Is My AI-Generated Code Production-Ready? A Pre-Ship Checklist

The Direct Answer: When AI Code Is Safe to Ship

AI-generated code is production-ready when it passes verification across five critical categories: authentication and secrets management, input validation and injection prevention, payment and financial handling, database transaction integrity, and network request safety. The core issue isn't that AI writes bad code—it's that AI writes code optimized for the happy path while missing security-critical edge cases.

From reviewing thousands of AI-generated pull requests, a clear pattern emerges: the code works correctly for the expected flow but fails on adversarial inputs, race conditions, duplicate requests, and security boundaries. This checklist helps you catch those gaps before they reach production.

Why AI Code Needs Different Verification

Traditional code review assumes a human developer who understands security implications and business context. AI assistants like Claude Code, Cursor, and Copilot operate differently—they generate statistically likely code based on training data, which tends to be example code, tutorials, and happy-path implementations.

This creates a specific failure mode: syntactically correct, functionally incomplete code. The authentication flow works, but passwords aren't hashed with a proper KDF. The payment integration processes charges, but doesn't verify webhook signatures. The database query returns results, but isn't wrapped in a transaction.

Understanding this failure mode is the key to effective AI code review. You're not looking for obvious bugs—you're looking for missing defensive code.

The Pre-Ship Checklist

1. Authentication and Secrets Management

Check password storage uses a slow KDF

AI-generated authentication code frequently stores passwords with fast hashes (MD5, SHA1, SHA256) or occasionally plaintext. Production systems require slow key derivation functions.

// AI often generates this (UNSAFE)
const hashedPassword = crypto.createHash('sha256').update(password).digest('hex');

// Production requires this
const hashedPassword = await bcrypt.hash(password, 12);
// Or: argon2.hash(password)
// Or: scrypt with appropriate parameters

Verify timing-safe comparison for password checks:

// AI often generates this (timing attack vulnerable)
if (storedHash === inputHash) { ... }

// Production requires this
if (crypto.timingSafeEqual(Buffer.from(storedHash), Buffer.from(inputHash))) { ... }

Verify no hardcoded secrets

This is one of the most common issues in AI-generated code. Live API keys end up directly in source files, destined to be committed to version control.

// Real finding from review: hardcoded Stripe key
const stripe = new Stripe('sk_live_abc123...');

// Required fix
const stripe = new Stripe(process.env.STRIPE_SECRET_KEY);

Search your codebase for these patterns:

• sk_live_, sk_test_ (Stripe)
• AKIA (AWS access keys)
• ghp_, github_pat_ (GitHub tokens)
• Connection strings with embedded passwords
• Any 32+ character alphanumeric strings in source files

If you find exposed production keys, rotate them immediately—assume they're compromised the moment they touch version control.

Verify session and token handling

Check that:

• JWTs have expiration times and are validated on every request
• Session tokens are regenerated after authentication state changes
• Logout actually invalidates server-side session state (not just client-side)

2. Input Validation and Injection Prevention

Check for SQL injection in dynamic queries

AI tends to generate parameterized queries for simple cases but falls back to string concatenation for dynamic conditions:

// AI handles simple cases correctly
const user = await db.query('SELECT * FROM users WHERE id = $1', [userId]);

// But generates this for dynamic filters (UNSAFE)
const query = `SELECT * FROM products WHERE category = '${category}' 
               ORDER BY ${sortColumn} ${sortDirection}`;

// Required: parameterize values, whitelist column names
const allowedColumns = ['name', 'price', 'created_at'];
const allowedDirections = ['ASC', 'DESC'];
if (!allowedColumns.includes(sortColumn)) throw new Error('Invalid sort column');
if (!allowedDirections.includes(sortDirection)) throw new Error('Invalid direction');
const query = `SELECT * FROM products WHERE category = $1 ORDER BY ${sortColumn} ${sortDirection}`;

Verify Row Level Security (RLS) or equivalent authorization

AI generates queries that return data but rarely adds authorization checks:

// AI generates this (returns any user's data)
app.get('/api/documents/:id', async (req, res) => {
  const doc = await db.query('SELECT * FROM documents WHERE id = $1', [req.params.id]);
  res.json(doc);
});

// Production requires authorization check
app.get('/api/documents/:id', async (req, res) => {
  const doc = await db.query(
    'SELECT * FROM documents WHERE id = $1 AND owner_id = $2', 
    [req.params.id, req.user.id]
  );
  if (!doc) return res.status(404).json({ error: 'Not found' });
  res.json(doc);
});

If using Supabase or Postgres RLS, verify policies are actually enabled and correct—AI often generates the policy syntax without enabling RLS on the table.

Check for SSRF in URL-fetching code

Any endpoint that fetches URLs based on user input is an SSRF vector:

// AI generates this (SSRF vulnerable)
app.post('/api/fetch-preview', async (req, res) => {
  const response = await fetch(req.body.url);
  const html = await response.text();
  res.json({ preview: extractPreview(html) });
});

// Required: validate URL against allowlist, block internal IPs
const isAllowedUrl = (url) => {
  const parsed = new URL(url);
  // Block internal IP ranges
  const ip = await dns.lookup(parsed.hostname);
  if (isPrivateIP(ip)) return false;
  // Allow only expected protocols
  if (!['http:', 'https:'].includes(parsed.protocol)) return false;
  return true;
};

3. Payment and Financial Handling

Verify webhook signature validation

AI generates webhook endpoints that process payloads but skip signature verification:

