Safeguarding Generative AI: Mastering Security Controls

In an age where AI systems present unique security challenges, understanding how to protect them is more crucial than ever. This course, “Securing AI Applications: From Threats to Controls,” delves deep into the intricacies of securing generative AI systems. It covers the security issues that traditional cybersecurity measures often overlook, equipping you with the knowledge and tools necessary to safeguard sensitive data and maintain robust workflows in AI environments.

Throughout this course, you’ll uncover how modern AI threats operate and how attackers exploit various vulnerabilities. With practical examples and a structured framework, you’ll learn to address risks associated with LLM applications, retrieval pipelines, and vector databases. This comprehensive course provides a complete AI Security Reference Architecture, allowing you to implement effective defenses tailored to each layer of your AI stack, ensuring that you can navigate the complexities of AI security with confidence.

Additionally, this course is filled with valuable resources, including architecture diagrams, control maps, and governance templates, to streamline your security efforts. Whether you are involved in AI deployments or overseeing AI governance, this course arms you with actionable strategies to protect your systems, making it an essential resource in today’s fast-evolving tech landscape.

What you will learn:

• AI Security Reference Architecture across model, prompt, data, tools, and monitoring layers
• How GenAI attacks work, including injection, leakage, misuse, and unsafe tool execution
• Using AI firewalls, filtering engines, and policy controls for runtime protection
• AI SDLC best practices for dataset security, evaluations, red teaming, and version management
• Data governance strategies for RAG pipelines, ACLs, encryption, filtering, and secure embeddings
• Identity and access patterns that protect AI endpoints and tool integrations
• AI Security Posture Management for risk scoring, drift detection, and policy enforcement
• Observability and evaluation workflows that track model behavior and reliability
• Step-by-step 30/60/90 day rollout plan for secure implementation in teams

Course Content:

• Sections: 10
• Lectures: 40
• Duration: 6 hours

Requirements:

• Basic understanding of software development or IT systems
• Familiarity with AI concepts such as LLMs or RAG is helpful but not required
• General knowledge of cybersecurity principles is beneficial
• Ability to read technical diagrams and system architectures
• No prior experience with AI security tools or frameworks needed.

Who is it for?

• Professionals building or maintaining applications enhanced with generative AI
• ML specialists working with embeddings, retrievers, and model endpoints
• Architects responsible for structuring secure AI and data pipelines
• Security teams evaluating risks in AI powered systems
• Leaders and practitioners managing AI adoption, governance, and operational safety.

What are you waiting for to get started?

Enroll today and take your skills to the next level. Coupons are limited and may expire at any time!

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