AI Security & Safety
Understanding the security implications of AI systems and building safer AI technologies
As artificial intelligence systems become more powerful and widespread, understanding their security implications and ensuring their safe deployment is increasingly critical. AI security and safety encompasses a range of disciplines focused on building robust, secure, and aligned AI systems.
AI Security
Protecting AI systems from attacks and ensuring they don't introduce new vulnerabilities
AI Alignment
Ensuring AI systems act in accordance with human values and intentions
AI Risk Assessment
Identifying and mitigating potential harms from AI systems
AI Governance
Developing frameworks, policies, and standards for responsible AI
AI Security & Safety Learning Path
- 1
Introduction to AI Security
Understanding the unique security challenges of AI systems
- 2
AI Safety Concerns
Overview of potential risks from AI systems
- 3
AI Ethics Fundamentals
Ethical considerations in AI development and deployment
- 4
AI Governance Basics
Introduction to AI governance frameworks
- 1
Adversarial Machine Learning
Understanding and defending against attacks on ML models
- 2
AI Red Teaming
Techniques for testing AI system security and safety
- 3
Secure AI Development
Best practices for building secure AI systems
- 4
AI Risk Assessment
Methodologies for evaluating AI system risks
- 1
AI Alignment Research
Current approaches to aligning AI with human values
- 2
Interpretability Techniques
Methods for understanding AI decision-making
- 3
AI Safety Engineering
Technical approaches to building safer AI systems
- 4
Long-term AI Safety
Considerations for advanced AI capabilities
AI Security & Safety Essentials
Fundamental Principles
- AI systems should be secure against adversarial manipulation
- AI development should prioritize safety throughout the lifecycle
- AI systems should be transparent and interpretable where possible
- AI risks should be systematically assessed and mitigated
- AI alignment with human values should be a core design goal
Key Practices
- Implement robust testing for security vulnerabilities
- Establish clear governance processes for AI development
- Maintain human oversight of critical AI systems
- Document design decisions and risk assessments
- Continuously monitor deployed AI systems for unexpected behavior