Should organisations trust AI explanations when approving external dependencies?

Why AI explanations are useful, but not sufficient, for dependency approval

An AI explanation can help reviewers understand what a dependency claims to do, how it was generated, and what assumptions it seems to rely on. That makes it a useful screening aid. It is not, however, evidence that the code is safe to ship, because the highest-risk behaviour often appears only when the dependency is executed, composed, or exposed to real inputs.

For approval decisions, the right question is not whether the explanation sounds plausible, but whether the dependency has been independently verified against the environments and trust boundaries it will actually face. That means treating the explanation as a hypothesis to test, not as a substitute for testing.

Explanation quality also varies with model behaviour, prompting, and context window limits. A dependency can be described accurately in one prompt and gloss over its most dangerous edge case in another. Security teams should assume the explanation is partial unless independent evidence says otherwise.

What must be checked before an external dependency is trusted

External dependencies should be reviewed for API security failure modes, package provenance, transitive behaviour, and the privilege context in which they will run. A dependency can look benign in isolation and still become risky when it reaches sensitive data, privileged functions, or network-reachable services.

Independent scanning matters because static claims do not surface all code paths, bundled components, or post-install behaviour. Behavioural testing matters because some problems only emerge at runtime, such as unexpected outbound connections, unsafe deserialisation, or dependency-chaining effects that only appear under specific inputs.

Provenance checks matter because the trust question is not only "what does the code do?" but also "who published it, how was it built, and can the version you received be tied back to a known source?" For identity-sensitive dependencies, verification should extend to secrets handling, signing, and publishing controls, not just the package description.

How to structure review so the explanation stays in the right role

AI explanations are best used as a triage layer. They can speed up review by highlighting likely purpose, dependencies, and claimed safeguards, but they should not be allowed to close the review path on their own. The safe sequence is: read the explanation, derive the risk hypothesis, then verify that hypothesis with code analysis, runtime testing, and provenance evidence.

Where the dependency affects authentication, authorisation, or token handling, the review should be anchored in the actual control model rather than in the model's narrative. A dependency that touches trust decisions should be tested for least privilege, boundary enforcement, and failure behaviour under malformed or malicious inputs. If those checks are absent, the explanation is not a sufficient basis for approval.

At scale, the practical issue is reviewer over-trust. Teams can become conditioned to accept fluent explanations as if they were audit evidence. The correct operating model is to use the explanation to focus human attention, then require independent artefacts before acceptance.

Risk and Threat Considerations

External dependencies create supply-chain exposure because the dangerous behaviour may be hidden in a later execution path, a transitive component, or a package update that was not obvious in the explanation. Attackers also benefit when reviewers mistake a well-written summary for assurance and skip the checks that would have exposed malicious or simply unsafe behaviour.

Failure mechanism: The dependency is approved on narrative confidence, but the review never validates runtime behaviour, provenance, or effective privilege boundaries, so unsafe code is admitted on a false basis of trust.

Impact: That can lead to compromised build integrity, unexpected data access, remote code execution, or downstream compromise of systems that imported the dependency as trusted software.

Practitioner Guidance

What to verify: Require three independent signals before approval: the explanation, a technical scan or review result, and a provenance check that ties the artefact to a known source and version. If any one of those is missing, treat the dependency as not yet approved rather than "probably fine."

Decision rule: If the dependency will run with elevated privileges, reach sensitive data, or execute inside a build or deployment pipeline, give runtime evidence more weight than the explanation and escalate for deeper review when the behaviour cannot be reproduced.

Common mistake: Teams often use the explanation to justify why a dependency looks safe, then stop before testing the very execution context where the risk would appear. The explanation should narrow the search; it should not end it.

Practitioner takeaway: Trust the explanation only as a starting point, because dependency approval is a verification problem, not a narration problem.

Related resources from NHI Mgmt Group

• Should organisations trust package popularity when approving dependencies?
• When should organisations block an AI app instead of approving it?
• When should organisations restrict AI assistants from reading external context sources?
• Should organisations extend zero trust or adopt a dedicated AI governance platform?