Cyber security and Artificial Intelligence Risk Course

Name: Cyber security and Artificial Intelligence Risk Course
Rating: 3.8 (97 reviews)

Learn Artificial Intelligence governance and Machine learning systems

Created byVijaykumar Uddhadi

Last updated 11/2025

English

English [Auto],

What you'll learn

Creating AI Governance Framework
Implementing Controls in ML Model Lifecycle
Artificial Intelligence and Machine Learning risks
Cyber-security risks of AI systems

Course content

4 sections • 12 lectures • 37m total length

Introduction1:13
Introduce cyber security and AI risk management, governance frameworks, and the differences between traditional programming and machine learning, including AI bios, model training, and the NIST AI Risk Management Framework.

AI Overview4:05
Explore how data science experts build intelligent systems with data and algorithms, while cyber security specialists protect data from breaches and manipulation, and risk governance frameworks ensure responsible artificial intelligence.
Overview of ML6:06
Learn how machine learning uses data to learn, adapt, and predict with supervised, unsupervised, and reinforcement methods, and explore AI governance and risk management such as the NIST framework.

AI bias3:31
Explore ai bias by examining common bias types and real-world examples, then mitigate risk with diverse datasets, fairness metrics, transparency, and human oversight.
AI Governance framework2:51
Lead with an AI governance framework that enforces ethical principles, defined roles, policies, audits, risk management, transparency, explainability, training, and continuous monitoring for regulatory-compliant, responsible AI.
AI regulations3:55
Explore how AI regulations apply a risk-based framework to govern development and use. From bans on unacceptable risks to high-risk obligations and transparency, learn core regulatory themes.
Need for AI Governance2:44
Ai governance establishes frameworks, policies, and practices to ensure responsible, ethical, and safe ai deployment across healthcare and finance, mitigating operational, legal, and reputational risks while promoting transparency.
NIST AI Risk Management Framework2:27
Apply the NIST AI RMF to identify, assess, and manage AI risks—bias, security, privacy, and legal exposure—across governance, map, measure, and manage functions for trustworthy, responsible AI.

AI based attacks2:22
Explore how AI-based attacks use machine learning to craft phishing emails, texts, and voice messages, create deepfakes, automate social engineering campaigns, and deploy data poisoning, adversarial and evasion attacks.
AI based security testing2:38
Learn AI-based security testing, including automated vulnerability scanning, AI penetration testing, threat modeling, and adversarial testing, with CI/CD integration for early vulnerability detection.
AI cyber-security framework2:36
Navigate a comprehensive AI cybersecurity framework guiding risk management across the AI lifecycle from development to decommissioning. Adopt governance, risk mapping, privacy, and monitoring with NIST AI RMF.
The Rise of Agentic AI and its risks2:34
Agentic AI autonomously sets goals, plans, and executes complex tasks with minimal supervision. Its proactive, adaptive, and collaborative reasoning introduces risks like unintended actions, opacity, security vulnerabilities, and governance challenges.

Requirements

No need of knowledge in AI or Machine Learning
No programming or data science experience needed
A PC or Mobile phone needed to follow Lectures

Description

Artificial Intelligence (AI) is rapidly transforming industries worldwide, creating new opportunities but also introducing significant risks that traditional cybersecurity and governance frameworks cannot fully address(see the generated image above). The global AI market is expected to reach over US$300 billion by 2026, making it crucial for organizations to adapt and respond to these emerging challenges(see the generated image above). Many professionals find AI governance and cybersecurity daunting due to its complexities and the fact that conventional controls are often insufficient for AI systems(see the generated image above).

This comprehensive course is designed to bridge the knowledge gap for risk management professionals, cybersecurity experts, and anyone interested in the evolving landscape of AI risk. You do not need a technical background to benefit from this course. The curriculum covers key risks associated with AI and Machine Learning models, practical strategies for managing these risks, and the creation of robust governance frameworks within organizations(see the generated image above). You will also learn how to identify and mitigate cybersecurity threats specific to AI systems, implement essential security controls throughout the machine learning lifecycle, and leverage tools like ChatGPT to support your security processes(see the generated image above). If you want to understand and manage AI risks effectively, this course is tailored for you—so let’s get started

Who this course is for:

AI Professionals
Cyber Security Professionals
Risk management Professionals

Cyber security and Artificial Intelligence Risk Course

What you'll learn

Explore related topics

Course content

Introduction1 lecture • 1min

Overview of AI2 lectures • 10min

AI Governance and Risk management5 lectures • 15min

AI Cyber security framework4 lectures • 10min

Requirements

Description

Who this course is for: