Artificial Intelligence Risks in Cybersecurity

Published 12/2025
Duration: 4h 1m | .MP4 1920×1080 30fps(r) | AAC, 44100Hz, 2ch | 1.58 GB

Master AI Governance, Secure LLMs, and Mitigate Generative AI Risks using NIST Frameworks. (Focuses on frameworks and LL

What you’ll learn
– Students will learn the core fundamentals of AI governance and risk management through industry-standard frameworks like NIST.
– The course provides deep insights into securing Large Language Models (LLMs) against specific threats like prompt injections, hallucinations, and SSRF.
– Learners will discover how to identify and mitigate ethical risks, including model bias and data privacy concerns in generative systems.
– Students will master defensive strategies to protect against data leakage, data poisoning, and evolving cyber-attacks from AI-powered threat actors.
– By the end, students will be equipped with practical tools and checklists to implement robust AI security and compliance within any organization.

Requirements
– A basic understanding of how computer systems and the internet function is required to navigate the course material.
– A foundational interest in cybersecurity and emerging technologies will help you better understand the threat mitigation strategies.
– Familiarity with general IT or business terminology is helpful for the sections on governance and risk management frameworks.
– An eagerness to explore the ethical, legal, and security challenges posed by modern AI systems is the most important prerequisite.

Description
Master the Strategy Behind AI Defense
In this course, we move beyond the hype to explore the actual mechanics of AI vulnerabilities. From the foundational principles ofAI Governanceto the intricate security challenges ofLarge Language Models (LLMs), you will learn how to identify, assess, and mitigate risks using industry-standard frameworks likeNIST.

What You Will Explore

The Foundation of AI Governance:Learn how to build and implement a robust governance framework for any organization.

Securing Generative AI:Understand the specific risks of LLMs, includingPrompt Injections,Hallucinations, andData Leakage.

Advanced Cyber Threats:Explore how modern threat actors use AI forData Poisoning,SSRF, and sophisticatedDDoS attacks.

Ethics & Legality:Navigate the complex world ofModel Bias, AI copyright issues, and the legal implications of automated decision-making.

Practical Implementation:Gain access to an exclusiveAI Governance Checklistto audit and secure AI systems effectively.

Why Choose This Course?

This is astrictly theory-based course, making it perfect for professionals who need to understand the "Why" and "How" of AI security without getting bogged down in programming. It is ideal for decision-makers, auditors, and security enthusiasts who want a high-level, strategic understanding of the AI threat landscape.

Who Is This For?

Cybersecurity professionals looking to pivot into AI security.

IT Managers and Compliance Officers overseeing AI integration.

Legal and Ethical consultants focused on emerging tech.

Students wanting to master the theory of AI risk management.

Who this course is for:
– Cybersecurity professionals and ethical hackers who want to specialize in securing AI infrastructure and Large Language Models.
– IT managers and organizational leaders responsible for implementing AI governance and risk management frameworks.
– Compliance officers and legal professionals interested in the ethical, legal, and copyright challenges of generative AI.
– Security auditors who need practical methodologies and checklists for evaluating the vulnerabilities of AI systems.
– Students and tech enthusiasts looking to stay ahead of the curve by understanding defense strategies against modern AI-driven cyber threats.