Mastering Generative AI for Cybersecurity from edX

Generative AI is transforming cybersecurity, enabling faster threat detection, proactive defense strategies, and advanced solutions to combat sophisticated attacks. This course builds skills in valuable generative AII techniques and tools that help cybersecurity professionals effectively handle complex cybersecurity challenges.

During the course, you’ll explore undetected vulnerabilities by leveraging AI and machine learning. You’ll learn the foundational principles of gen AI and their practical applications in cybersecurity, including user and entity behavior analytics (UEBA), threat intelligence, report summarization, automated playbooks, countering phishing, malware, misinformation, and deepfakes. Plus, you’ll look at natural language processing attack techniques, such as prompt injection, and the strategies to counteract them.

You’ll learn how generative AI transforms Security Information and Event Management (SIEM) and Security Operations Center (SOC) workflows. The course covers how generative AI enhances incident response, threat detection, and vulnerability management.

You’ll also review the latest advancements in gen AI for cyber defense and threat prediction. Plus, you’ll complete real-world practical exercises, hands-on labs, and a final project where you’ll apply your skills.

If you’re an existing or aspiring cybersecurity professional looking to add in-demand gen AI skills to your resume, enroll today!

What's inside

Learning objectives

Leveraging the power of gen ai in cybersecurity, including ueba, threat intelligence, report summarization, and playbooks.
Using generative ai in countering cybersecurity threats like phishing and malware.

Streamlining siem and soc tasks with generative ai to optimize operational efficiency and threat management.
Integrating generative ai in cybersecurity operations, such as incident response, threat detection, and vulnerability management.

Leveraging the power of gen ai in cybersecurity, including ueba, threat intelligence, report summarization, and playbooks.
Using generative ai in countering cybersecurity threats like phishing and malware.
Streamlining siem and soc tasks with generative ai to optimize operational efficiency and threat management.
Integrating generative ai in cybersecurity operations, such as incident response, threat detection, and vulnerability management.

Syllabus

Module 1: Get Started with Generative AI in Cybersecurity

Video: Course Introduction

Reading: Prerequisites and Course Syllabus

Reading: Helpful Tips for Course Completion

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores the use of generative AI to enhance Security Information and Event Management (SIEM) and Security Operations Center (SOC) workflows, which is a growing trend in the industry

Covers strategies to counteract natural language processing attack techniques, such as prompt injection, which is a critical skill for maintaining the integrity of AI-driven systems

Includes hands-on labs and a final project, providing practical experience in applying generative AI to real-world cybersecurity challenges, which is essential for skill development

Presented by IBM, a company recognized for its contributions to cybersecurity and artificial intelligence, which lends credibility to the course content

Examines the ethical concerns of using AI in cybersecurity, which is an important consideration for responsible and effective implementation of these technologies

Requires learners to understand the transition from conventional AI to generative AI, which may necessitate some familiarity with basic AI concepts and cybersecurity principles

Reviews summary

Practical generative ai for cyber ops

According to learners, this course provides a highly relevant and practical introduction to applying generative AI in cybersecurity. Many students found the content, especially the hands-on labs and demos, extremely useful for understanding how Gen AI can be used in real-world scenarios like threat detection, incident response, and SIEM/SOC tasks. The course is praised for offering a solid overview and helping professionals upskill in this rapidly evolving field. While generally well-received, some feedback suggests certain topics could benefit from more in-depth coverage for advanced practitioners, and occasional technical issues with labs were mentioned, though many found them valuable despite minor hiccups. Overall, it's considered a valuable resource for cybersecurity professionals.

Good introduction to key concepts.

"Provides a solid foundation on the intersection of Generative AI and Cybersecurity."

"Excellent overview of the various applications of Gen AI in the cyber domain."

"Good starting point for understanding the basics and potential of Gen AI in security."

"Helped me grasp the fundamental principles and use cases."

"Covers a broad range of topics from UEBA to NLP attacks."

Labs and demos are a key strength.

"The hands-on labs were the highlight for me, reinforcing the concepts taught."

"The demos were very helpful in visualizing how Gen AI integrates with security systems."

"Labs allowed me to experiment with the tools and techniques covered in the lectures."

"The practical exercises solidified my understanding of the material."

"Appreciated the real-world scenarios presented in the labs."

Highly relevant for real-world cyber tasks.

"The practical applications demonstrated for threat intelligence and incident response are highly relevant to my work."

"I found the labs on using Gen AI for report summarization and playbooks particularly useful."

"Focuses heavily on how to actually *use* Gen AI in a cybersecurity context, which is great."

"Provides concrete examples of applying Gen AI to SIEM and SOC workflows."

"Learned practical techniques for vulnerability management and detection."

Occasional issues with the lab environment.

"Encountered some minor technical issues with the lab setup at times."

"The lab environment was sometimes slow or unresponsive."

