Secure AI-generated code exactly as you would security-sensitive human code, but add controls for model context, agent permissions, provenance, and traceability. Before deployment, require an independent human review, run automated security and quality checks, verify dependencies and signed build metadata, enforce severity thresholds, and obtain accountable approval. An AI agent must never be the only reviewer or have unapproved authority to change production.
The practical workflow below turns that principle into gates you can apply to every pull request that contains generated or AI-modified code.
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Why AI-generated code needs an enhanced delivery process
Generated code is not inherently unsafe, and human-written code is not inherently safe. The security problem is that an AI system can produce a large, plausible-looking change quickly while lacking your application’s full threat model, data-classification rules, operational history, or accountability. It can also read more project context than you intended, suggest vulnerable dependencies, reproduce insecure patterns, or act on malicious instructions hidden in a repository or pull request.
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That changes the controls around the normal secure software development life cycle (SSDLC), not the need for the SSDLC itself. NIST’s SP 800-218A, published July 26, 2024, is a Community Profile for generative-AI development that augments the base Secure Software Development Framework (SSDF). Its guidance says AI outputs should be traceable, pass established DevSecOps gates, be logged, and be approved by accountable stakeholders before use. AI should not independently deploy or modify production.
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OWASP’s AI-assisted coding guidance reaches the same operational conclusion: the person or agent that generated a change cannot provide independent assurance about it. Treat the generated diff as an untrusted proposed implementation until it has passed your normal controls plus AI-specific checks.
Set a written policy before anyone asks an agent to write code
A secure process starts before the first prompt. Document the allowed tools, data classifications, environments, repositories, and actions. OWASP AISVS control AC.1 calls for a written workflow spanning design through monitoring and naming mandatory gates. A short policy is better than informal advice because it gives engineers and reviewers a consistent decision rule.
Define approved use
- List which coding assistants, model endpoints, plugins, and agent modes are approved.
- State whether generated code may be used in production, internal tools, test fixtures, infrastructure, or security-sensitive components.
- Identify files and services that require a second reviewer or security-team approval, such as authentication, authorization, cryptography, payment handling, secrets management, network policy, and deployment configuration.
- Specify who may approve a merge, who may approve a policy exception, and who owns the final production decision.
Classify prompt and repository data
Define what may enter a prompt or be exposed through an agent’s workspace. Common categories include public information, internal source code, confidential business data, personal data, regulated records, credentials, private keys, certificates, and production data. For each category, state whether it is prohibited, allowed only with an approved provider control, or allowed with logging and review.
Write prohibited actions and escalation paths
- Prohibit agents from deploying to production, changing production configuration, rotating credentials, approving their own pull requests, or disabling security checks.
- Require escalation when an agent requests a secret, attempts an unapproved network connection, encounters an instruction embedded in code or documentation, or proposes bypassing a gate.
- Record exceptions with the affected asset, risk, compensating control, expiry date, and named human approver.
Threat-model the AI workflow, not just the application
Traditional application threat modeling still applies, but the generation path introduces additional assets and attack surfaces. Include the model provider, prompts, retrieved files, tool calls, generated artifacts, build system, and audit records in the diagram.
Prompt injection through project content
An agent may treat text in a README, issue, test fixture, source comment, or pull request as an instruction. An attacker who can alter that content could try to make the agent disclose secrets, weaken validation, add a dependency, or execute a command. Mark repository content as untrusted input. The agent’s system policy and tool permissions must take precedence over instructions found in files.
Secret exposure and data exfiltration
Assistants can send broader project context to a provider than the developer realizes. A .gitignore file controls Git tracking; it does not prevent an assistant from reading a file on disk. Assume that any file available to the agent may be included in context unless the provider or a local proxy explicitly excludes it.
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Untrusted generated dependencies and infrastructure
Generated code can add a package with a look-alike name, select an outdated version, loosen a version range, or create an insecure container, cloud policy, or CI configuration. Dependency and infrastructure checks must therefore be independent of the model and run against the final artifact.
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An agent with shell, network, repository-write, or deployment access can turn a coding mistake into an incident. Give each tool the narrowest permission needed for the task, separate read and write capabilities, and require an explicit human action for irreversible operations.
Protect model context, credentials, and tools
Context isolation is the first technical control. It limits what the model can learn and what a malicious repository instruction can influence.
Keep sensitive files out of the workspace
- Exclude
.envfiles, private keys, certificates, credential stores, cloud metadata, regulated records, and production exports from the agent’s readable directories. - Use environment variables, a secrets vault, or an encrypted store for runtime credentials. Never paste a secret into a prompt or commit it to a generated patch.
