Securing AI: Navigate Risks & Safeguard Your Systems

In the age of AI, traditional security measures fall short against the complex challenges posed by modern AI applications. “AI Security Fundamentals: Risks, Frameworks & Tools” is designed to address these unique challenges with a complete, practical approach. This course delves deep into how large language models (LLMs), retrieval-augmented generation (RAG) systems, and data connectors can be exploited and highlights vital strategies for mitigating risks. You’ll gain insights into how attackers leverage vulnerabilities in AI models, leading to sensitive data leaks and compromised systems.

Explore a structured lifecycle approach to securing GenAI systems. The curriculum is rich with real-world applications, teaching you how to design secure AI architectures and implement essential security controls. By covering crucial topics such as injection attacks, data governance for RAG systems, and AI Security Posture Management, this course equips you with the skills needed to build robust, secure environments for AI applications.

Not only does this course provide theoretical knowledge, but it also emphasizes tangible, actionable strategies through detailed architecture blueprints, governance frameworks, and threat modeling templates. With a thorough understanding of AI security—from development workflows to monitoring risks—you’ll be ready to tackle the growing demand for AI security expertise in the tech industry. Whether you’re enhancing existing AI systems or developing new ones, this course offers the foundational tools and knowledge you need to succeed.

What you will learn:

• Identify modern GenAI risks and understand how attackers target LLM and RAG pipelines
• Apply a layered AI security design to strengthen every component of an AI application
• Create detailed AI threat models and link each threat to concrete control measures
• Configure AI firewalls and runtime guardrails to manage prompts, responses, and tool actions
• Embed security practices into AI development workflows, including dataset checks and eval automation
• Implement robust identity, authorization, and scoped access for AI endpoints and integrations
• Enforce data governance for RAG systems through access rules, tagging, and secure retrieval patterns
• Use SPM platforms to maintain visibility over models, datasets, connectors, and policy violations
• Build observability pipelines to track prompts, responses, decisions, and model quality metrics
• Assemble a unified AI security strategy and translate it into clear 30, 60, and 90 day actions

Course Content:

• Sections: 3
• Lectures: 20
• Duration: 6h 7m

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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