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An agency may use AI to help plan, code, test, document, or analyze parts of your backend. That does not mean an AI built the system on its own: the important questions are what project information the tools can access, who checks each change, and who is accountable for the software after delivery.
Where AI may fit into backend development
AI assistance can appear at several points in a software project. NIST’s September 2026 DevSecOps practices describe assisted work across the development lifecycle, with review and validation as part of established processes. Depending on the agency’s tools and workflow, AI might help with:
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- Breaking requirements into tasks or supporting threat modeling.
- Drafting or changing application code, APIs, or infrastructure-as-code configuration.
- Generating unit or integration tests and technical documentation.
- Analyzing dependencies, vulnerability reports, or other security findings.
- Supporting parts of the continuous integration and deployment workflow.
These are possible uses, not evidence that a particular agency used AI on your project. The title alone does not identify a tool, model, contract, or architecture. Ask the agency what it actually used and where human review occurred.
What information an AI tool might receive
Some AI coding assistants can send context beyond the file currently open in an editor. Depending on the product and its settings, that context may include project structure, other files, or terminal output. OWASP’s Secure Coding with AI guidance recommends understanding what context a tool transmits and configuring exclusions for sensitive files and paths.
That matters because project context can contain credentials, personal information, proprietary business logic, or details of internal systems. Do not assume a file is invisible to an assistant just because it is excluded from version control: .gitignore prevents Git from tracking files, but does not by itself stop a locally running assistant from reading them.
Secrets such as API keys and database passwords should be stored outside AI-readable project files, for example in environment variables, a vault, or an encrypted secret store. Ask the agency which tools can access project files or logs, what information is sent to an external provider, and what exclusions are configured. Where appropriate, the agency can also explain how it checks outbound requests.
What can go wrong—and what controls matter
Incorrect or insecure code
AI-generated output can be inaccurate or insecure. It may also be difficult to maintain or fail to match the project’s requirements. NIST’s DevSecOps guidance calls for human monitoring and validation of AI-generated content; normal code review, security checks, and tests still matter.
Risky changes to build and deployment files
The review should cover more than application code. An AI coding agent may be able to change build scripts, package scripts, CI/CD configuration, or deployment infrastructure. Those files can run with significant privileges or affect how software reaches production. Ask whether an agent can modify them, run commands, or access production systems, and which actions require human approval.
Unclear responsibility after delivery
Using AI does not make accountability disappear. OWASP says an AI-assisted change should have a human owner who reviews and approves it and is responsible for its security and maintainability. The agency should be able to identify who approved changes, explain how they were checked, and say who will maintain the backend after handoff.
Questions to ask before approving the work
Use the same questions when comparing agencies or reviewing a proposal. These are practical discussion points, not universal legal requirements for every client-agency relationship.
- Tools and data: Which AI tools will be used? What source code, documents, logs, or other project information can they access? What is sent outside the agency, and what is excluded?
- Permissions: Can an AI agent run commands, reach production systems, or edit CI/CD and deployment files? Which actions need a person’s approval?
- Review and testing: Who reviews AI-assisted changes? What code review, automated tests, security analysis, and additional testing will be used for this project?
- Traceability and ownership: Can the agency show who approved each change and provide relevant change records? Who is responsible for maintenance once the backend is handed over?
- Acceptance and response: What test results or other evidence will accompany delivery? How will the agency prioritize, fix, and communicate a vulnerability found before or after acceptance?
Use a shared secure-development vocabulary
NIST’s Secure Software Development Framework (SSDF) offers a common vocabulary for discussing secure development with a software supplier. Its SP 800-218, SSDF Version 1.1, published in February 2022, describes practices and terminology that purchasers and suppliers can use in their communications. NIST’s SP 800-218A, published July 26, 2024, supplements that framework with practices for generative AI and dual-use foundation models. You can use these references to make a conversation about secure development more specific without assuming one checklist or contract applies to every project.
The cited guidance describes development practices and risks; it does not establish a universal productivity gain, defect rate, or breach likelihood for agencies using AI to build backends.
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