Home /Independent assurance

Can a second AI review your code? What independence really adds

Use AI review as a source of hypotheses while requiring independent expectations, reproduction and human ownership of release decisions.

Weekly fieldnotes / 23

Archive week: . This retrospective edition was published in September 2026.

Another model. Another opinion. — Independent assurance
CodeSignOff Fieldnotes · Independent assuranceDownload banner ↗

Separate a suggestion from a finding

A second assistant can suggest questions, locate suspicious code and propose tests. Treat those outputs as hypotheses until the relevant behaviour is verified. Two models agreeing does not establish that a business rule is correct. They may share the same incomplete description or make similar assumptions. The useful question is what new evidence the review produces and whether somebody accountable can explain it.

Supply an independent expectation

Write the intended behaviour outside the implementation before requesting review. For an organisation-based product, specify who may invite, remove and view members. Ask the reviewer to compare each path against that expectation. If the only specification is the code itself, a reviewer can mistakenly accept a coherent but incorrect design. Independence begins with a separate basis for judgement, not simply a different tool name.

Demand reproducible evidence

For an alleged cross-account access issue, identify the request, test identities and observed result in an authorised environment. For a maintainability concern, explain the concrete change that becomes difficult and why. Reject confident severity labels without an affected path or a clear limitation statement. OWASP’s secure review guidance offers useful context for a structured review, but the report still needs product-specific evidence.

Check what the assistant could not see

Document missing configuration, dependencies, runtime state and external services. A source review may not know which environment variables are deployed or which policy was changed in a dashboard. Ask the reviewer to identify these blind spots explicitly. Keep sensitive data and credentials out of unnecessarily broad prompts. A review that acknowledges missing evidence is more actionable than one that declares production ready after seeing only a subset of files.

Give the release decision a human owner

Use assisted review to improve the questions and accelerate verification. Assign a person to decide whether the evidence supports release, remediation or a narrower scope. Retain rejected findings with a brief explanation when doing so will prevent repeated confusion. The objective is not to prove that one model is smarter than another; it is to make the product’s important promises testable and the remaining uncertainty visible.

Sources & further reading

Reference material for the guidance and examples above. Where included, community discussions provide context rather than verified incident evidence.

Retrospective weekly fieldnote, prepared with AI assistance and published on 12 September 2026. Examples are illustrative; this is not a client case study or a claim about events in the assigned week.

Continue reading

← Back to all insights
A clear next step

Put these questions to your own codebase.

Tell us what you’re building and what’s coming next.
We’ll help you scope the right review.