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Security & Compliance in AI Platforms

AI systems handling sensitive data face unique security challenges. Beyond traditional application security, they must protect model inputs/outputs, ensure data privacy, meet regulatory requirements, and prevent adversarial attacks. This guide covers practical security and compliance measures for production AI systems.

Data Protection

Encryption

At Rest: All stored data should be encrypted:

  • Use database encryption (most modern databases support this)
  • Encrypt files in object storage (S3 server-side encryption)
  • Store encryption keys separately (AWS KMS, HashiCorp Vault)

In Transit: All network communication must use TLS 1.2+:

  • HTTPS for API endpoints
  • TLS for database connections
  • Encrypted connections to LLM APIs

PII Handling

Personal Identifiable Information requires special handling:

  • Detection: Use NER models to identify PII in inputs
  • Redaction: Remove or mask PII before sending to external APIs (if required)
  • Access Control: Limit who can access PII-containing data
  • Logging: Don't log PII in plain text
  • Deletion: Implement right-to-deletion workflows (GDPR, CCPA)

API Security

Authentication & Authorization

  • API Keys: Use secure key management, rotate regularly
  • OAuth 2.0 / JWT: For user-facing applications
  • Role-Based Access Control (RBAC): Different permissions for different users

Prompt Injection & Adversarial Attacks

Prompt Injection

Attackers can manipulate LLM behavior through crafted inputs:

  • Direct injection: User input contains instructions for LLM ("Ignore previous instructions...")
  • Indirect injection: Malicious content in retrieved documents

Mitigations:

  • Separate system prompts from user inputs clearly
  • Validate and sanitize user inputs
  • Use structured outputs to constrain model responses
  • Implement output validation
  • Human review for high-risk operations

Compliance Frameworks

GDPR (EU)

Requirements for EU users:

  • Right to access: Users can request their data
  • Right to deletion: Users can request data deletion
  • Data portability: Export user data in machine-readable format
  • Privacy by design: Build privacy into system architecture

CCPA/CPRA (California)

Similar to GDPR with some differences:

  • Right to know what data is collected
  • Right to delete personal information
  • Right to opt-out of data sale (if applicable)

Audit & Logging

Comprehensive audit trails are essential:

  • Access logs: Who accessed what data, when
  • Operation logs: All data modifications
  • Model logs: Inputs/outputs (with PII redaction)
  • Security events: Failed auth attempts, suspicious patterns

Conclusion

Security and compliance in AI systems is not an afterthought—it must be designed in from the beginning. Key principles: encrypt everything, minimize data, implement access controls, maintain audit trails, and understand regulatory requirements. For highly sensitive use cases, consider on-premises or self-hosted models to avoid sending data to external APIs.