Generative AI Security and Privacy Fundamentals

As generative AI transforms the modern workplace, understanding the balance between innovation and protection is essential for any professional. Navigating this new landscape requires a firm grasp of how these technologies handle data and where they are most vulnerable to exploitation. You will gain a clear path to identifying AI-specific vulnerabilities and building a framework for secure, ethical AI adoption. This course prepares you to lead AI initiatives with a focus on risk mitigation and data integrity. What you'll learn: - Understand the fundamental mechanics of generative AI and its core security implications - Identify modern AI threats such as prompt injection, model inversion, and data poisoning - Apply privacy principles to protect sensitive organizational data within AI workflows - Analyze the security impacts of retrieval-augmented generation (RAG) and vector databases - Develop governance strategies to ensure ethical and compliant AI usage across teams - Evaluate the security posture of third-party AI tools and integrated platforms Starting with essential definitions and foundational concepts, the course moves through technical risks and organizational privacy before concluding with strategic governance frameworks. You will read through detailed explanations and practical scenarios designed to build your expertise. This course is designed for beginners, managers, and decision-makers who need a foundational understanding of AI security without requiring a deep technical background or prior experience in cybersecurity. Prepare your organization for a secure AI-driven future through these comprehensive written lessons.