Secure Your Repositories with AI Code Audit & Cleanup
44% of AI code-generation tasks introduce a security vulnerability. We audit, verify, and clean up vibe code inside your repository. No rewrites.
✅Expert Verification: Flaws confirmed by an engineer, never a scanner alone.
✅Direct Remediation: Fixes land in your existing repository and workflow.
✅Defended ROI: Every fix priced before code is written.
ISO 27001 Certified
OraclePartner
SnowflakePartner
AWS Partner
For Engineering Leaders Who Can't Answer "What's in Our AI-Generated Code?"
AI now writes roughly half the code committed inside organizations that have adopted it. Your developers did not ask permission, and your security policy did not keep pace. The uncomfortable part is not that AI-generated code is wrong — it usually runs. It is that it passes the tests you wrote and fails the ones an attacker writes. Veracode scored cross-site scripting at a 15% pass rate and log injection at 12%. Those are the categories your scanner is worst at explaining.
An AI-generated code audit is a structured review of AI-generated and AI-assisted code against security, architecture, dependency, and compliance standards, performed by senior engineers rather than tooling alone. GrowExx scores what you have, ranks findings by business impact, and prices each fix — ROI defended before code is written.
The Three Layers of an AI Code Audit
Linters pass, tests are green, the build is clean, the app is live. Real signal — and the reason the risk stays invisible.
Secure by default, dependencies are real, access control holds under a hostile request, logic matches the business rule. In most AI-generated codebases, nobody has checked this layer.
Senior-engineer verification of every finding, a costed remediation plan, ISO 27001-aligned evidence for SOC 2 and investor diligence, and a permanent review gate in your CI/CD pipeline.
Our Vibe Code Audit & Cleanup Services
AI Code Security Scan
Static analysis, secrets scanning and dependency verification, then a manual AI code security review by senior engineers who confirm which findings are exploitable in your deployment, not just flaggable. Targets the three failures present in most AI-generated codebases: injection flaws, hardcoded secrets and broken access control. Your team fixes verified risk instead of triaging a scanner export.
Production Readiness Audit
A production readiness audit that answers one question with evidence: what breaks first at 10x your current load, and how long until it does? Covers architecture, database indexing, connection pooling, caching, error handling, test coverage, cloud IAM configuration and CI/CD pipeline integrity across AWS, GCP and Azure.
Expert AI Code Review
Senior engineers read the business logic no linter can judge — code that looks correct but fails the edge case only your company understands. Pricing rules, tenant isolation, permission tiers and payment flows get tested against how your customers use the product in practice, not against how the prompt described it.
Vibe Code Cleanup & Remediation
For applications generated with Lovable, Bolt, Replit, v0, Cursor or Claude Code. GrowExx engineers work the prioritized fix list inside your existing repository, critical findings first. No rewrite, no parallel codebase. Each sprint closes a defined set of findings and re-tests them before sign-off.
Ongoing AI Code QA
A validation gate inside your deployment pipeline checks every future AI-assisted commit for exposed secrets, dependency integrity and access-control regressions. Your team keeps its release velocity; the gate keeps the assumptions in Layer 2 true after the engagement ends. Runs in GitHub Actions, GitLab CI or your equivalent.
Compliance & Due Diligence
Findings report, remediation log, and architecture summary, structured the way SOC 2 assessors, enterprise security reviewers, and investor diligence teams ask for them. Documentation follows GrowExx's ISO 27001:2022-certified process. Diligence becomes a one-hour document review instead of a two-week fire drill.
AI Code Security Review, Matched to How Your Industry Operates
AI-generated code creates a different risk profile in every sector. Four audit playbooks, one method.
The Challenge
Multi-tenant isolation is the one thing a SaaS platform cannot get wrong, and it is exactly the thing AI-generated code gets wrong quietly. A tenant filter missing from one query path leaks another customer’s data without raising a single error. Add rapid AI-assisted feature cycles and permissive API defaults, and the gap between what your access-control policy says and what your code enforces widens with every release.
