Security Best Practices for AI
A practical guide to securing your AI implementations. Learn how to protect your data, ensure compliance, and manage risk.
Data Encryption
All data should be encrypted in transit and at rest. Use industry-standard encryption protocols.
Access Control
Implement principle of least privilege. Only give AI systems access to data they absolutely need.
Data Minimization
Collect and process only the data necessary for the specific task. Delete when no longer needed.
Monitoring & Auditing
Log all AI system activities. Regularly audit outputs for bias, errors, and security issues.
Executive Summary
AI security is not fundamentally different from traditional software security, but it does introduce unique challenges. AI systems often handle sensitive data, make autonomous decisions, and can be vulnerable to new types of attacks like prompt injection and model inversion.
This whitepaper provides actionable guidance for securing AI implementations at enterprise scale. It covers data protection, access control, compliance, and operational security.
1. Data Protection
Understanding Your Data
Before implementing any AI solution, classify your data:
- Public: Information that can be freely shared
- Internal: Business information not meant for public disclosure
- Confidential: Sensitive business data, customer information
- Restricted: Highly sensitive data (PII, PHI, financial records)
Data Handling Requirements
| Data Type | Encryption | Access Control | Logging |
|---|---|---|---|
| Public | In transit | Basic authentication | Standard |
| Internal | In transit + at rest | Role-based | Standard |
| Confidential | AES-256 | Role-based + MFA | Enhanced |
| Restricted | AES-256 + tokenization | Zero-trust | Comprehensive |
2. Access Control
Principle of Least Privilege
AI systems should only have access to the minimum data and capabilities required to perform their specific function. This limits the blast radius if the system is compromised.
Authentication & Authorization
- Use multi-factor authentication for all administrative access
- Implement API key rotation every 90 days
- Use short-lived tokens for service-to-service communication
- Regularly audit access permissions
3. Compliance Considerations
GDPR Compliance
If you process EU citizen data:
- • Obtain explicit consent for AI processing
- • Implement right to explanation for automated decisions
- • Maintain data processing records
- • Enable data portability and deletion
HIPAA Compliance
For healthcare data:
- • Execute Business Associate Agreements with all vendors
- • Implement audit controls and access logs
- • Ensure data encryption at rest and in transit
- • Regular risk assessments
4. Operational Security
Prompt Injection Prevention
Prompt injection attacks attempt to manipulate AI systems by crafting malicious inputs. Defend against them by:
- • Validating and sanitizing all user inputs
- • Using prompt boundaries and delimiters
- • Implementing output filtering
- • Never executing AI-generated code without review
Model Security
- • Keep model versions and dependencies updated
- • Monitor for model drift and unexpected behavior
- • Implement circuit breakers for anomalous outputs
- • Use private endpoints for sensitive applications
5. Incident Response
Prepare for security incidents before they happen:
- 1. Detection: Monitor logs for unusual patterns and set up alerts
- 2. Containment: Have procedures to quickly disable AI systems if compromised
- 3. Investigation: Preserve logs and evidence for forensic analysis
- 4. Recovery: Document rollback procedures and backup restoration
- 5. Post-Incident: Conduct blameless postmortems and update security measures
Security Checklist
Conclusion
AI security is an ongoing process, not a one-time setup. Regular reviews, testing, and updates are essential as both threats and capabilities evolve.
Need help securing your AI implementation? Contact us for a security assessment.