AI Cybersecurity Risks: Master Threats & Governance Now

Name: AI Cybersecurity Risks: Master Threats & Governance Now
Rating: 4.4 (32 reviews)

Understand LLM vulnerabilities, data poisoning, and AI governance frameworks—no coding or technical labs required.

Created bySyed Muhammad Hatim Javaid

Last updated 4/2026

English

What you'll learn

Build and implement AI governance frameworks to manage organizational AI risk responsibly
Identify and analyze security threats specific to Generative AI and Large Language Models
Recognize prompt injection, hallucinations, and LLM data leakage vulnerabilities in real systems
Understand how attackers use AI for data poisoning, SSRF exploits, and advanced DDoS campaigns
Navigate AI ethics and legal implications—model bias, copyright, and automated decision liability
Apply NIST framework principles directly to AI risk assessment and organizational security strategy
Use the included AI Governance Checklist to immediately audit and improve your AI security posture
Communicate AI cybersecurity risks clearly to both technical teams and executive stakeholders

Course content

8 sections • 33 lectures • 4h 1m total length

Introduction5:14
A brief overview of the course structure and objectives.
What is AI7:18
Explains the fundamental definition, history, and scope of Artificial Intelligence.
What is Machine Learning3:36
Differentiates Machine Learning from general AI and explains how algorithms learn from data.
Governance and Risk Management in AI3:49
Introduces the need for oversight, policies, and risk management strategies in AI deployment.
Can AI Be Biased5:59
how AI models inherit prejudices from training data.
Negative Impacts of AI Biases3:36
Real-world examples of how biased AI can harm individuals and society.

AI Governance Framework7:54
Explains the components of a robust governance structure.
How to Create an AI Governance Framework7:24
A step-by-step guide to building a governance policy for an organization.
NIST AI Risk Management Framework10:01
A deep dive into the industry-standard guidelines provided by NIST.
Cyber Security Risks in AI Systems14:40
An overview of security vulnerabilities unique to AI infrastructure.
Attacks Against AI Systems7:32
Discusses specific attack vectors like evasion attacks or model theft.
AI-Based Security Testing7:14
How to test AI models for vulnerabilities and robustness.

What is Generative AI14:12
Defines GenAI and how it creates new content (text, images, code).
What are Large Language Models (LLMs)6:10
Explains the architecture behind models like GPT and BERT.
Exploring Generative AI Security Risks15:20
Discusses risks specific to content generation, such as deepfakes or misinformation.
Safeguarding Data Privacy and Confidentiality6:37
Best practices for protecting sensitive user data when using GenAI.
Vulnerabilities from Generative AI Providers8:37
Risks associated with relying on external APIs and third-party AI services.

Preventing Data Exposure6:23
Techniques to ensure sensitive data is not inadvertently exposed by AI outputs.
The New Era of Cyber Threat Actors13:50
How attackers are using AI to enhance their own capabilities.
Data Leakage Risks in Generative AI7:38
Understanding how proprietary data can leak into public models.
Advanced Techniques: Indirect Prompt Injections8:02
A look at complex attacks where external data manipulates the LLM's behavior.
Challenges of Inadequate AI Sandboxing4:59
The dangers of running AI models without proper isolation from critical systems.

Hallucinations in LLMs6:37
Understanding why AI generates false information and how to manage it.
Defending Against Server-Side Request Forgery (SSRF)4:43
Protecting AI systems from being tricked into accessing internal servers.
Strategies to Combat DDoS Attacks3:11
specific methods to protect AI resources from Denial of Service attacks.
Counteracting Data Poisoning in AI Models7:41
Preventing attackers from corrupting the training data to manipulate model behavior.

Addressing Model Bias: A Critical Look8:03
Advanced strategies for detecting and correcting bias in deployed models.
Navigating Copyright in AI Outputs3:48
Understanding the legal landscape regarding who owns AI-generated content.
How to Secure Generative AI11:30
Comprehensive security measures for GenAI deployments.
Threat Modeling and Generative AI Security11:25
Applying threat modeling techniques (like STRIDE) specifically to AI systems.

Requirements

No coding, programming, labs, or hacking experience required—purely conceptual and strategic
Basic familiarity with general cybersecurity concepts is helpful but not at all mandatory
An interest in AI security strategy, governance, compliance, or risk management leadership

Description

AI is reshaping cybersecurity faster than most organizations can keep up with. Prompt injections. Data poisoning. Model hallucinations. LLM data leakage. These aren't buzzwords from a conference presentation—they're active, real threats already hitting businesses. And the professionals who actually understand them? They're in serious demand.

This AI cybersecurity course gives you that strategic understanding—without a single line of code.

We start with governance. Because that's what decision-makers and compliance teams actually need: a real framework for managing AI risk at the organizational level. You'll learn how AI governance works, what the NIST framework says about AI-specific risks, and how to build a governance program that holds up under audit scrutiny.

Then the AI cybersecurity course moves into Generative AI and Large Language Model security—where the most interesting (and honestly terrifying) new threats live. Prompt injection attacks. Hallucination risks in production systems. Data leakage through LLMs. These are problems most organizations have adopted AI faster than they've thought about, and this section gives you the conceptual tools to address them properly.

Advanced threats next. How are modern attackers using AI for data poisoning? What does an AI-enabled DDoS attack look like? How does SSRF intersect with machine learning? I've seen teams blindsided by these questions in real security reviews—and this AI cybersecurity course makes sure you're not one of them.

Ethics and legality round things out. Model bias, AI copyright, automated decision-making liability. Stuff that keeps legal and compliance teams up at night.

You get an AI Governance Checklist at the end you can actually use immediately to audit AI systems in your own organization. That alone is worth the course.

No labs. No code. Just clear, strategic AI cybersecurity knowledge—the kind that matters at the leadership and compliance level.

Who this course is for:

Cybersecurity professionals pivoting into AI security, governance, or compliance-focused roles
IT managers and compliance officers overseeing AI tool adoption and integration decisions
Legal and ethical consultants advising on emerging AI risks and regulatory implications
Students and professionals building strategic AI cybersecurity expertise for leadership roles

AI Cybersecurity Risks: Master Threats & Governance Now

What you'll learn

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Course content

Introduction to AI and Governance6 lectures • 30min

AI Risk Management and Cyber Security6 lectures • 55min

Generative AI Overview and Security Risks5 lectures • 51min

Advanced Threats and Mitigation Strategies5 lectures • 41min

Security Challenges and Solutions in LLMs4 lectures • 22min

Ethical and Legal Considerations of Artificial Intelligence4 lectures • 35min

Implementation and Practical Considerations2 lectures • 8min

Bonus Lecture1 lecture • 1min

Requirements

Description

Who this course is for: