AI Code Audit Service | Palm Beach Software Design

Is This You?

Who needs an AI code audit

You don't have to be in crisis to benefit from an audit. These are the most common situations we encounter.

🏗️

Founders with a vibe-coded MVP about to raise funding or launch publicly

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CTOs who inherited an AI-generated codebase and need an honest assessment

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IT Directors with AI tools deployed by a department without IT review

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Organizations in regulated industries concerned about compliance exposure

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Growing companies whose AI-built prototype is now under real production load

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Investors conducting technical due diligence on an AI-built product or company

Common Findings

What we typically find in AI-generated code

These aren't rare edge cases. These are the patterns that appear in the majority of AI-assisted systems we audit.

Hardcoded Credentials

No Logging

Missing Validation

Brittle Integrations

Auth Gaps

Scale Issues

Hardcoded credentials

Critical · Found in ~70% of audits

Database passwords, API keys, and access tokens written directly into source code. When this code is pushed to a repository — even a "private" one — those credentials are exposed and permanently baked into git history.

// FOUND: credential in source code
const db = mysql.connect({ password: "Sup3rSecr3t!" });

// CORRECTED: environment variable
const db = mysql.connect({ password: process.env.DB_PASS });
          

No logging or monitoring

Critical · Found in ~85% of audits

AI-generated code almost universally omits structured logging, error tracking, and performance monitoring. Your first indication of a problem is often a customer complaint — not an internal alert.

// FOUND: silent failure
try { await processPayment(order); }
catch(e) { console.log("error"); }

// CORRECTED: structured logging + ops alert
try { await processPayment(order); }
catch(e) { logger.error({ event: 'payment_failed', orderId: order.id, err: e }); }
          

Missing input validation

Critical · Found in ~75% of audits

AI-generated endpoints often trust all incoming data without verification. This enables SQL injection, XSS attacks, and data corruption from malformed inputs — often exploitable with simple tools.

// FOUND: raw user input to database (SQL injection risk)
db.query(`SELECT * FROM products WHERE name = '${req.body.term}'`);

// CORRECTED: parameterized + schema-validated
app.post('/search', validate(searchSchema), async (req, res) => {
  db.query('SELECT * FROM products WHERE name = ?', [req.body.term]); });
          

Brittle third-party integrations

High Risk · Found in ~90% of audits

AI-generated integration code assumes success on every call. No retry logic, no timeout configuration, no rate limit handling, and no fallback when the external service is unavailable.

// FOUND: optimistic, no error handling
const shipment = await shippingAPI.create(order);

// CORRECTED: resilient with retry + fallback
const shipment = await retry(() => shippingAPI.create(order), {
  attempts: 3, backoff: 'exponential', timeout: 5000 });
if (!shipment.success) { queueForManualReview(order); return; }
          

Authentication gaps

Critical · Found in ~65% of audits

Incomplete authentication: missing token expiry, no refresh logic, predictable password reset tokens, absent brute-force protection, and routes that should require authentication but don't.

// FOUND: no expiry, predictable reset token
const token = jwt.sign({ userId }, SECRET);

// CORRECTED: expiry + cryptographically secure reset
const token = jwt.sign({ userId }, SECRET, { expiresIn: '15m' });
const resetToken = crypto.randomBytes(32).toString('hex');
          

Scale & performance issues

High Risk · Found in ~80% of audits

N+1 query problems, missing indexes, synchronous operations that should be async, no connection pooling. The system works in development. At 10x the data volume, it becomes unusable.

// FOUND: N+1 query (100 orders = 101 DB calls)
for (const order of orders) {
  order.customer = await db.find('users', order.userId); }

// CORRECTED: single JOIN query
const orders = await db.query(
  'SELECT o.*, u.name FROM orders o JOIN users u ON o.user_id = u.id');
          

What you receive

The audit deliverable

You receive a written, human-readable report — not a raw scanner output. Every finding is explained in plain language with a severity rating and clear remediation guidance.

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Executive summary

One-page overview of overall risk posture and critical findings — written for leadership, not developers. Clear, prioritized, and actionable.

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Detailed findings report

Every finding documented with: location in code, severity rating, business impact, and specific remediation guidance. No ambiguity.

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Prioritized remediation roadmap

Findings organized by risk level with a suggested fix sequence. Immediate actions vs. planned improvements clearly separated with effort estimates.

Your AI-Built System Has Problems.
We Can Find Them.