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Set the review scope and risk before reading code
Start by establishing what the software is for, how it is structured, and what the proposed change affects. A small, isolated change may call for a focused review of its diff and affected dependencies. A new application, major release, or change to a high-impact system warrants a broader review of the application and its operational environment.
NIST’s Secure Software Development Framework (SSDF) is a useful way to organize that work. It groups practices around preparing the organization, protecting software, producing well-secured software, and responding to vulnerabilities. NIST describes SSDF as a basis for planning and continuous improvement, not a universal checklist. Match review depth to the application’s business or mission needs, risk tolerance, available resources, and criticality.
- Identify important data and other assets the software handles.
- Map trust boundaries: where users, services, external systems, or AI agents can provide input or trigger actions.
- Locate high-risk functions, such as access control, payments, sensitive-data handling, administrative operations, and production changes.
- Review relevant requirements, existing architecture, affected components, and prior security findings.
For reference, NIST SP 800-218 version 1.1 is final guidance published February 3, 2022. NIST’s publications listing also identifies SP 800-218 Rev. 1, version 1.2, as an initial public draft dated December 17, 2025; a draft should not be described as the final standard. NIST SP 800-218A, published in July 2024, adds practices for generative-AI and dual-use foundation-model development, including extending code-review and analysis policies to AI-model code and related components. It is a useful supplement, but its scope is model development—not a bespoke standard for every application made with a coding assistant.
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Make a person accountable for each change
Before merge, name the developer responsible for the change’s correctness, security, and future maintenance. That person needs to understand what the code does, review it, and approve it; an AI tool’s output or a green status check cannot take that responsibility. OWASP’s Secure Coding with AI Cheat Sheet says: “Every AI-assisted change should be reviewed, approved, and attributable to a developer who is responsible for its security and maintainability.”
Keep enough provenance to trace who approved the work and, when available, which AI tool and model version contributed. Provenance is especially important when code is generated or modified by agents, passed between people, or incorporated into a build whose origin may later need investigation.
Inspect sensitive paths from input to outcome
Read the code along the route data and actions take through the system, rather than reviewing generated files in isolation. Start at entry points, then follow validation, authorization, business logic, storage or external calls, and error handling. Check whether each boundary has the controls the application actually requires.
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- Authentication and authorization: verify how identity is established and whether each sensitive action checks the right permissions. Do not assume that hiding a control in the interface enforces access.
- Validation and business logic: inspect how untrusted input is constrained and whether the implementation preserves the intended rules. Context-specific business-logic mistakes can be hard for automated analysis to recognize.
- Data and cryptography: trace sensitive data into storage, logs, and external services; check that cryptographic operations and key handling are appropriate to their use.
- Configuration and deployment: examine secrets, permissions, environment-specific settings, and the way the change will run in production.
- Integrations and errors: scrutinize new services, APIs, and libraries, along with failure paths that might disclose data, bypass a check, or leave the system in an inconsistent state.
OWASP’s Secure Coding with AI Cheat Sheet and Secure Code Review guidance both support combining automation with manual inspection, particularly for business logic and context-dependent controls.
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Use tests and security tools as evidence, not a verdict
Choose analysis appropriate to the change: review relevant tests and use static or dynamic security analysis where it fits the application. Triage findings rather than treating every alert as a confirmed defect, and verify that fixes address the underlying issue. A clean tool report only describes what that tool and configuration detected.
Passing tests do not prove security. OWASP cautions against trusting AI-generated test suites as evidence without scrutiny and against using test pass rate alone as a confidence measure. Independently examine tests for security-critical behavior, including whether they exercise denied as well as permitted actions, boundary conditions, and failure cases. Confirm that the tests actually assert the required behavior rather than merely running without an error.
Check dependencies, builds, and AI-tool data exposure
Inspect new and changed dependencies for authenticity, maintenance, suitability, and version. Review build configuration as well as application code; a safe-looking change can still rely on an inappropriate package or introduce an unsafe build or deployment step. Coding tools may not know about CVEs published after their training cutoff or most recent security-index update, so check dependency risk against current sources rather than relying on the assistant’s answer.
Also establish what context the coding assistant sends to its provider. Depending on the tool and workflow, that context may include files, terminal output, credentials, personal data, or proprietary material. Understand the applicable data-handling behavior and exclude sensitive context where possible; do not paste secrets into a prompt as a shortcut.
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NIST’s DevSecOps reference model calls for AI-generated outputs to pass through established processes, including peer review, security validation, automated testing, and approval workflows. It identifies risks such as inaccurate output, insecure code, unauthorized actions, data leakage, and artifacts entering the software supply chain without provenance or approval.
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Accordingly, an agent should not independently deploy software or alter production state outside the organization’s existing controls. Keep its permissions bounded, preserve review and approval gates, and make sure generated artifacts can be traced to an accountable owner.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Review reliability and maintainability as product qualities
Security checks do not establish that a change is dependable or understandable. Verify it against the requirements and the project’s architecture, then examine the behavior the team will need to operate and maintain.
- Check expected behavior and edge cases, including error paths and recovery from failed operations.
- Confirm configuration is understandable and that dependencies are consistent with the project’s conventions.
- Assess whether logs, alerts, or other observability make important failures visible without exposing sensitive data.
- Read the implementation as a future maintainer: can the team explain the logic, identify its assumptions, and safely modify it?
- Check that tests cover behavior the requirements depend on and are maintainable alongside the code.
These are practical engineering checks, not a validated rubric specific to “vibe-coded” reliability or maintainability. The available evidence does not establish that vibe-coded software is inherently better or worse than conventionally authored software on either quality.
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Decide whether the change is ready to release
Before acceptance, make the decision from the risks and evidence gathered—not from how quickly the code was produced or whether a test dashboard is green. A reviewer can use these questions to reach and record a release decision:
- Is the change’s purpose, scope, and risk understood, with the affected assets and trust boundaries identified?
- Has a named developer reviewed and approved the change, and can its provenance be traced?
- Have sensitive data and authorization paths been inspected directly, including relevant failure behavior?
- Have tests and security-analysis findings been reviewed, with important results independently verified?
- Are dependencies, build configuration, and the AI tool’s data exposure acceptable for this application?
- Can the team operate, understand, and maintain the result, and will established approval controls remain in force?
If a material risk remains unexplained or an important behavior cannot be verified, do not treat generated code as ready merely because it compiles or passes its current tests. Resolve the uncertainty, narrow the change, or defer release according to the system’s risk and the team’s established process.
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