Securing AI Systems Protecting Data, Models, & Usage
AI Summary
This IBM Technology video by Jeff Crume presents a comprehensive framework for AI security using a “donut of defense” approach. The video outlines how to protect AI systems by wrapping them with four essential security capabilities:
Key Security Framework: The Four Pillars
1. Discover
- Shadow AI Detection: Find unauthorized AI implementations across cloud and on-premises platforms
- Comprehensive Inventory: Catalog all AI systems including machine learning models and large language models
- Agentless Discovery: Use approaches that don’t require deploying agents everywhere
- Log Collection: Gather AI system logs into a centralized data lake for threat analysis
2. Assess
- AI Security Posture Management: Scan for vulnerabilities and misconfigurations in AI environments
- Model Security Scanning: Inspect imported third-party models (like those from Hugging Face) for malware
- Penetration Testing: Test AI systems against potential attacks before bad actors do
- Policy Compliance: Ensure systems stay aligned with security policies and don’t drift
3. Control
- AI Gateway Implementation: Filter incoming prompts to detect and block prompt injection attacks (identified by OWASP as the #1 threat to generative AI)
- Jailbreak Prevention: Block attempts to make AI violate safety rules or guardrails
- Privacy Protection: Prevent sensitive data (PII, PHI, confidential information) from leaving the environment
- Flexible Response: Option to monitor vs. block based on confidence in controls
4. Report
- Risk Visualization: Dashboard showing prioritized risks and vulnerabilities
- Compliance Reporting: Audit reports against frameworks like MITRE AI Risk Management Framework and OWASP Top 10
- Centralized Management: Single pane of glass for all AI security monitoring
- Informed Decision Making: Data-driven approach to risk tolerance and response
Core Security Areas
The framework emphasizes securing three critical components:
- Data Security: Protecting information going into and coming out of AI systems
- Model Security: Ensuring AI models themselves are not compromised or malicious
- Usage Security: Controlling how AI systems are accessed and used
Key Takeaways
- You cannot secure what you cannot see - discovery is fundamental
- Most organizations will use third-party models, introducing supply chain risks
- Prompt injection is the primary threat vector for generative AI systems
- A layered defense approach is essential for comprehensive AI security
- Balance between security controls and business continuity is crucial
The video emphasizes that with proper implementation of these four capabilities (discover, assess, control, report), organizations can create a robust “defensive donut” that makes their AI systems both secure and effective.