Overview

Google, IBM & Meta Certificates – 40% Off

One plan covers every Professional Certificate on Coursera.

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive deep into the security frameworks necessary for GenAI and agentic systems. This course covers the critical aspects of securing AI systems from development to deployment, using industry-standard frameworks such as OWASP LLMSecOps, NIST, and MITRE ATLAS. Learn how to establish security governance, manage risks, and integrate regulatory compliance into your AI systems. The course begins by introducing foundational concepts, the AI risk landscape, and key guidelines from industry leaders. You will then explore the core frameworks used for managing and securing AI systems, followed by practical governance strategies, including inventory management, documentation, and transparency. The course also emphasizes securing AI development and operations, including threat intelligence, red teaming, and AI vulnerability scoring. This course is ideal for professionals seeking to strengthen their understanding of AI security. It provides the tools to safeguard Generative AI and agentic systems from emerging risks while ensuring compliance with global regulations. By the end of the course, you will be able to design secure AI systems, implement risk management practices, and apply best security practices to GenAI and agentic systems.

Syllabus

Introduction to AI Security

In this module, we will introduce you to the fundamental principles of AI security, highlighting the importance of a structured approach to secure GenAI and agentic systems. We’ll explore the current state of AI adoption and the resulting security challenges. Additionally, we will dive into various frameworks, such as OWASP LLMSecOps and NIST, that offer guidance for securing AI throughout its lifecycle and aligning with global regulations.

The AI Risk Landscape

In this module, we will explore the landscape of AI risks, providing a detailed taxonomy of the risks AI systems face across multiple dimensions. We will examine the OWASP Top 10 risks for LLM and GenAI, as well as the unique security challenges posed by agentic systems. You’ll learn how to mitigate these risks using structured security practices and best practices for agentic application security.

Governance and Compliance

This module focuses on the importance of governance and compliance in AI security. We’ll discuss how to build an AI security governance framework that aligns risk management with strategy, ensuring compliance with evolving AI regulations. You’ll learn how to establish an AI asset inventory, prevent shadow AI, and map security controls to specific regulatory requirements to streamline your compliance process.

Secure Development and Operations

In this module, we will explore how security is integrated into every phase of the AI lifecycle through the LLMSecOps framework. You’ll gain insights into securing AI data, training models, and AI-specific threat intelligence to strengthen your system’s defenses. Additionally, we will cover AI red teaming, vulnerability scoring, and setting AI security KPIs to monitor and improve the security posture of your AI systems.