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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 issue is not simply whether staff use AI: it is whether the firm knows what tools receive its data, what systems they can access, and how their outputs affect customers, markets, or regulated work. Firms can manage the risk by making use visible, reviewing each use case, applying proportionate technical controls, and assigning clear ownership.
What does shadow AI mean in financial services?
Shadow AI can include public generative-AI services, browser extensions, AI features embedded in software, locally built scripts, automated agents, or external services used for business without the firm’s approval or oversight. The term describes a governance and operational problem; it is not a settled statutory definition shared across financial regulators.
The Financial Stability Board used the phrase directly in its 2026 consultation report, recommending “implementing measures to monitor, prevent, and remedy the use of ‘shadow AI’.” Those are consultation recommendations, not a new binding rule. In a related regulatory context, the European Securities and Markets Authority’s DORA Q&A says certain end-user computing and systems developed or managed outside the ICT function still belong within ICT risk-management and control processes.
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Why does unauthorized AI use matter to financial firms?
Risk depends on the information submitted, the permissions granted, the role of the output, and the provider handling the service. Official financial-sector sources identify several areas firms should consider:
| Risk area | Why it matters | Official source context |
|---|---|---|
| Privacy and confidentiality | Submitting customer, account, transaction, or internal material to an unapproved service can create a disclosure and provider-management issue. The actual exposure depends on the service’s terms, data practices, security, and the information submitted. | The U.S. Department of the Treasury identifies data privacy as a risk in its financial-services AI work. |
| Bias and customer harm | AI used in credit, pricing, customer treatment, or advice can affect people in consequential ways. The use case needs review proportionate to its effect on customers. | Treasury identifies bias and potential consumer harm; the U.S. Government Accountability Office discusses lending bias among AI risks in financial services. |
| Cybersecurity and operational resilience | A tool connected to internal systems or data can create access, vulnerability, or incident-response concerns. The risk grows when a service or agent can take actions rather than merely generate suggestions. | GAO identifies cybersecurity risk. The FSB consultation discusses shadow AI in a broader cyber and ICT-control context. A 2026 joint statement from UK authorities addresses resilience against frontier-AI-driven cyber threats. |
| Third-party and concentration exposure | The firm needs to know which provider receives data, what services are integrated, how the relationship is monitored, and whether it can be changed or exited. | Treasury flags third-party-provider risks. GAO notes oversight challenges associated with credit unions’ reliance on AI service providers. |
| Opaque or consequential output | When a system’s reasoning is difficult to inspect, a firm may struggle to validate its output or explain how it meets legal duties and protects investors. | SEC Commissioner Caroline Crenshaw raised “black box” governance and investor-protection questions in personal remarks at a 2025 roundtable. Her remarks were not a Commission rule or finding. |
How can a firm find and inventory shadow AI?
Give employees a workable disclosure route
Provide a straightforward way for employees and business teams to report the AI tools they use or want to use. Make clear that disclosure is a route to review and safe alternatives, not simply a trigger for punishment. A block-only policy can leave the firm with poor visibility into workarounds; that is an implementation inference, rather than a finding stated by the FSB.
Inventory tools and their actual use
Include external services, browser extensions, embedded assistants, locally built scripts, agents, and systems managed outside central ICT. Record enough information to understand the exposure and make a decision:
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- Ownership and purpose: the accountable business owner and the work the tool supports.
- Provider and service: who operates it and which external applications, libraries, or services it uses.
- Data: what information it receives, including whether it handles customer, confidential, or regulated material.
- Connections and permissions: what accounts, devices, applications, or systems it can reach, and whether it can act on them.
- Controls and review: how the output is checked, how the use is monitored, and when it must be reviewed again.
ESMA’s DORA Q&A is relevant to this inventory discipline: in the circumstances it describes, systems and assets managed outside the official ICT function and end-user-computing practices remain within ICT risk-management processes. The firm should not assume that a tool is outside its control perimeter just because employees or a business team introduced it.
How should a firm assess an AI use case?
Assess the specific use, not just the product label. A general-purpose service used to format non-sensitive text presents a different profile from an agent that can read customer records or influence a credit decision. The following are practical triage criteria synthesized from the cited governance, ICT, consumer-protection, investor-protection, and cyber concerns; they are not a regulator-issued universal scoring method.
- Data sensitivity: What information is entered, exposed through retrieval, or produced by the system?
- Customer or market impact: Could the output influence eligibility, pricing, advice, transactions, disclosures, or how customers are treated?
- Autonomy: Does the system only suggest content, or can it make decisions, trigger workflows, or take actions?
