Secure Your AI Future: Master the Fundamentals of AI Security

As artificial intelligence continues to reshape the technological landscape, it brings with it a host of security challenges that traditional defenses simply cannot tackle. This course, “AI Security Fundamentals: Risks, Frameworks & Tools,” is designed to tackle these emerging threats head-on. You will navigate the intricacies of modern AI applications and understand how systems like LLM based technologies, retrieval pipelines, agents, data connectors, and vector databases introduce new vulnerabilities.

Throughout this course, you will gain a comprehensive, engineering-focused perspective on securing GenAI systems across their entire lifecycle. You’ll delve into how attackers exploit AI models, how sensitive data can inadvertently leak through prompts and outputs, and the various ways retrieval-augmented generation (RAG) pipelines can be manipulated. You will also learn to identify misconfigurations that expose your environment to potential breaches, and develop the skills necessary to design secure AI architectures that apply appropriate controls at multiple layers.

What sets this course apart is its practical approach. With detailed blueprints, templates for threat modeling, and frameworks for governance and policy, you will receive clear, actionable guidance. Not only does this course prepare you to safeguard AI systems against misuse and breaches, but it covers the entire AI ecosystem rather than focusing on isolated controls. By the end, you will be equipped with a structured, comprehensive set of tools and processes to protect AI systems confidently at scale and prepare for the demand for these critical skills in the tech industry.

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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