// AI often generates this (accepts any POST)
app.post('/webhooks/stripe', async (req, res) => {
  const event = req.body;
  if (event.type === 'payment_intent.succeeded') {
    await fulfillOrder(event.data.object);
  }
  res.sendStatus(200);
});

// Production requires signature verification
app.post('/webhooks/stripe', async (req, res) => {
  const sig = req.headers['stripe-signature'];
  let event;
  try {
    event = stripe.webhooks.constructEvent(
      req.rawBody, 
      sig, 
      process.env.STRIPE_WEBHOOK_SECRET
    );
  } catch (err) {
    return res.status(400).send(`Webhook Error: ${err.message}`);
  }
  // Now safe to process event
});

Check for idempotency handling

Webhooks can be delivered multiple times. AI-generated code typically processes each delivery as a new event:

// AI generates this (processes duplicates)
if (event.type === 'payment_intent.succeeded') {
  await db.query('INSERT INTO orders (...) VALUES (...)');
}

// Required: idempotent handling
if (event.type === 'payment_intent.succeeded') {
  const existing = await db.query(
    'SELECT id FROM orders WHERE payment_intent_id = $1', 
    [event.data.object.id]
  );
  if (existing.rows.length > 0) {
    return res.sendStatus(200); // Already processed
  }
  await db.query('INSERT INTO orders (...) VALUES (...)');
}

Verify currency uses integers, not floats

Floating-point arithmetic causes rounding errors with money:

// AI generates this (rounding errors)
const total = price * quantity;
const tax = total * 0.0825;
const finalPrice = total + tax;

// Required: use integer cents
const totalCents = priceCents * quantity;
const taxCents = Math.round(totalCents * 0.0825);
const finalPriceCents = totalCents + taxCents;

4. Database Transaction Integrity

Verify multi-step operations use transactions

AI generates sequential database calls without transactions, creating partial-failure states:

// AI generates this (partial failure possible)
await db.query('UPDATE accounts SET balance = balance - $1 WHERE id = $2', [amount, fromId]);
await db.query('UPDATE accounts SET balance = balance + $1 WHERE id = $2', [amount, toId]);
await db.query('INSERT INTO transfers (...) VALUES (...)');

// Required: wrap in transaction
const client = await db.connect();
try {
  await client.query('BEGIN');
  await client.query('UPDATE accounts SET balance = balance - $1 WHERE id = $2', [amount, fromId]);
  await client.query('UPDATE accounts SET balance = balance + $1 WHERE id = $2', [amount, toId]);
  await client.query('INSERT INTO transfers (...) VALUES (...)');
  await client.query('COMMIT');
} catch (e) {
  await client.query('ROLLBACK');
  throw e;
} finally {
  client.release();
}

Check for race conditions in read-modify-write patterns

// AI generates this (race condition)
const user = await db.query('SELECT credits FROM users WHERE id = $1', [userId]);
if (user.credits >= cost) {
  await db.query('UPDATE users SET credits = credits - $1 WHERE id = $2', [cost, userId]);
}

// Required: atomic operation or SELECT FOR UPDATE
await db.query(
  'UPDATE users SET credits = credits - $1 WHERE id = $2 AND credits >= $1',
  [cost, userId]
);

5. Error Handling and Logging

Verify errors don't leak sensitive information

AI generates helpful error messages that expose internals:

// AI generates this (leaks database schema)
catch (err) {
  res.status(500).json({ error: err.message, stack: err.stack });
}

// Required: generic client errors, detailed server logs
catch (err) {
  console.error('Database error:', err);
  res.status(500).json({ error: 'An internal error occurred' });
}

Check that sensitive data isn't logged

// AI generates this (logs passwords/tokens)
console.log('Login attempt:', { email, password });
console.log('API request:', req.headers);

// Required: redact sensitive fields
console.log('Login attempt:', { email, password: '[REDACTED]' });

Automating Parts of This Checklist

Some checks can be automated:

• Secret scanning: GitHub's built-in secret scanning, or tools like gitleaks
• SQL injection patterns: SAST tools catch obvious cases
• Dependency vulnerabilities: npm audit, Dependabot

However, context-dependent issues—missing idempotency, incorrect authorization logic, transaction boundaries—require understanding what the code is supposed to do. This is where Fairy Scout helps: it's a free AI review tool that knows these patterns and flags them automatically in your PRs.

For code that's particularly sensitive—payment flows, authentication, data access—connecting with a human Fairy provides expert sign-off with a 24-hour SLA. Staff engineers review the actual implementation against these security patterns and your specific business logic.

A Practical Review Workflow

For each AI-generated PR:

1. Run automated checks first: secret scanning, linting, type checking
2. Use the checklist above for manual review, focusing on the categories relevant to the change
3. Question the happy path: Ask "what happens if this is called twice? With malformed input? By an unauthorized user?"
4. Verify edge cases have tests: AI-generated tests often only cover the happy path too

If you're shipping AI-generated code frequently, Fairy Intelligence can answer specific questions about your codebase's patterns and help you build institutional knowledge about what to check.

The Bottom Line

AI-generated code is safe to ship when you've verified it handles the cases AI tends to miss: authentication edge cases, payment idempotency, injection in dynamic queries, transaction boundaries, and secret management. The happy path is usually fine—it's the error cases and security boundaries that need human attention.

Use this checklist systematically, automate what you can, and get expert review for high-stakes code. The goal isn't to distrust AI tools—they're genuinely useful—but to verify that generated code meets production standards before it affects real users.