"While the labs were valuable, getting them to work smoothly wasn't always easy."

"There were a few frustrating moments with the lab platform."

"Despite issues, the labs were worth the effort."

Sometimes lacks depth for advanced topics.

"While the breadth is good, some topics could go into more technical depth."

"Felt like it scratched the surface on certain advanced Generative AI techniques."

"Could benefit from more complex examples or case studies."

"It's a great overview, but isn't sufficient for deep specialization in all areas covered."

"Wish there were more details on integrating specific models."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Mastering Generative AI for Cybersecurity with these activities:

Review Foundational Cybersecurity Concepts

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Strengthen your understanding of core cybersecurity concepts to better grasp how generative AI enhances and transforms these areas.

Browse courses on Cybersecurity Fundamentals

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Review key cybersecurity definitions and principles.
Summarize common attack vectors and defense mechanisms.
Practice identifying vulnerabilities in sample systems.

Review 'Cybersecurity For Dummies'

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Gain a broader understanding of cybersecurity fundamentals to better appreciate the role and impact of generative AI.

View Cybersecurity For Dummies on Amazon

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Read the book's introductory chapters on cybersecurity principles.
Summarize key concepts related to threat detection and incident response.
Identify areas where generative AI could improve existing cybersecurity practices.

Review 'AI and Machine Learning for Coders'

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Understand the underlying AI and machine learning principles to better leverage generative AI tools in cybersecurity.

View AI and Machine Learning for Coders on Amazon

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Read the book's chapters on neural networks and deep learning.
Experiment with the coding examples provided in the book.
Apply the concepts learned to cybersecurity-related problems.

Four other activities

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Explore Generative AI Tutorials for Cybersecurity

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Deepen your understanding of generative AI applications in cybersecurity through hands-on tutorials and practical examples.

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Find tutorials on using generative AI for threat detection.
Follow tutorials on generating synthetic data for security testing.
Experiment with AI-powered tools for vulnerability assessment.

Blog Post: Generative AI Use Cases in Cybersecurity

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Solidify your knowledge by researching and writing about specific applications of generative AI in cybersecurity.

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Research different applications of generative AI in cybersecurity.
Write a blog post outlining the benefits and challenges of each application.
Publish the blog post on a relevant platform or share it with peers.

Develop a Generative AI-Powered Phishing Detector

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Apply your skills by building a practical tool that leverages generative AI to identify and mitigate phishing attacks.

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Gather a dataset of phishing emails and legitimate emails.
Train a generative AI model to distinguish between phishing and legitimate emails.
Test the model's accuracy and refine it as needed.
Deploy the model as a phishing detection tool.

Contribute to an Open-Source Cybersecurity Project

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Enhance your skills and contribute to the cybersecurity community by participating in open-source projects related to generative AI.

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Find an open-source cybersecurity project that uses generative AI.
Identify areas where you can contribute, such as bug fixes or new features.
Submit your contributions and participate in code reviews.

Career center

Learners who complete Mastering Generative AI for Cybersecurity will develop knowledge and skills that may be useful to these careers:

Incident Responder

An incident responder is tasked with identifying, containing, and eradicating security incidents. The focus is on minimizing damage and restoring systems to their normal operation. This course covers the use of gen AI for incident response, including generating incident reports and developing playbooks. A course like this is essential for an incident responder who would benefit from the knowledge of using AI to speed up the process of analyzing threats, responding to incidents, and also automating tasks. This course teaches skills directly relevant to incident handling and will help you respond more efficiently to cybersecurity events.

See salaries and explore the career path for Incident Responder

Security Operations Center Analyst

A security operations center analyst works within a team to monitor and respond to security events as they occur. The role requires a deep understanding of security tools and the ability to respond quickly. This course covers the use of gen AI to enhance Security Information and Event Management, which is a primary tool for a security operations center analyst. The course offers training in how generative AI can streamline tasks like incident response, vulnerability management, and threat detection, all of which directly impact the effectiveness of a security operations center analyst.

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Threat Intelligence Analyst

The role of a threat intelligence analyst involves gathering, analyzing, and disseminating information about potential security threats. A threat intelligence analyst needs to sift through vast amounts of information and understand the nature of attacks. This course specifically addresses how to use AI for threat intelligence, which includes identifying patterns, understanding attacker behavior, and summarizing findings from reports. By mastering these techniques, you will be better equipped to provide timely and accurate insights to defend against current and future threats. This course also covers how to use AI to analyze vast amounts of data, a crucial skill for a threat intelligence analyst.

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Vulnerability Analyst

Vulnerability analysts assess and identify weaknesses in systems and networks to secure them in advance of threats. This work requires knowledge of vulnerabilities and how they're exploited. This course includes modules on how to use AI to detect vulnerabilities, manage them, and generate reports. A vulnerability analyst will learn specific skills in this course that directly apply to their day to day work. This training will be useful for anyone pursuing a role as vulnerability analyst and will aid in identifying and mitigating weaknesses effectively.

