Security review gate for AI-assisted and AI-generated code
A security review gate for AI-assisted or AI-generated code adds three checks a human-only review skips: proving every dependency the assistant named actually exists, scanning for the insecure patterns AI tools repeat most, and requiring a named human reviewer who can explain the change before it merges to production.
Book a solution review Talk to the delivery and quality deskBy Netbase Delivery and Quality Desk · Reviewed by David (CEO) · Updated · 9 min read
This guide is for engineering leads, CTOs and platform or security engineers whose developers already use AI coding assistants, and for outsourcing buyers who want AI-assisted delivery kept under human control. Our software quality and delivery governance guide sets the wider release process; this page goes deeper on one checkpoint inside it, and quality engineering and testing runs that checkpoint day to day.
In this guide
- What makes AI-assisted code different from code a developer wrote alone?
- Four failure modes AI tools add, and the check that catches each
- The four-stage gate
- Where AI fits
- What delivery record exists, and what does not
- Alternatives: who runs the gate, and the trade-off of each
- Limits of this guide
- Frequently asked questions
- Next step
What makes AI-assisted code different from code a developer wrote alone?
An AI assistant can produce code that compiles, passes its own tests and still carries three risks a human author rarely introduces at the same rate: a dependency that does not exist, a security pattern copied from the statistically common (not the correct) answer, and a confident explanation that hides what the change actually does. The code itself is not inherently worse; the problem is that a reviewer's usual signal, "the author understands what they wrote," is weaker when the author is a model predicting the next plausible line. A gate has to replace that missing signal with explicit checks instead of assuming it.
Four failure modes AI tools add, and the check that catches each
| Failure mode | Why it happens | The check that catches it | When it is most likely |
|---|---|---|---|
| A dependency that does not exist (package hallucination) | The model names a plausible package it has seen a pattern for, not one it verified | Resolve every new dependency against the real registry and a vulnerability database before the build proceeds | New language, new framework, or a request phrased unusually |
| An insecure pattern repeated at scale | The model reproduces the most common example from its training data, which is not always the safest one | Static analysis and a security-focused rule set run on every AI-touched file, not only on files a human flagged | Input handling, authentication, error handling and anything resembling boilerplate |
| A confident but wrong explanation of the change | The same model that wrote the code also explains it, so a flawed assumption appears consistent on both sides | A named human reviewer re-derives the change from the ticket and the diff, not from the assistant's summary | Larger diffs, and changes the requester did not fully specify |
| A secret or production value pasted into a generated example | The assistant was given, or found, real configuration and reused it in a sample or test | Secret scanning on every commit, including comments and test fixtures | Debugging sessions, and tests written against a real environment |
The four-stage gate
-
Tier the change by what it touches
A change to authentication, payments, secrets, access control or production data gets the full gate below; an internal tool or a documentation change gets automated checks only. Tiering is the single decision that keeps the gate fast enough to use.
-
Run automated checks before a human looks at it
Dependency resolution against a real registry, static analysis, and secret scanning run on every AI-touched file; a change that fails any of them never reaches a reviewer's queue.
-
Route the change to a named human reviewer
The reviewer re-derives what the change does from the ticket and the diff, asks the author (human or AI-assisted) to explain one part in their own words, and approves or sends it back; approval is recorded against that person, not against "AI review passed."
-
Log the release record
Which files an AI tool touched, which checks ran, and who approved it are recorded with the release, so an incident review or an audit can trace a production change back to its origin without reconstructing it from memory.
This sequence does not replace an existing CI pipeline; it adds the dependency-verification and named-approval steps a pipeline built for human-only changes usually lacks, and it tiers effort so routine changes stay fast.
Where AI fits
Treat an AI coding assistant the same way you would treat an unfamiliar contributor: useful, productive, and not trusted by default. Every suggestion still passes through the same tests, scans and review as a human-written change; the gate above adds the two checks that a human-only process does not need (dependency verification and the named re-derivation step), rather than replacing existing controls. Netbase works with the major commercial and open-source AI coding tools and models, chosen per project rather than tied to one vendor.