- Use a clean, least-privilege checkout for generation. Do not mount a developer’s entire home directory or a production filesystem.
- Review provider retention, training-use, regional-processing, and access-control settings before sending proprietary code.
Constrain tool calls
- Allow read-only commands by default and require confirmation for writes, network access, package installation, and process execution.
- Restrict file paths, outbound hosts, and available credentials with operating-system or container controls.
- Log prompts, tool calls, returned context, generated diffs, approvals, and blocked actions in a system with controlled access.
- Expire temporary tokens and use separate identities for local development, CI, staging, and production.
Detect attempts to override policy
Train reviewers to recognize phrases such as “ignore previous instructions,” requests to print environment variables, instructions to disable scanners, or claims that a test failure should be hidden. Treat these as security events or suspicious input, not as normal coding guidance. Preserve the triggering content so the team can improve filters and policy.
Review the generated diff with an independent engineer
OWASP AISVS AC.4.1 requires review by a qualified human engineer and separation of duties. The AI agent does not count as the reviewer, and the person who generated the change should not be the only person approving it. For high-risk components, require two-person review or a security-team sign-off.
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Use a security-focused review sequence
- Read the requirement first. Confirm the change solves the stated problem and does not introduce unrequested behavior, hidden telemetry, or broad permissions.
- Trace trust boundaries. Identify every external input, privilege transition, service-to-service call, file operation, queue, and data-store write.
- Check authentication and authorization. Verify identity checks, object-level authorization, tenant isolation, session handling, and default-deny behavior. Do not accept a comment claiming that a check exists; follow the execution path.
- Validate and encode data. Confirm server-side validation, length and type limits, canonicalization, output encoding, and safe query construction for each input and output context.
- Inspect cryptography. Check algorithm selection, key generation, storage, rotation, randomness, certificate validation, and error handling. Reject custom cryptographic schemes.
- Examine deserialization and file handling. Look for unsafe object deserialization, path traversal, archive extraction issues, temporary-file races, and unrestricted uploads.
- Review network and process access. Confirm allowlists, timeouts, TLS verification, redirect handling, command construction, and sandbox boundaries.
- Review errors and logs. Ensure logs support investigation without exposing tokens, personal data, stack traces, or sensitive request bodies. Check that failures fail closed where appropriate.
- Inspect dependencies and configuration. Verify versions, licenses, lockfiles, container bases, cloud permissions, CI settings, and feature flags.
- Compare tests with the threat model. Look for tests of abuse cases and negative paths, not only the happy path.
Require evidence of what the agent changed
Keep the prompt or task identifier, model or agent identity, generation timestamp, repository commit, generated diff, human reviewer, and tool versions with the change record. If the agent made several attempts, retain the final accepted diff and enough history to explain material decisions. Traceability makes later incident analysis possible and discourages unreviewed copy-and-paste.
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Run independent automated checks on every AI-code pull request
OWASP AISVS AC.4.2 names static application security testing (SAST), interactive testing (IAST), dynamic testing (DAST), secret scanning, infrastructure-as-code (IaC) scanning, and software-composition analysis (SCA). Use the checks that match the artifact, and run them from CI or another system separate from the generating agent.
| Check | What it examines | When it is most useful | Typical release action |
|---|---|---|---|
| SAST | Source and data-flow patterns such as injection, access-control errors, and unsafe APIs | Every supported language and every pull request | Fail or require triage for policy-defined severities |
| SCA and lockfile review | Direct and transitive dependencies, versions, licenses, and known vulnerabilities | Any change to manifests, lockfiles, images, or build files | Block disallowed or critically vulnerable components |
| Secret scanning | Keys, tokens, passwords, certificates, and high-entropy credentials | Diffs, full history where appropriate, and build artifacts | Revoke exposed credentials and reject the change |
| IaC scanning | Cloud, container, Kubernetes, CI, and policy configuration | Infrastructure or deployment changes | Block overly broad permissions and unsafe defaults |
| DAST | Running application behavior from an external perspective | Deployed test or staging environments | Open a finding or stop promotion for exploitable issues |
| IAST | Runtime data flow while instrumented tests execute | Integration and end-to-end test stages | Correlate code paths with observed vulnerabilities |
| Unit, integration, property-based, and abuse-case tests | Functional requirements, invariants, boundaries, and attacker behavior | Every behavior change, especially parsers and authorization | Require reproducible results independent of the agent |
OWASP’s Secure Coding with AI guidance states that a passing test suite generated by the same agent that produced the code provides no independent assurance. A model can write tests that encode its own mistaken assumptions or omit the dangerous path it introduced. Have a human author or independently review security tests, and run them in a separate CI job.
