Banks should evaluate AI coding assistants as third-party services embedded in the software development lifecycle—not as ordinary editor add-ons. Before approving one, map the code and other data it can access, verify the exact product tier and configuration’s data handling and controls, review the provider’s security and contract commitments, and test generated code through the bank’s existing review and validation process.
Start with the use case and its information boundary
An assistant that suggests a line of code has a different risk profile from an agent that can edit repositories, run terminal commands, or connect to other tools. Define the workflow before comparing vendors: the tool’s capabilities, the repositories and teams involved, the permissions it will receive, and what could happen if it produces an insecure or incorrect change.
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Inventory the information the workflow might expose. That can include prompts, source-code snippets, open-file and neighboring-file context, repository indexes, terminal output, responses, feedback, telemetry, and account metadata. Classify the information by sensitivity: public or internal code is not equivalent to confidential business information, customer records, payment data, authentication material, or other regulated information.
Set approval conditions for the use case rather than assuming one policy fits every coding workflow. NIST’s AI Risk Management Framework Generative AI Profile recommends use-case-based supplier risk assessment and inventorying third parties with access to organizational content. A bank can use those ideas to decide which workflows may be piloted, which data or permissions are out of bounds, and which require additional review.
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What data does an AI coding assistant send or retain?
Get a data-flow answer for the exact product, paid tier, enabled features, and deployment configuration. A general statement about a vendor’s service is not enough: collection and processing can vary by feature, and the relevant settings may differ between tiers.
- Collection: Identify every input and output handled, including prompts, code context, repository indexing, terminal output, feedback, telemetry, and account information.
- Use and retention: Establish what is transmitted, logged, retained, used for service improvement, used for model training, or accessible to support personnel. Ask whether organization administrators can disable or limit these uses centrally.
- Location and access: Ask where data is stored and where inference occurs; whether processing can cross regions; which subprocessors or support personnel can access it; and what backup, deletion, export, and access mechanisms apply.
- Evidence and enforceability: Compare the vendor’s documentation with the proposed contract, configuration, and administrative controls. Record which commitments are contractual and which are descriptions of default behavior.
Vendor documentation illustrates why these details matter. Amazon Q Developer documentation says the service stores questions, responses, and additional context; location varies by tier and feature, and some features may use U.S. regions. Google’s documentation for Gemini Code Assist Standard and Enterprise identifies developer prompts and code context as customer data, says prompts and responses are not stored by default, and says regional processing is not guaranteed. These are vendor statements about particular offerings, not a determination that either product meets a bank’s requirements. Verify the current terms and the bank’s actual configuration before approval.
Do not infer a model-training policy from a retention statement. Ask separately whether prompts, code, or other content may be used for training or product improvement, and whether the bank can prevent that use at the organization level. The cited product details do not establish a universal answer for every tier or configuration.
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Review identity, architecture, and shared responsibility
Assess the security boundary between the vendor’s service and the bank’s environment. Determine what the provider operates and what the bank must configure or monitor. AWS describes Amazon Q Developer security as a shared responsibility and documents identity, logging, and configuration topics; that framing is also a useful prompt for evaluating other services, not a substitute for product-specific evidence.
- Identity and permissions: Check single sign-on, account lifecycle, role-based access, least privilege, repository permissions, and whether administrators can enforce policy centrally.
- Network and secrets: Review network egress and private connectivity options, encryption, and how credentials, secrets, and sensitive values are protected from accidental exposure.
- Monitoring and response: Establish which activity generates audit events, how logs can be exported and retained, and what incident notification and vulnerability disclosure commitments apply.
- Operations: Evaluate support access, service resilience, and the controls available if the service or a connected integration is unavailable or compromised.
Translate each requirement into an accountable owner and a testable setting. A feature documented by a provider is not an effective bank control unless the bank has confirmed how it is configured, who can change it, and how exceptions are detected.
