Secure Your AI Future: Essential Knowledge for GenAI Systems

In today’s rapidly evolving digital landscape, the integration of Artificial Intelligence (AI) into business processes presents unique security challenges that traditional defenses often fail to address. This course, ‘AI Security Fundamentals: Risks, Frameworks & Tools’, dives deep into the complexities of securing AI systems, specifically focusing on Generative AI (GenAI) applications. You will gain a comprehensive understanding of how modern AI applications, including LLM-based systems and retrieval pipelines, expose new attack paths and vulnerabilities that organizations must be prepared to mitigate.

Over the duration of this course, you will explore the practical aspects of AI security, learning how attackers exploit AI models and how sensitive data can leak through prompts and outputs. We will cover essential topics such as designing secure AI architectures, applying the right controls at various levels, and establishing a repeatable security process for AI-powered systems. Furthermore, the course includes a detailed AI Security Reference Architecture that encompasses models, prompts, data, tools, and monitoring, ensuring you have the frameworks necessary for effective implementation.

Beyond theoretical knowledge, this course emphasizes practical applications, providing you with valuable artifacts like architecture blueprints, threat modeling templates, and security checklists. By the end of the course, you will be equipped with the skills needed to build a robust AI security posture, making you an asset in the field of technology as AI security becomes increasingly crucial. Whether you’re a developer, machine learning engineer, or security analyst, this comprehensive guide will prepare you for one of the most in-demand skill sets in modern tech.

What you will learn:

• Identify attacks on LLM models and common exploitation vectors.
• Detect and prevent data leaks through prompts, responses, and RAG pipelines.
• Design secure architectures for GenAI applications and integrated systems.
• Apply practical controls: AI firewalls, filtering, permissioning, and access policies.
• Implement governance and authorization models for endpoints and connectors.
• Establish AI Security Posture Management workflows to monitor risk and drift.
• Build observability pipelines to log prompts, responses, and quality metrics.
• Use templates and checklists to integrate security into the AI SDLC: datasets, evaluations, and red teaming.

Course Content:

• Sections: 8
• Lectures: 32
• Duration: 8 hours

Requirements:

• Some background in tech, engineering, or system development
• Optional exposure to machine learning concepts or LLM based tools
• Basic understanding of common security practices is a plus
• Ability to interpret high level architecture and process diagrams
• No previous experience with specialized AI security solutions required.

Who is it for?

• Developers integrating AI capabilities into existing or new products
• Machine learning engineers maintaining model workflows and RAG systems
• System and cloud architects designing secure AI infrastructures
• Security analysts and DevSecOps teams responsible for safeguarding AI services
• Team leads and decision makers who oversee AI initiatives and compliance requirements.

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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