AI Security Overview
AI Security is an emerging field focused on protecting artificial intelligence systems from attacks, ensuring data privacy, and building trustworthy AI applications.
Why AI Security Matters
As AI systems become critical infrastructure, they face unique security challenges:
- Data poisoning - Attackers corrupt training data to manipulate model behavior
- Model theft - Stealing proprietary models through extraction attacks
- Adversarial attacks - Crafted inputs that fool AI systems
- Privacy leakage - Models inadvertently revealing training data
AI Security Domains
| Domain | Focus |
|---|---|
| AI Security Fundamentals | Core concepts and attack taxonomy |
| AI Threat Modeling | Identifying risks in AI systems |
| AI Data Security | Protecting training and inference data |
| AI Model Security | Securing ML models and pipelines |
| AI Governance | Policies, ethics, and compliance |
Key Frameworks and Standards
- OWASP ML Top 10 - Common ML security risks
- NIST AI RMF - AI Risk Management Framework
- MITRE ATLAS - Adversarial Threat Landscape for AI Systems
- EU AI Act - Regulatory requirements for AI systems
Getting Started
- Understand AI Security Fundamentals
- Learn to identify risks with AI Threat Modeling
- Implement controls for Data and Model Security
- Establish Governance practices