Assess provider governance and contract terms
Request security assurance and documentation appropriate to the service and the proposed use. Review the provider’s data-processing terms, subprocessor inventory and change notice, access and audit rights, limits on data use, retention and deletion commitments, incident notification, vulnerability handling, service-change notice, continuity arrangements, and exit support.
NIST’s Generative AI Profile recommends supplier due diligence that considers security, privacy, and intellectual-property risks; ongoing monitoring; approved provider inventories; and contract terms that allow evaluation of third-party AI processes and standards. Apply those recommendations proportionately to the bank’s use case. The contract should make material protections clear rather than leaving the bank to rely only on general product documentation.
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Validate generated code inside the bank’s SDLC
Run a controlled pilot on representative code that is non-sensitive or explicitly approved for the service. Define what success and failure look like for the intended workflow; for example, whether suggestions are useful and whether the tool creates unacceptable review burden or risky changes. A pilot can inform a decision, but it is not a security certification.
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Generated changes should pass the bank’s normal engineering gates. NIST’s Secure Software Development Framework (SSDF) and SP 800-218A provide relevant foundations: SP 800-218A, published July 26, 2024, augments SSDF 1.1 with AI-specific secure-development practices for generative AI and dual-use foundation models, and is intended for producers and acquirers of AI models and systems.
- Keep branch protection and peer review in place; require accountable human approval for changes.
- Run the bank’s tests and use static and dynamic analysis where appropriate.
- Check dependencies and licenses, and retain normal deployment authorization.
- Use techniques such as threat modeling and static analysis to look for security weaknesses; assess the generated change in context rather than assuming plausible-looking code is safe.
Compare vendors on decision-relevant evidence
Use the same questions for each proposed service, tier, and configuration. Separate documented defaults from settings the bank can enforce and from contractual commitments.
| Evaluation area | Questions to resolve |
|---|---|
| Data collected and retained | Which prompts, code context, outputs, feedback, and telemetry are processed or retained, and for how long? |
| Training and service improvement | Can content be used for training or product improvement? Can the bank disable that use centrally? |
| Geography and subprocessors | Where are data stored and processed? Which subprocessors may access them? Can regional processing be guaranteed? |
| Identity and administration | Can the bank enforce SSO, role restrictions, repository boundaries, and usage policies centrally? |
| Audit and incident response | What activity is logged, how can logs be exported, and what notification commitments apply? |
| Contract and exit | Are audit, deletion, change-notice, continuity, and termination rights adequate for this use? |
| Code quality and security | Does a controlled pilot produce acceptable results under the bank’s review and testing process? |
Record the decision and the conditions for use
Make the approval specific to the use case and deployment that were assessed. The decision record should name the approved workflows, prohibited data, required settings, accountable owner, review cadence, exception route, and rollback or exit plan. Reassess when the provider changes relevant features or terms, the bank changes its configuration or permissions, or the intended use expands.
What banking guidance applies?
OCC and interagency model risk guidance
The OCC’s 2026 revised model risk guidance covers model development and use, validation and monitoring, governance and controls, and third-party products. It expressly says generative and agentic AI are outside the guidance’s scope because they are novel and rapidly evolving. The OCC also says the guidance is not prescriptive or enforceable. It therefore should not be represented as a generative-AI compliance rule or a checklist that authorizes a coding assistant.
The OCC says the guidance is expected to be most relevant to banks with more than $30 billion in total assets, while it may also matter to smaller institutions with significant model-risk exposure. That threshold is not an exemption for smaller banks. The agencies announced plans for a future request for information on model risk generally, including AI; banks should check for subsequent developments when applying the guidance.
NIST and information-security guidance
NIST SP 800-218A adds AI-specific secure-development practices to SSDF 1.1. NIST’s Generative AI Profile offers supplier-risk, privacy, security, intellectual-property, monitoring, and contract-evaluation recommendations. They are useful evaluation resources, not a single universally mandated bank checklist.
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