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AI can produce code quickly, but that does not make the result safe to deploy. Treat every AI-assisted change as a proposed contribution: assign a human owner, review the actual changes, run appropriate tests and security checks, record approvals, and keep AI agents within the same permission and deployment controls used for other development work.
What “trust” should mean before deployment
Trust is not a judgment that code looks plausible or that an AI tool produced it confidently. It is a decision supported by evidence that the change fits the application, has been reviewed and tested, meets security requirements, and passed the organization’s approval process.
NIST’s DevSecOps guidance identifies inaccurate outputs and insecure code among the risks of using AI in software development. Its risk model also describes possible unauthorized actions, excessive agent privileges, data leakage, limited explainability, context tampering, hallucinated security recommendations, and unapproved or untraceable artifacts entering the software supply chain. These are categories of risk—not claims that every tool or generated change will exhibit every problem.
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Who is accountable for an AI-assisted change?
Assign a named developer to each change and require that person to understand it well enough to explain its purpose, assumptions, and behavior before approving it. OWASP’s Secure Coding with AI Cheat Sheet puts the responsibility plainly: “AI tools do not accept responsibility for the code they generate.” The human owner is responsible for review, approval, security, and maintainability.
Keep an audit trail showing who reviewed and approved the change. Where it is appropriate under organizational policy, record which tool and model version contributed to it. Provenance helps an organization trace how a change was produced and how it passed its controls; it does not replace reviewing the code itself.
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What to check in the actual change
Review the diff—the exact additions, deletions, and modifications proposed for the codebase—not just the prompt, summary, or explanation supplied by the tool. Check whether the change is necessary, consistent with the application’s architecture, and compatible with its security requirements.
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- Assumptions and inputs: Identify what the code assumes about user input, external services, files, and data. Check how untrusted or malformed input is handled.
- Authorization paths: Verify that access checks happen in the right places and cannot be bypassed. Pay particular attention to code that reads, changes, or exposes protected data.
- Error handling: Check what happens when dependencies fail, data is missing, or an unexpected condition occurs. Confirm that errors do not disclose sensitive information or leave the application in an unsafe state.
- Dependencies and configuration: Review new packages, versions, permissions, secrets handling, and configuration changes. Confirm that each addition is needed and follows the project’s requirements.
- Scope and maintainability: Look for unrelated edits, duplicated logic, and changes that future maintainers cannot readily explain. A change that passes tests can still be a poor fit for the application.
Generated code that looks familiar or idiomatic is not self-validating. If the reviewer cannot explain a security-sensitive part of the change, it is not ready for approval.
Which tests and security checks should run?
Use the project’s normal development checks, including tests and security validation appropriate to the change. NIST calls for human validation and oversight of AI-generated content. OWASP and GitHub guidance likewise emphasizes review and testing; GitHub’s advice is vendor-authored product guidance, not independent empirical evidence.
- Run relevant unit, integration, and end-to-end tests where the project has them, including tests for expected failures and edge cases.
- Run the project’s established security checks and review any findings rather than treating a clean tool result as proof that the change is safe.
- Add or update tests for behavior introduced by the change, especially for authorization, input validation, and error paths where applicable.
- Use additional review for security-sensitive changes, such as code affecting authentication, authorization, sensitive data, or critical system behavior.
A passing test suite establishes only that the tested cases passed under the test conditions. It does not show that every behavior is correct, that security requirements have been met, or that the change is safe in production.
How to control AI agents and deployment access
AI agents can do more than suggest text: depending on their setup, they may interact with tools, repositories, or other systems. NIST identifies excessive privileges and unauthorized actions as risks. Limit access to what the AI system needs for its task, and keep proposed code, configuration changes, and corrective actions behind existing review and approval gates.
- Do not grant an agent production permissions merely because it can generate or fix code.
- Require human review before suggested changes are merged or applied to consequential systems.
- Keep deployment authorization within the organization’s established release process rather than allowing an agent to bypass it.
- Log actions and preserve enough context to trace what changed, who approved it, and how it passed the organization’s controls.
These controls matter even when an agent’s output is useful: an agent’s ability to make a change is not evidence that it should be authorized to approve or deploy that change.
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How to handle sensitive code and data
Follow organizational policy before sending source code, prompts, logs, or other context to an AI tool. NIST identifies data leakage as a risk, and GitHub’s guidance cautions users about context used by Copilot Chat and extra care with security-sensitive applications. Do not assume that information is suitable to share simply because it is needed to get a useful answer.
Decide what information the tool may receive, use approved configurations and services, and avoid including secrets or sensitive data unless policy explicitly permits it. The right boundary depends on the organization’s rules and the tool’s actual data-handling terms; the guidance cited here does not establish a universal configuration for every tool.
What NIST SP 800-218A does—and does not—establish
NIST Special Publication 800-218A, published July 26, 2024, is an AI-specific community profile that supplements version 1.1 of the Secure Software Development Framework (SSDF). It addresses secure development practices for generative AI and dual-use foundation models across their lifecycle and is intended for model producers, AI system producers, and acquirers.
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It is a companion profile for secure development practices, not a certification and not a guarantee that a particular generated application change is safe. It supports a process-based approach: keep established secure-development controls, adapt them for AI-related risks, and validate changes through accountable review and approval.
A practical deployment gate
- Assign ownership. Name the developer accountable for understanding, reviewing, and approving the AI-assisted change.
- Inspect the diff. Review the implementation, assumptions, inputs, authorization paths, error handling, dependencies, and configuration.
- Validate behavior and security. Run the project’s normal tests and security checks, and add coverage for the behavior introduced by the change.
- Check permissions and data handling. Confirm that any agent has only the access needed and that code or data shared with the AI tool complies with policy.
- Record and approve. Preserve the relevant change history and approval record, then send the change through the existing merge and deployment controls.
If the owner cannot explain the change, a required check has not run, a security concern remains unresolved, or the approval trail is missing, hold deployment until the issue is addressed. These controls reduce risk; they do not make software defect-free.
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