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This video aims to inform viewers about the importance of AI security in today's world, provide them with practical knowledge, and empower them to stay secure in an AI-driven environment.
Here's a breakdown of what viewers will learn:
The definition of AI security: Preventing unauthorized access, use, disclosure, disruption, modification, and destruction of AI systems and their data.
The three main attack surfaces are development time, use, and runtime.
The six significant impacts of AI security breaches are disclosure, deception, disruption, denial, degradation, and destruction.
The OWASP Periodic Table of AI Security: A valuable tool for understanding and mitigating AI threats.
Key security risks for generative AI: Data poisoning and prompt injection.
Protecting AI models from theft: Runtime model protection and other measures.
Shared responsibility in cloud-based AI: Understanding the security measures of AI providers.
AI risk management: A four-step roadmap for identifying, evaluating, and handling AI security risks.
The human element in AI security: The importance of security awareness training and building a security culture.
The role of developers and data scientists: Building security into every step of the AI development process.
The average person's takeaway is being informed about AI security and demanding transparency from companies.