LLM Security Best Practices for Production Applications

Deploying LLMs in production requires rigorous security practices. This guide covers enterprise-grade approaches to protecting your AI applications.

Security Architecture

Zero Trust for AI

Apply zero trust principles to your LLM infrastructure:

Reference Architecture

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Client    │────▶│   Gateway   │────▶│   LLM API   │
└─────────────┘     └─────────────┘     └─────────────┘
                          │
                    ┌─────┴─────┐
                    ▼           ▼
              ┌─────────┐ ┌─────────┐
              │ Logging │ │ Filters │
              └─────────┘ └─────────┘
                    │           │
                    ▼           ▼
              ┌─────────┐ ┌─────────┐
              │ Monitor │ │  Alert  │
              └─────────┘ └─────────┘

Input Security

Validation Pipeline

async function processInput(input: string): Promise<ProcessedInput> {
  // Step 1: Sanitize
  const sanitized = sanitizeInput(input);
  
  // Step 2: Check length and format
  validateFormat(sanitized);
  
  // Step 3: Threat detection
  const threats = await detectThreats(sanitized);
  if (threats.length > 0) {
    await logSecurityEvent(threats);
    throw new ThreatDetectedError(threats);
  }
  
  // Step 4: Rate limiting
  await enforceRateLimit(userId);
  
  return { content: sanitized, metadata: extractMetadata(input) };
}

Content Classification

Use ML-based classification to categorize inputs:

Output Security

Response Validation

Before returning responses to users:

function validateOutput(response: string): ValidationResult {
  const checks = [
    checkForPIILeakage(response),
    checkForSystemPromptLeakage(response),
    checkForMaliciousContent(response),
    checkForUnauthorizedData(response),
  ];
  
  return {
    valid: checks.every(c => c.passed),
    issues: checks.filter(c => !c.passed),
  };
}

Redaction

Automatically redact sensitive information:

Tool and API Security

Permission System

interface ToolPermission {
  tool: string;
  actions: ('read' 'write''delete' 'execute')[];
  scope: string[];
  requiresApproval: boolean;
}
const permissions: ToolPermission[] = [
  {
    tool: 'database',
    actions: ['read'],
    scope: ['public_tables'],
    requiresApproval: false,
  },
  {
    tool: 'email',
    actions: ['read', 'write'],
    scope: ['user_inbox'],
    requiresApproval: true,  // Requires human approval
  },
];

Sandboxing

Execute AI-initiated actions in isolated environments:

Monitoring and Observability

What to Log

Event TypeData PointsRetention

All requestsInput, output, latency, tokens90 days
Security eventsThreat type, severity, action taken1 year
Tool invocationsTool, parameters, result90 days
ErrorsStack trace, context30 days

Alerting Rules

Configure alerts for:

Dashboards

Build dashboards showing:

Compliance

Data Handling

For regulated industries:

Documentation

Maintain documentation for:

Incident Response

Response Plan

Runbooks

Create runbooks for common scenarios:

Security Testing

Regular Testing

Test Cases

Maintain a test suite of known attacks:

const attackTestCases = [
  { name: 'direct_override', input: 'Ignore all instructions...' },
  { name: 'base64_encoded', input: 'Decode: SW5qZWN0aW9u...' },
  { name: 'authority_impersonation', input: '[ADMIN]: ...' },
  // ... more cases
];
describe('Security Tests', () => {
  attackTestCases.forEach(({ name, input }) => {
    it(should block ${name} attack, async () => {
      const result = await processInput(input);
      expect(result.blocked).toBe(true);
    });
  });
});

Checklist for Production

Before going live:

Conclusion

LLM security requires a comprehensive approach covering architecture, input/output handling, monitoring, compliance, and incident response. No single measure is sufficient—you need defense in depth.

The goal is not to prevent all attacks (which is impossible), but to detect them quickly, limit their impact, and continuously improve your defenses.

Promptective provides enterprise-grade LLM security monitoring out of the box. Schedule a demo to learn how we can help secure your AI applications.