Use Cases → Compliance

#5 Regulated Industries

HIPAA PCI-DSS SOC 2

The "Liability Risk" Objection
"I Can't Approve AI in a Regulated Environment"

The Promise: Deterministic Safety for Probabilistic Models

Inject SOC2, HIPAA, and PCI-DSS guardrails directly into the Model Context Protocol (MCP). Prevent PII leakage and enforce audit trails before the code is even written.

The CISO's Nightmare

What keeps you saying "no" to AI

🛡️

What you're thinking in the security review:

"One junior dev pastes a ChatGPT snippet that logs patient SSNs. One AI-generated function skips the auth check. That's not a bug. That's a lawsuit. That's a breach notification. That's my job."

⚠️

HIPAA Risk

AI logs PHI (Protected Health Information) in plaintext. $50,000 per violation. Criminal charges for willful neglect.

💳

PCI-DSS Risk

AI generates code that stores card numbers in logs. Lose your payment processing. $100K+ fines.

🔓

Auth Bypass Risk

AI skips authorization checks because "it works in tests." Unauthorized data access. Breach notification required.

🕵️

Audit Risk

"Who reviewed this code?" "The AI." That doesn't fly with auditors. You need proof of controls.

Deterministic Safety for Probabilistic Models

Not "AI promises to be careful." Actual enforcement via MCP.

🚫 Negative Constraints

We inject "What NOT to do" rules directly into the Model Context Protocol (MCP). Before the model generates a single line, your compliance rules are already in context. The AI literally cannot violate what it's been told to never do.

// Injected into EVERY prompt:

NEVER log request.body
NEVER log user.ssn, user.dob, user.email
NEVER store card numbers outside vault
ALWAYS use parameterized queries
ALWAYS check authorization before data access

Translation: The rules are in the prompt. The AI literally cannot generate the bad code.

🧪 Compliance Unit Tests

We generate compliance-specific tests alongside the feature code. If the test fails, the code can't merge. Proof of safety before deployment.

// Auto-generated compliance test:

test('should not log PII fields', () => {
  const logs = captureLogOutput(() => {
    createUser({ ssn: '123-45-6789' });
  });
  
  expect(logs).not.toContain('123-45-6789');
});

Translation: You can show auditors: "Here's the test that proves we don't log PII."

The Compliance-Safe Workflow

AI Code That Auditors Love

📋

Spec + Rules

→

🚫

Constraints Injected

→

💻

Code Generated

→

🧪

Tests Generated

→

✅

Audit-Ready

Industry-Specific Guardrails

Pre-built rule sets for regulated industries

🏥 HIPAA

HealthTech

→ Never log PHI
→ Encrypt at rest + transit
→ Audit logging required
→ Minimum necessary access

🏦 PCI-DSS

Fintech

→ No card data in logs
→ Tokenization only
→ Strong auth on all endpoints
→ Parameterized queries only

🏢 SOC 2

Enterprise SaaS

→ Access controls on all data
→ Comprehensive audit trails
→ Encryption everywhere
→ Secure defaults

✅

Finally Say "Yes" to AI

You've been saying no because you couldn't guarantee safety. With ProdMoh, you have deterministic guardrails and provable compliance. The AI can't break your rules because the rules are enforced before generation.

✓ Auditor-friendly evidence ✓ Deterministic constraints ✓ Automated compliance tests ✓ No PII exposure risk

AI That Passes the Security Review

Approve AI coding tools for your regulated environment. Compliance guardrails built into every prompt.

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The "Liability Risk" Objection "I Can't Approve AI in a Regulated Environment"