Learn to defend AI systems, including LLMs and GenAI applications, against cyber threats. This course equips you with strategies to assess vulnerabilities, mitigate risks, and secure AI infrastructures.Key FeaturesDetailed exploration of AI-related cyber threatsStep-by-step security practices for AI systemsReal-world case studies for practical insightsBook DescriptionArtificial intelligence is transforming industries, but it also exposes organizations to new cyber threats. This course begins by introducing the foundational concepts of securing large language models (LLMs), generative AI applications, and the broader AI infrastructure. Participants will explore the evolving threat landscape, gaining insights into how attackers exploit vulnerabilities in AI systems and the risks posed by trust and compliance failures. The course provides real-world case studies to highlight attack vectors like adversarial inputs, data poisoning, and model theft. Participants will learn practical methods for identifying and mitigating vulnerabilities in AI systems. These insights prepare learners to proactively safeguard their AI infrastructures using advanced security assessment techniques. Finally, the course equips participants with actionable strategies to defend AI systems. You ll learn to protect sensitive data, implement robust security measures, and address ethical challenges in AI. By the end, you ll be ready to secure AI ecosystems and adapt to the fast-evolving AI security landscape.What you will learnIdentify key threats to AI ecosystemsUnderstand AI-specific attack vectorsApply security frameworks to AI modelsIntegrate cybersecurity into AI systemsNavigate legal and ethical AI security issuesImplement secure AI deployment practicesWho this book is forThis book is designed for AI developers, cybersecurity professionals, and technology leaders who want to understand the vulnerabilities of AI systems. Basic knowledge of AI principles and cybersecurity frameworks is recommended.]]>
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