How GrowExx Helps
Deliverables
- Tenant isolation test results across every data path
- API authorization and rate-limit gap list
- Row-level security policy verification
- Severity-ranked findings register with fix cost
- CI/CD review gate for future commits
Quantified Result
The Challenge
Checkout, payment integration and inventory logic concentrate three kinds of risk in one codebase: regulated card data, personal information and peak-traffic load. AI-generated checkout flows commonly validate on the client and trust the server-side input, or recalculate price in a path the discount logic never sees. Both failures surface as revenue loss rather than as an error log, which is why they survive to Black Friday.
How GrowExx Helps
We trace every price, discount and inventory mutation to its authoritative source and test the paths that bypass it. Payment integration points are reviewed against PCI DSS expectations, and the checkout path is load-tested at peak multiples of current traffic so the bottleneck is found before the sale is.
Deliverables
- Checkout and payment flow security review
- Price and inventory logic integrity tests
- PCI DSS alignment gap list
- Peak-load profile at current and 10x traffic
- Prioritised fix list with cost of inaction
Quantified Result
The Challenge
Financial applications carry obligations no model was trained to enforce. AI-generated code handling transactions, KYC workflows or payment integrations will produce a function that works and omit the audit trail, the idempotency key, and the encryption-at-rest requirement that make it legal. Regulators and enterprise buyers both ask for documented human oversight of AI-assisted development — and a scanner report is not that.
How GrowExx Helps
We review transaction integrity, idempotency, encryption and access control against the frameworks your supervisor and your buyers check, then map each finding to the specific control it affects. The remediation log becomes the documented human-oversight evidence that compliance questionnaires ask for, produced under our ISO 27001:2022-certified process.
Deliverables
- Transaction and KYC workflow logic review
- Encryption and key-handling verification
- Access control and audit logging gap map
- Framework control mapping (SOC 2 / PCI DSS)
- Remediation log as oversight evidence
Quantified Result
The Challenge
Patient data creates liability from the first commit. HIPAA obligations attach to a data-handling module whether or not anyone reviewed it, and AI coding tools do not ask where personal health information flows, who can read it, or how long it is retained. A single unencrypted field or over-permissive cloud role can trigger breach reporting — and in healthtech, a reportable incident costs more than the contract it threatens.
How GrowExx Helps
Deliverables
- PHI data flow map across the application
- HIPAA technical safeguards gap analysis
- Encryption at rest and in transit verification
- Cloud IAM and role permission review
- Audit-ready control evidence pack
Quantified Result
See how a 3-person fintech team closed 14 critical findings in 1 week.
The real audit findings, walked through end to end. 20 minutes. No slides.
How the GrowExx AI Code Audit Process Works
Five phases that move a codebase from unmeasured risk to documented, defensible confidence. Click any step to see what happens inside it and what you receive.
We map the repository: dependencies, services, data flows, cloud configuration, and which components were AI-generated versus hand-written. Coverage includes Lovable, Bolt, Replit, v0, Cursor, Claude Code and Windsurf output running on Supabase, Postgres, Firebase, Node.js, Python or .NET.
Output: Scoped system map and audit plan, agreed with you before work starts.
Static analysis, secrets scanning and dependency checks — including hallucinated-package detection — flag candidates. Senior engineers then verify which are genuinely exploitable, eliminating false positives and surfacing the contextual vulnerabilities automated tooling cannot see.
Output: Findings register with a severity rating and exploitability verdict for every issue.
We test how AI-generated components behave inside your wider system: scaling bottlenecks, data flow integrity, tenant isolation, permission tiers and business-logic accuracy. Load tests run at current and 10x traffic so the first failure point is measured, not estimated.
Output: Load profile and business-logic gap list with reproduction steps.
Every finding receives a severity, a business impact, a fix cost and a plain-language cost of inaction, so a board or founding team can approve the plan in one meeting — ROI defended before code is written. Remediation sprints then close findings in priority order.
Output: Costed fix list and remediation log.
An AI code review gate goes live in your CI/CD pipeline so future AI-assisted commits are checked automatically. Your team receives full documentation, the evidence pack and a walkthrough session.
Output: Live CI/CD quality gate and compliance-ready due diligence package.
Why Choose GrowExx for AI Code Audit & Cleanup?
