AI Security Landscape
AI systems face unique security challenges beyond traditional software. This module covers essential security practices for production AI.
Prompt Injection Prevention
Protect against malicious prompt manipulation:
Data Privacy Protection
Protect sensitive information in AI systems:
PII Detection and Redaction
Differential Privacy
Add noise to protect individual privacy:
- Implement epsilon-differential privacy
- Add calibrated noise to outputs
- Aggregate data before processing
- Limit query frequency per user
Access Control
Implement fine-grained access control:
Model Security
Protect AI models from attacks:
Adversarial Input Detection
Security Monitoring
Continuous security monitoring for AI:
- Track unusual prompt patterns
- Monitor for data exfiltration attempts
- Detect model extraction attacks
- Alert on permission violations
- Regular security audits
Best Practices
- Assume all user input is malicious
- Implement defense in depth
- Regular security testing
- Keep models and data encrypted
- Maintain audit logs
- Plan for incident response