AI. Those are consultation recommendations and sound practices, not new binding requirements. The SEC’s Caroline Crenshaw observed in personal remarks at a March 2025 roundtable that financial-services participants do not share one definition of AI; her remarks were not Commission rules or findings.
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Why can unapproved AI use create financial-services risk?
Exposure depends on the information employees enter, the permissions granted to the tool, the decisions its output informs and the provider that processes the data. U.S. Treasury’s December 2024 AI work identifies privacy, bias and third-party concerns; the U.S. Government Accountability Office (GAO) also discusses cybersecurity and lending-bias risks. The FSB consultation addresses shadow AI within a broader cyber and ICT-control discussion.
- Privacy and confidentiality: Customer, account, transaction or internal information submitted to an unapproved service can create a disclosure and provider-management issue. Whether it does so in a particular case depends on the data, tool and applicable obligations.
- Customer or investor harm: AI output used in credit, pricing, customer treatment, advice or investment activity may influence consequential decisions. Treasury identifies bias and potential consumer harm; GAO identifies lending bias among financial-services AI risks. The more direct the effect on people or markets, the more careful the review should be.
- Cybersecurity and resilience: A tool connected to firm data or systems can create access, vulnerability and incident-response concerns. A 2026 joint statement from the UK FCA, Bank of England and Treasury asks regulated firms to consider governance, vulnerability management, supply-chain risk, protection, response and recovery in relation to frontier-AI-driven cyber threats.
- Third-party and concentration exposure: Firms need to know which provider receives information, which services are integrated and whether the relationship can be monitored or exited. Treasury flags third-party-provider risks; GAO discusses oversight challenges associated with credit unions’ reliance on AI service providers.
- Opaque or consequential output: A firm may be unable to explain, validate or challenge output that affects customers or investors. Crenshaw’s personal 2025 remarks raised questions about governing “black box” systems, meeting legal duties and protecting investors; they should be read as issues under discussion, not binding SEC guidance.
How should a firm identify and assess shadow AI?
1. Make disclosure easy and build an inventory
Give employees and business teams a straightforward way to disclose AI tools used for work, including browser extensions, embedded assistants, locally built scripts, agents and external services. Record enough information to decide what controls are needed:
- Business owner and work purpose
- Provider and service involved
- Data handled, including its sensitivity
- Connections to firm systems and the permissions granted
- Whether output informs customer, market or internal decisions
- Approval status and next review date
Include systems managed by users or business teams outside central ICT. ESMA’s published DORA Q&A says relevant ICT risk-management processes cover systems developed or managed outside the official ICT function and end-user-computing practices.
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For each use, consider how sensitive the inputs are, how the output may affect customers or markets, how autonomous the tool is, what it can access, what third parties are involved, whether output can be validated and whether an error can be contained or reversed. These are practical triage criteria synthesized from the cited governance, ICT, cyber and investor-protection concerns; they are not a regulator-issued universal checklist.
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Review the use against applicable laws and regulations before deployment and periodically afterward, as Treasury recommends. Escalate uses involving confidential data, regulated records, customer-impacting decisions, investment activity or access to internal systems for stronger review.
3. Provide an approval route as well as controls
Pair technical discovery and monitoring with a clear path for requesting a tool or proposing an alternative. The FSB consultation recommends monitoring, preventing and remedying shadow AI. As an implementation choice, a block-only policy can leave workarounds less visible; a route to an approved option gives staff a practical way to raise legitimate needs.
What controls help manage approved AI use?
Controls should match the risk of the particular use. The following measures are grounded in the cited official material, but the sources do not prescribe one identical control set for every firm or application.
- Control software and ICT assets: ESMA cites DORA requirements for measures ensuring only authorized software is installed and says relevant ICT assets should be identified, documented and managed. Apply these processes to employee-managed and end-user systems where relevant.
- Limit access: Use identity and access management and least privilege, especially for AI agents. The FSB consultation discusses both access management and least privilege for agents.
- Monitor activity and data movement: Consider logging, anomaly monitoring and data-loss-prevention controls suited to the use. These appear in the FSB consultation’s discussion of cyber and ICT controls.
- Track integrations and suppliers: Identify, monitor and manage external applications, libraries and services integrated into firm networks, as called for in the 2026 UK joint statement.
- Exercise response and recovery: Test how the firm would respond to and recover from relevant AI-related cyber or ICT incidents. The FSB report discusses scenario testing for high-materiality AI cyber and ICT risks; the UK statement emphasizes response and recovery.
- Check provider terms before use: Do not assume a consumer-facing or free service is suitable for confidential financial data. Establish the actual provider terms, retention and training practices, security controls, data geography and contractual rights before approving it. This is due diligence based on the cited privacy and third-party concerns, not an assessment of any particular consumer AI product.
Who should own AI governance, and when should it be reviewed?
Assign a named business owner to each use case. Involve risk, compliance, privacy, security, legal and ICT functions according to the use’s materiality. Keep an exception process and record approvals and changes. Reassess when the model, provider, data, purpose, permissions or relevant regulatory context changes.
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The FCA’s AI overview reports that 84% of firms had an individual accountable for their AI approach; the page extract reviewed does not expose the survey year or methodology. Treat this as a reported FCA figure, not evidence that accountability by itself guarantees effective controls.
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What do financial-services rules and guidance say?
| Jurisdiction or source | What it says for firms | Important limit |
|---|---|---|
| European Union: ESMA’s DORA Q&A | In the circumstances it describes, end-user computing, software under relevant end-user license agreements and ICT systems managed outside ICT fall within ICT risk-management processes. Relevant assets must be authorized, securely integrated, identified, documented and managed. | Do not assume every employee’s personal AI use automatically creates a DORA-regulated vendor relationship. Assess the facts, the EULA and whether the service-provider conditions are met. |
| United States: Treasury and GAO | Treasury’s December 2024 summary identifies privacy, bias and third-party risks, and recommends reviewing AI use cases for compliance with existing law before deployment and periodically. GAO’s 2025 report covers benefits, risks, oversight and AI-service-provider issues, including for credit unions. | These sources do not create a single standalone federal “shadow AI” law. |
| United Kingdom: FCA overview and 2026 joint statement | The FCA overview describes its regulatory approach and testing initiatives. The FCA, Bank of England and Treasury statement addresses governance, vulnerability management, third parties, protection, response and recovery for frontier-AI-driven cyber threats. | The joint statement applies to its stated cyber-resilience context; it is not a blanket prohibition on AI. |
| United States: SEC Commissioner remarks | Caroline Crenshaw’s March 2025 roundtable remarks raise governance and investor-protection questions about AI, including opaque systems. | Crenshaw stated that her views were personal and not necessarily those of the Commission or its staff. The remarks are not binding SEC guidance. |
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