- System permissions: Which internal services, files, accounts, or records can it access or change?
- Provider exposure: Which third parties handle the data or support the service, and what is known about the relationship?
- Auditability and validation: Can staff check the output, understand its role, and preserve records needed to review its use?
- Monitoring and reversibility: Can the firm detect misuse or failure, contain it, and reverse or correct consequential actions?
Review each proposed use against applicable laws and regulations before deployment and periodically afterward, as Treasury recommends. Give stronger scrutiny to customer-impacting decisions, regulated records, confidential information, investment activity, and tools with access to internal systems.
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What controls help manage shadow AI?
Control software and access
Use authorised-software and endpoint controls appropriate to the firm’s environment. ESMA cites DORA requirements for measures to ensure only authorised software is installed, and says relevant ICT assets should be identified, documented, and managed. For AI agents and connected tools, limit permissions to what the approved use requires; the FSB consultation specifically discusses identity and access management and least privilege.
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Monitor use and data movement
Monitor relevant usage, anomalies, and movement of data, and consider data-loss-prevention measures where appropriate. The FSB consultation discusses monitoring, logging, and data-loss prevention as part of cyber and ICT controls. Monitoring should support detection and investigation while remaining proportionate to the firm’s obligations and the use being controlled.
Review integrations and suppliers
Track external applications, libraries, and services integrated into firm networks. The 2026 joint statement from the UK Financial Conduct Authority, Bank of England, and Treasury calls on firms to identify, monitor, and manage such connections in the context of frontier-AI-driven cyber threats. Review the provider relationship, the data handled, and the firm’s ability to monitor or exit it.
Prepare for incidents
Test how the firm would respond if an AI service exposed information, produced harmful output, or acted through a connected system. The FSB consultation discusses scenario testing for high-materiality AI cyber and ICT risks, while the UK joint statement emphasizes protection, response, and recovery.
Check service terms before handling confidential data
Do not infer that a consumer-facing or free service is suitable for confidential financial information. Before approving it, establish the provider’s actual terms, retention and training practices, security controls, data geography, and contractual rights. Treasury’s privacy concerns and ESMA’s ICT and third-party risk context support this due-diligence approach; the cited sources do not assess any particular consumer AI product.
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Assign a named business owner to every approved use case. Involve risk, compliance, privacy, security, legal, and ICT functions according to the use’s materiality. Keep a record of approvals and exceptions, and define who can authorize a change in purpose, data, provider, permissions, or system integration.
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Reassess a use when its model or provider changes, the data or purpose expands, permissions increase, a service is integrated with a new system, or relevant legal and regulatory expectations shift. Treasury recommends reviewing AI use cases for compliance with existing law both before deployment and periodically afterward.
The FCA’s AI overview reports that 84% of firms had an individual accountable for their AI approach. The reviewed page extract does not expose the survey year or methodology, so the figure should be treated as a reported FCA statistic, not as a dated estimate or evidence that accountability alone makes controls effective.
What do the regulatory sources say by jurisdiction?
| Jurisdiction or source | What it establishes | What not to infer |
|---|---|---|
| European Union: ESMA’s DORA Q&A | In the circumstances described, end-user computing, software under relevant end-user licence agreements, and ICT systems managed outside the ICT function fall within ICT risk-management processes. Relevant systems and assets must be authorised, securely integrated, identified, documented, and managed; third-party provisions may apply where the EULA and service-provider conditions are met. | It does not mean every employee’s personal AI use automatically creates a DORA-regulated vendor relationship. The facts, service, and contract matter. |
| United States: Treasury and GAO | Treasury’s December 2024 summary identifies privacy, bias, and third-party concerns and recommends compliance review before deployment and periodically. GAO’s 2025 report discusses benefits, risks, oversight, and AI-service-provider issues, including for credit unions. | These sources do not create a single standalone federal law called “shadow AI.” |
| United Kingdom: FCA and joint authorities | The FCA’s AI overview describes its regulatory approach and testing initiatives. A 2026 FCA, Bank of England, and Treasury statement calls for active cyber-resilience steps in its stated frontier-AI context, including governance, vulnerability management, third parties, protection, response, and recovery. | The joint statement is not a blanket prohibition on AI use. |
| United States securities regulation: SEC Commissioner remarks | Caroline Crenshaw’s 2025 roundtable remarks raise governance and investor-protection questions and expressly state that her views are personal. | They are not binding SEC guidance or a Commission position. |
The regulatory picture is evolving. A firm should map its own use cases to applicable obligations rather than treat “shadow AI” as a single, universally defined category.
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