Copilot · Cursor · Claude Code
Your team ships with Copilot and Cursor. Who audited what they wrote?
A tool-led AI code audit for engineering leaders who rolled out GitHub Copilot or Cursor — and now face security questionnaires, SOC 2, diligence, or production risk from unreviewed AI output.
GitHub Copilot and Cursor accelerate delivery — and create a review gap scanners cannot close
Rolling out GitHub Copilot, Cursor, or Claude Code is the easy part. The hard part is proving that AI-assisted merges are safe for enterprise buyers, auditors, and production. Maxiom runs a fixed-scope senior audit of repositories where those tools are in active use: security, architecture, compliance, dependencies, and test quality — delivered as a written findings package, typically within 5–10 business days. This landing is for teams searching specifically for Copilot or Cursor risk review; the engagement itself is our AI Code Audit.
- 2002
- Founded
- $100M+
- Delivered
- 98%
- Satisfaction
The problem
Copilot is on every laptop — review is not
Adoption metrics look great. Merge velocity is up. Senior review capacity did not scale with suggestion volume, and nobody owns the AI risk narrative for buyers.
Questionnaires now ask about AI coding tools
Enterprise security reviews and SOC 2 evidence requests ask how Copilot or Cursor output is controlled. Vague policy language is not enough without findings.
Scanners miss what Cursor confidently ships
Static tools catch some issues. They miss architectural shortcuts, licensing exposure, authorization edges, and compliance intent that AI assistants routinely invent.
What we inspect
Tool-aware senior review — written findings, not vibes
Every engagement is scoped to your Copilot / Cursor / Claude Code usage patterns and conducted by engineers with 10-plus years of production experience.
Security and secrets
OWASP-class issues, hardcoded credentials, unsafe defaults, and prompt-injection exposure in LLM-integrated features
Architecture and data flow
Coupling, schema assumptions, and design drift introduced by AI-generated scaffolding
Compliance gap analysis
SOC 2, HIPAA/FHIR, or FedRAMP/NIST mapping when your questionnaire or market requires it
Dependencies and licensing
Hallucinated or unvetted packages, copyleft contamination, and supply-chain risk
Tests on critical paths
Coverage quality where AI-assisted changes touch auth, payments, PHI, or tenancy
Maintainability and ownership
Whether the next engineer can safely inherit what Copilot and Cursor accelerated
Free resource
AI Code Risk Checklist (Copilot & Cursor teams)
A practical review list for teams shipping with Copilot, Cursor, and other AI coding tools — covering security, architecture, compliance, and code quality.
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
Is this right for you?
The Copilot rollout already happened
Leadership mandated GitHub Copilot. Six months later, security asks what was reviewed. You need a documented audit before the next board or buyer conversation.
Cursor is the default IDE
Engineers live in Cursor. PR volume is high. You need a senior pass on AI-heavy paths before launch, fundraise, or an enterprise security review.
SOC 2 evidence is thin on AI
Controls mention AI tools in policy, but there is no severity-ranked findings trail. An audit creates the evidence packet.
How a Copilot / Cursor 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 a Copilot or Cursor code audit?
It is a point-in-time senior review of code produced or heavily assisted by GitHub Copilot, Cursor, Claude Code, or similar AI coding tools. Engineers inspect a defined scope and deliver severity-ranked findings — not a scanner report marketed as oversight.
How is this different from Maxiom’s AI Code Audit page?
Same core engagement and deliverable. This page is the tool-led entry for teams searching Copilot or Cursor risk specifically. The AI Code Audit page is the broader category landing. Both convert into the same senior audit process.
Does Maxiom review only Copilot or Cursor output?
We scope to the repositories and paths where AI assistants are used most. That often includes mixed human and AI-authored code — because the risk lives in what merged, not in a perfect AI/human label.
Will this help with SOC 2 or enterprise security questionnaires?
Yes. Teams use the findings report to answer how AI-generated code is reviewed, what risks were found, and what remediation is prioritized — especially for SaaS SOC 2 and customer security reviews.
How long does a Copilot / Cursor audit take?
After NDA and read-only access, initial findings are typically shared within five business days, with full report and debrief commonly inside 5–10 business days depending on size and regulatory scope.
Do you process our source through third-party AI during the audit?
No. Client code stays in client environments. Senior engineers inspect directly. We do not run proprietary source through external AI tools as part of the engagement.
What if we need ongoing review after the audit?
Start with the audit for a baseline, then move to AI Code Oversight for recurring senior review as the team keeps shipping with Copilot or Cursor.
Is this the same as vibe coding governance?
No. Copilot/Cursor audits fit established engineering teams using assistants inside a normal process. Vibe coding governance is for products built primarily through prompting with limited formal engineering practice.
Can the audit include HIPAA or FedRAMP concerns?
Yes, when scoped. We can map findings to HIPAA/FHIR, SOC 2, or FedRAMP/NIST expectations, and connect remediation to Compliance Engineering when controls need to be built into the system.