Make the checks reproducible
- Pin scanner versions or record them with every run.
- Store machine-readable results, commit identifiers, configuration, and suppression justifications.
- Run the same mandatory checks on retries; do not silently skip a failing job because the code was generated.
- Use a clean build environment so a local agent cannot alter scanner configuration or inject prebuilt artifacts.
Set severity thresholds and handle exceptions explicitly
OWASP AISVS AC.4.3 recommends blocking merges for critical findings. Its example threshold is CVSS 9.0 or higher, or the organization’s equivalent. Adapt that threshold to your risk model, but write it down before a finding appears.
Example policy matrix
| Finding state | Default decision | Required evidence |
|---|---|---|
| Critical or exploitable high severity | Block merge and promotion | Remediation, verified retest, and reviewer approval |
| Medium severity with reachable impact | Fix before release or document a time-bounded exception | Impact analysis, compensating control, owner, and expiry |
| Low severity or confirmed false positive | Track and resolve under normal backlog rules | Reproducible rationale and suppression owner |
| Missing scan, provenance, or reviewer | Quarantine; do not promote | Completed gate and immutable audit record |
An exception must be an authorized human decision, not an agent suggestion. Record the finding, affected version, business reason, compensating controls, approver, and expiration date. When the exception expires, the pipeline should reopen the gate automatically.
Verify dependencies, artifacts, and provenance before promotion
Security checks on source are not enough if the artifact cannot be tied to the reviewed source. NIST SSDF practice PW.8 calls for executable-code testing that finds vulnerabilities and verifies requirements before release. Supply-chain controls extend that assurance from source to the exact package or image you deploy.
Build a traceable artifact
- Pin dependencies and review every new direct and transitive component.
- Generate a software bill of materials (SBOM) for the build output and retain it with the artifact.
- Record the source commit, build definition, compiler or packaging tool, dependency lockfile, scanner results, and artifact digest.
- Scan acquired models, plugins, containers, and other components when they are part of the delivered system.
Attach signed generation metadata
OWASP AISVS AC.9 calls for signed origin and generation metadata and trusted verification before promotion. The record should identify the AI system or tool, relevant generation context, humans involved, source revision, build environment, and audit records. Sign the provenance and the artifact, then verify both with trusted tooling in the promotion pipeline.
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Reject or quarantine an artifact when required metadata is missing, the signature is invalid, the digest does not match, the build came from an unapproved environment, or the source revision was not reviewed. Do not “fix” a provenance failure by attaching metadata after deployment; rebuild from a verified source.
Approve deployment and keep monitoring after release
Passing checks does not transfer accountability to the model. An accountable human must approve the release, and the audit trail should link the task or prompt, generated diff, review, scan results, exceptions, artifact digest, and deployment event.
Use a staged release
- Promote the verified artifact to a non-production environment.
- Run integration, DAST, IAST, performance, and abuse-case checks that require a live service.
- Compare runtime permissions, network calls, logs, and data access with the threat model.
- Obtain the named release approval and record the decision.
- Deploy using the signed artifact digest rather than rebuilding from an unpinned branch.
- Monitor authentication failures, authorization denials, error rates, unusual outbound traffic, secret access, and resource consumption.
Feed incidents, near misses, false positives, and reviewer findings back into the secure-development process. AI-generated remediation is still a proposed change: it must pass the same review, tests, provenance checks, and approval gates as the original code.
Common failure modes and how to recover
“The assistant only saw the files I selected”
That assumption is unsafe when the tool can search the workspace, follow imports, inspect environment variables, or invoke shell commands. Move generation into a restricted checkout, remove sensitive files, disable unnecessary tools, and inspect provider context controls. If a secret may have been exposed, revoke and rotate it immediately.
“The generated tests pass, so the change is safe”
Tests written by the same agent can share the implementation’s blind spots. Add independently authored tests, static and dynamic analysis, dependency checks, and abuse cases. A green result from one agent is evidence of behavior under those tests, not independent security assurance.
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Disablement creates a blind spot that can affect future changes. Keep the check enabled, document a narrowly scoped suppression with an owner and expiry, and verify the finding manually. Correct the rule or its configuration when possible.