-
Available capability
AI-assisted engineering under human review
Security by design from the start of a project, with AI-assisted changes held to the same secure code review, version control and scanning as any other change; not yet tied to a published AI-code-gate case.
What delivery record exists, and what does not
- What exists. Netbase's published security practices include secure code review and version control, vulnerability scanning and penetration testing, and role-based access with MFA for admin access; security is stated as a practice built in from the start of delivery, alongside AI-assisted engineering kept under human review. Netbase also works as offshore development and managing partner on a multi-tenant cloud ERP for a US client, a production SaaS system of the kind this gate protects.
- What does not. No published Netbase record states how many AI-generated changes have been gated, how many were rejected, or any defect or vulnerability count tied to AI-assisted code; this guide is a framework to apply, not a report of Netbase's own metrics. Netbase does not publish certificate numbers, audit dates or scope for ISO 27001 or SOC 2.
Alternatives: who runs the gate, and the trade-off of each
| Approach | Strength | Weakness | Choose it when |
|---|---|---|---|
| Existing reviewers, no new tooling | No new cost or rollout | Misses dependency hallucination and relies on reviewers noticing AI involvement unprompted | A small team, early-stage product, or a trial period before investing in tooling |
| Automated scanning added to CI, human review unchanged | Catches dependency and pattern issues before a human spends time | Still depends on the human step for the "does the author understand this" signal | Most teams once AI-assisted commits become routine |
| Dedicated AI-code reviewer role or rotation | One person builds the judgement to spot plausible-but-wrong explanations quickly | Needs a team large enough to dedicate the role, and a single point of expertise | Regulated or high-change-volume teams, or where the full four-stage gate runs often |
| Independent security assessment of AI-assisted releases | Fresh, unbiased check against the whole pipeline, not one change at a time | Periodic, not a merge-time gate, and adds cost | Before a major release, after a security incident, or to satisfy a customer's audit |
Choose by three criteria: how much of the codebase AI tools already touch, whether the product handles payments, personal data or production access, and whether a customer contract or regulation requires a documented review process. Most teams need the second row as a baseline and add the third as AI-assisted volume grows.
Limits of this guide
- It is a framework for designing the gate, not a specific tool recommendation; which scanner, registry check or CI integration to use depends on your stack.
- It assumes AI-assisted code already goes through the same tests and functional review as any other change; this guide adds to that process, not instead of it.
- Regulated sectors add their own audit and documentation requirements beyond what is described here; take specialist advice for those.
- The failure modes named here (dependency hallucination, repeated insecure patterns, confident-but-wrong explanations, pasted secrets) are the ones with published research behind them as of this guide's publish date; treat the list as a starting point, not a complete taxonomy.
Plan the next step with a Netbase consultant
Frequently asked questions
It needs the same tests, scans and human sign-off, plus two checks a human-only process usually skips: verifying that every new dependency actually exists, and having the reviewer re-derive the change from the ticket rather than trust the assistant's own summary of it.
It is a software dependency name an AI tool suggests that does not exist in the real package registry; an attacker who predicts which hallucinated names a model repeats can register that name and ship malicious code to anyone who installs it without checking, which is why dependency resolution belongs in the automated stage of the gate.
No. Tier the change first: a change touching authentication, payments, secrets or production data gets the full gate, while a low-risk internal change can pass with automated checks alone, so the gate stays fast enough that teams keep using it.
A named human reviewer, recorded against their name in the release log, not "AI review passed." The point of the gate is a person who can explain the change, not a tool that ran without one.
Next step
Send us your current review process, and we will book a solution review to map the four-stage gate onto your pipeline and tooling. For the vendor-selection questions to ask before AI-assisted work starts, see security questions to ask a development partner; for the general release go/no-go checklist, see acceptance criteria and release readiness; and for securing an AI agent that acts at runtime rather than one that helps write code, see AI agent security and human approval controls. You can also browse more Netbase insights or the SaaS product accelerator solution.
Work behind this guide
Since 2020 Netbase has worked remotely as its offshore development and managing partner; phase 1 brought CRM, messaging, HR, a knowledge base, work management and API integrations into one platform for agency customers.
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