AI code audit
A point-in-time audit of what AI wrote into your codebase.
Fixed-scope senior review for teams facing a fundraise, security questionnaire, compliance deadline, or production incident. Written findings. Named engineer. No scanners marketed as oversight.
A timeboxed AI code audit when you need answers before the next forcing function
AI coding tools accelerate delivery — and leave behind security gaps, architectural shortcuts, and compliance exposure that automated scanners miss. Maxiom's AI code audit is a point-in-time engagement: senior engineers inspect a defined repository scope, deliver a severity-ranked findings report, and walk you through remediation priorities. It is the right fit when you need a clear snapshot before due diligence, an enterprise sale, a HIPAA/SOC 2/FedRAMP review, or after an incident — not an ongoing review retainer.
- 2002
- Founded
- $100M+
- Delivered
- 98%
- Satisfaction
The problem
You need answers before a deadline
A fundraise, enterprise security review, or compliance questionnaire is coming. You need a clear snapshot of AI-assisted risk — not a multi-month oversight program.
Something already went wrong
A production incident, failed questionnaire, or near-miss traced back to unreviewed AI output. Leadership wants a structured audit of what else is in the codebase.
Velocity outpaced review quality
Copilot and Cursor accelerated merges. Review capacity did not. You need a senior engineer to inspect a defined scope and tell you what actually matters.
What the audit covers
Senior engineers. Fixed scope. Written findings.
Every AI code audit is conducted by engineers with 10-plus years of production experience. Scope is agreed up front so you get a complete, actionable report — not an open-ended review.
Security analysis
OWASP Top 10 plus AI-specific patterns including prompt injection in LLM-integrated code
Architecture review
Against your stated system design and near-term roadmap
Compliance gap analysis
HIPAA/FHIR for healthcare, SOC 2 for SaaS, FedRAMP for federal
Dependency and licensing review
GPL contamination, unvetted OSS packages, supply-chain risk
Test coverage assessment
Coverage quality and critical-path gaps — not vanity percentages
Maintainability scoring
Structured scoring for long-term team velocity and inheritance risk
The AI Code Risk Checklist
Security
- ·Injection vulnerabilities in AI-generated query construction
- ·Authentication and authorization logic gaps
- ·Hardcoded or improperly handled secrets
- ·Insecure dependencies introduced without review
- ·Prompt injection exposure in LLM-integrated features
Architecture
- ·Unintended coupling between modules
- ·Data model assumptions conflicting with existing schema
- ·Performance anti-patterns (N+1 queries, unbounded pagination)
- ·Missing error handling and edge case coverage
Compliance
- ·PHI handling that does not meet minimum necessary standards
- ·Audit logging gaps for covered transactions
- ·Data residency and encryption-at-rest assumptions
- ·Third-party integrations without compliant data agreements
Code quality
- ·Test coverage on security-critical paths
- ·Dead code and duplicate logic from AI regeneration
- ·Documentation accuracy vs. actual behavior
Download the full AI Code Risk Checklist
Is this right for you?
The diligence deadline
Investors, acquirers, or enterprise buyers are about to look at your code. You need a severity-ranked audit before they do.
The questionnaire wall
A customer security review asks how AI-generated code is reviewed. You need documented findings — not a vague process claim.
The post-incident snapshot
Something broke in production. Leadership wants an independent audit of AI-assisted areas before the next release.
How an AI code audit works
- NDA signed before access
- Read-only repository only
- Senior engineers every time
- Report in 10 business days
Frequently asked questions
What is an AI code audit?
An AI code audit is a fixed-scope, point-in-time review of code produced or heavily assisted by AI tools such as GitHub Copilot and Cursor. Senior engineers evaluate security, architecture, compliance, dependencies, and test quality, then deliver a written findings report with a live debrief.
How is an AI code audit different from AI code oversight?
An AI code audit is a one-shot, timeboxed engagement with a defined deliverable — typically findings within 5–10 business days. AI code oversight is ongoing or recurring review as your team continues shipping with AI tools. Start with an audit when you need answers now; move to oversight when you want continuous senior judgment.
Who needs an AI code audit?
Teams in healthcare, fintech, federal contracting, and enterprise SaaS who use AI coding tools and face a near-term forcing function: customer security questionnaires, compliance reviews, fundraising due diligence, acquisition diligence, or a production incident tied to unreviewed AI output.
How long does an AI code audit take?
Standard audits share initial findings within five business days after read-only repository access and scoping, with full report and debrief typically within 5–10 business days. Timeline depends on repository size, stack complexity, and regulatory scope.
What does the AI code audit deliverable include?
A written findings report with severity-ranked issues across security, architecture, compliance, dependencies, and test quality — plus a live debrief and prioritized remediation guidance from a named senior engineer.
Does Maxiom run client code through third-party AI tools during the audit?
No. Client code stays in client environments. Audits use direct inspection by senior engineers — not automated scanners or third-party AI processing of proprietary source.
Can an AI code audit cover HIPAA, SOC 2, or FedRAMP concerns?
Yes. Audits can include compliance gap analysis mapped to your framework — HIPAA/FHIR for healthcare, SOC 2 for SaaS, and FedRAMP/NIST alignment for federal environments.
How is repository access handled?
NDA first, then read-only repository access by default. Least-privilege permissions are scoped to the engagement; build access is only added if you engage remediation afterward.
How is this different from vibe coding governance?
An AI code audit reviews AI-assisted code inside an established engineering team. Vibe coding governance assesses products built primarily through AI prompting for production readiness before fundraise, launch, or team inheritance.
Need a clear picture of AI risk before the next forcing function?
A scoping conversation takes 30 minutes. Most audits deliver findings within 5–10 business days after access.
- NDA signed before access
- Read-only repository only
- Senior engineers every time
- Report in 10 business days
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