“The agent added a familiar package”
Package names can be mistyped, abandoned, compromised, or selected at an unsafe version. Confirm the component’s provenance, maintainer information, version history, license, vulnerability status, and transitive dependencies. Prefer an approved internal catalog and pinned lockfiles.
“We can add provenance later”
Metadata created after the fact cannot prove how an artifact was generated or reviewed. Quarantine the artifact, rebuild in the approved environment, sign the new output, and rerun the promotion checks.
“The agent can deploy because it has done so successfully before”
Past success is not an authorization model. Remove production credentials from the agent, require a human approval step, and use a deployment identity that accepts only verified, signed artifacts.
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A practical CI/CD gate sequence
The following sequence works for a pull request that contains AI-generated or AI-modified code:
- Classify the change. Mark the pull request as AI-assisted and identify whether it touches security-critical code, infrastructure, dependencies, or sensitive data.
- Validate context controls. Confirm that prohibited files and data were not available to the agent and that tool permissions match the policy.
- Require independent review. Assign a qualified reviewer who did not generate the change; add a second reviewer for designated high-risk areas.
- Run the full automated set. Execute SAST, SCA, secret, IaC, and applicable IAST, DAST, unit, integration, property-based, and abuse-case tests.
- Triage findings. Block critical issues and document any authorized exception with a deadline.
- Build and inventory. Generate the artifact, SBOM, digest, and complete scan record from a clean environment.
- Sign and verify provenance. Attach generation and review metadata, sign it, and have the promotion system verify the signature and source relationship.
- Approve and deploy. Obtain accountable human approval, deploy the verified digest to staging, complete live checks, and promote the same artifact.
- Monitor and learn. Watch runtime signals, preserve the audit trail, and feed incidents or near misses into policy and training updates.
How to choose tools and process controls
There is no single scanner or agent setting that establishes trust. Compare candidate controls on the dimensions that determine whether a gate is useful in your environment.
| Decision axis | Questions to ask |
|---|---|
| Coverage | Which languages, frameworks, containers, infrastructure formats, dependencies, and runtime paths are analyzed? |
| Independence | Does the check run outside the generating agent, with separately controlled configuration and credentials? |
| Blocking and exceptions | Can policy block merges, require approval, expire exceptions, and prevent silent bypasses? |
| Secret and data handling | What context leaves the environment, how is it retained, and can sensitive paths and outbound destinations be restricted? |
| Provenance and auditability | Can the system connect the prompt or task, source revision, reviewer, scan results, artifact digest, and deployment? |
| CI/CD integration | Does it support your pull-request, build, registry, staging, and promotion workflow without manual copying? |
| False-positive workload | Can findings be explained, triaged, suppressed narrowly, assigned, and revisited before an exception expires? |
| Total cost | Include licensing, compute, storage, reviewer time, remediation, integration, and the cost of delayed releases. |
What you can and cannot conclude about AI-code defect rates
Authoritative guidance does not establish a universal, decision-ready defect-rate percentage for AI-generated code across tools, languages, prompts, repositories, and review practices. A percentage from one experiment should not be used as a release threshold for another environment. Make decisions from the controls and evidence for the specific change: independent review, reproducible tests, scanner results, dependency status, provenance, and runtime monitoring.
Minimum release checklist
- The AI tool, model endpoint, data classification, and permitted actions comply with written policy.
- Sensitive files, credentials, private keys, certificates, and regulated data were excluded from model context.
- The pull request identifies AI involvement and includes a traceable generated diff.
- A qualified human who did not generate the change reviewed the implementation; high-risk files received the required additional review.
- SAST, SCA, secret, IaC, and applicable dynamic or interactive tests completed in independent CI.
- Unit, integration, property-based, and abuse-case tests were authored or independently reviewed rather than accepted solely because the agent produced them.
- Critical findings are fixed or an authorized, time-bounded exception exists.
- Dependencies are pinned and reviewed; the artifact has an SBOM and immutable digest.
- Signed provenance identifies the generation context, source, build, tools, reviewers, and audit records.
- The promotion system verified signatures and rejected missing or untrusted metadata.
- An accountable human approved deployment, and post-release monitoring is active.
The bottom line
AI-generated code is deployable when it earns the same trust as any other production change. Keep the model away from secrets and unsupervised production actions, make an independent engineer responsible for review, run broad automated checks on every relevant pull request, enforce documented severity thresholds, and promote only artifacts with verified provenance and human approval. These controls let teams gain speed from code generation without treating plausible output as proof of security.
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