Vendor-Neutral and Model-Neutral
GrowExx is not paid by OpenAI, Anthropic, Lovable, Cursor, Snyk or any scanning vendor. The recommendation follows the audit evidence: keep the tool, change the tool, or add a gate — never a partnership agreement or referral fee.
ROI Defended Before Code Is Written
Every finding carries a fix cost and a cost of inaction in terms a CFO can approve. You decide what to remediate with numbers in hand, instead of signing an open-ended statement of work and discovering the scope later.
Senior Engineers, Not Scanner Output
Most code audit services resell a scanner report. Scanners flag patterns; our 200+ AI-first engineers diagnose root causes. Every reported finding has been manually verified as exploitable, which is why remediation lists from a GrowExx AI code audit are short enough to finish.
Audit-Grade Governance
ISO 27001:2022, ISO 9001:2015, and ISO 13485:2016-certified processes stand behind every report. Documentation maps to SOC 2, HIPAA, and GDPR control sets, so the paper trail holds up in a diligence room rather than only in a Slack thread.
Findings in 48 Hours
The core security assessment lands while your fundraise or customer security review is still live. For an MVP-scale codebase you see verified findings in two days — not a kickoff deck in two weeks.
Yours to Keep After We Leave
Engagements end with a review gate running on your infrastructure, full documentation and a team walkthrough. No retainer lock-in, no proprietary tooling you cannot run without us.
Convinced? Schedule a 30-min AI Code Audit Call →
Real-World AI Code Audit Case Studies & Success Stories
See exactly what an audit finds, and what it costs to leave it unfound.
Artificial Intelligence
Revolutionizing HR Policy Management: A Generative AI Solution for a Logistics Company
In the modern corporate setting, effective HR policy management is one of the key elements in ensuring organizational governance and contentment among employees while creating an environment that allows business to run smoothly. Client Overview…
Artificial Intelligence
Business Intelligence Solution built on Big Data for Internet Telephony Enterprise
Growexx provided a dedicated team that worked as an extended part for an MNC offering business intelligence solutions for big data analytics.
Artificial Intelligence
Funding Platform To Help Budding Musicians
GrowExx helped in launching a funding platform to help budding musicians with no strings attached.
Artificial Intelligence
Creating a Product Roadmap for AI-powered Career Counselling System
GrowExx team held a product discovery session to chalk out a product roadmap to create an AI-powered career counselling system.
AI
The App Worked Fine. Its Keys Were in the Source Code
CASE STUDY Industry Manufacturing Work Done Executive Summary An AI code audit is an independent review of a codebase that combines automated analysis with senior engineer verification, so a team learns which findings are real and which are noise. GrowExx…
Artificial Intelligence
Digitizing Culinary Heritage: Transforming Handwritten Reviews with NLP
In the heart of Paris, a leading restaurant that has been operating for decades faced a challenge. The reviews by customers were hand-written about their experience at the eatery. Thus, there was a need…
The CFOs and CIOs we've worked with.
GrowExx is the only partner who refused to start coding until we agreed on the ROI math. That discipline is exactly why our reconciliation agent shipped on time, on budget.
We had three AI vendors. Two showed us demos. GrowExx showed us the eval harness, the cost-per-task dashboard, and the rollback plan. That's why they got the contract — and the renewal.
Their team treats every agent like a regulated piece of software, not a science project. That's the difference between a pilot you brag about and an agent your auditor signs off on.
Talk to the team behind these outcomes.
Stay up to date with our recent posts
The 4-Tier AI Code Audit — the packaged framework behind every engagement.
AI Code Security Scan → Production Readiness Audit → Expert AI Code Review → Ongoing AI Code QA. Buy any tier standalone or run them in sequence; each tier feeds the next one's scope, so you never pay to re-discover what a prior phase already mapped. Start free with the same 15-point readiness checklist our engineers work from on day one.
Where to go next.
For teams moving from ad-hoc AI assistance to governed AI-driven development across the full software lifecycle.
Production-grade GenAI for content, code, and customer experience.
For teams that need the remediation capacity in-house — AI-first engineers who work inside your repository and your pipeline.