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Cybersecurity Analyst

A cybersecurity analyst is responsible for monitoring and protecting an organization's computer systems and networks. This role involves identifying security threats, developing and implementing security measures, and responding to security incidents. This course provides hands-on experience with using generative AI to enhance threat detection, vulnerability management, and incident response, all of which are key responsibilities of a cybersecurity analyst. By learning to leverage AI for tasks like analyzing logs and generating incident reports, you will be well-prepared for the demands of a cybersecurity analyst position.

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Digital Forensics Analyst

Digital forensics analysts investigate cybercrimes and security incidents. This role involves collecting, preserving and analyzing data to find evidence of malicious activity. As digital forensics analysts need a good grasp of incident analysis and reporting, this course can be helpful because it explores how to use generative AI for incident reports and playbooks. A digital forensics analyst can gain a better understanding of how AI can aid in faster forensic analysis and evidence collection. This course may be useful for its specific modules on incident response and forensic analysis, which will help the digital forensics analyst to leverage AI for their work.

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Penetration Tester

A penetration tester, also called an ethical hacker, simulates cyberattacks to identify vulnerabilities in an organization's systems and networks. The role requires deep technical expertise and analytical thinking. This course may be helpful by exploring how gen AI can be used by malicious actors to create sophisticated attack vectors. By learning how such attacks are created, a penetration tester, using this knowledge, can better evaluate defenses. Additionally, the course explores how AI is used in attack analysis. This course may be useful for penetration testers to learn the cutting-edge AI tools and techniques that are used in cyber attacks.

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Data Security Analyst

A data security analyst helps to protect sensitive data, ensuring compliance with privacy regulations and preventing data breaches. This role requires technical skills as well as knowledge of data management. This course addresses how generative AI is used to improve analysis in security, which helps in identifying threats and patterns relevant to data security. This course may be helpful for a data security analyst because it explores the practical applications of AI for spotting risks and protecting data. Learning how to use AI in these contexts will benefit those working to secure and safeguard data.

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Cybersecurity Consultant

A cybersecurity consultant advises organizations on how to improve their security posture. This position requires technical knowledge as well as the ability to communicate complex ideas clearly. This course offers value in understanding the role AI plays in threat detection and mitigation. This course may help a cybersecurity consultant understand how to design a more resilient and secure infrastructure. By demonstrating knowledge of these AI techniques, a cybersecurity consultant can offer more current solutions and maintain a competitive edge.

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Information Security Manager

An information security manager is responsible for the overall security of an organization's information assets. This includes policy development, risk assessment and security awareness programs. This course may be useful as it addresses how AI can streamline security operations in incident response, threat detection, and vulnerability management. While this course does not directly cover policy or risk, it provides context for how generative AI affects security and can guide an information security manager to keep current with best practices. The technical knowledge of AI applications will be useful in planning and decision-making.

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Security Engineer

A security engineer is responsible for designing, implementing, and maintaining security systems and infrastructure. The role requires a broad understanding of security technologies and the ability to integrate them into an organization's environment. While this course does not cover all the aspects of security engineering, a security engineer will find value in learning about how generative AI enhances incident response, threat detection, and vulnerability management, which they will need to consider when designing a secure system. This course may be useful for integrating generative AI into their security architectures, which will make this work more accessible and efficient.

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Security Software Developer

A security software developer creates and maintains security software and tools. The role requires software development skills as well as a solid understanding of security principles. This course introduces how to apply AI in cybersecurity, which may be of value to a security software developer when creating new security solutions. While this course does not focus specifically on software development, it gives context for how AI is impacting modern cybersecurity workflows and how they might improve software. This course may be useful for developing skills in areas such as threat detection or automated playbooks, which will inform development work.

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Security Architect

A security architect designs the security framework of an organization. This requires a deep understanding of technologies and risk management. This course may be useful as it gives insights into how AI can improve threat detection and response. Learning about how AI will influence security system design is useful for anyone with an aspiration to a career as a security architect. This course also provides some understanding of the cutting-edge of AI implementation, which is a useful skill for this role.

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Network Security Engineer

A network security engineer focuses on securing an organization's network infrastructure. This role requires knowledge of network protocols, security devices, and threat mitigation. While this course is not solely focused on network security, it does discuss the general use of AI for threat detection and vulnerability management. A network security engineer may find this course valuable in understanding the current AI landscape, which may help to develop more secure network architectures that protect against AI propelled threats such as sophisticated phishing attacks, malware and deepfakes. This course may be useful in gaining an overview of AI and how it is used in security and can be applied to network security.

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Security Researcher

A security researcher is responsible for exploring and identifying new cybersecurity threats and vulnerabilities. A security researcher is typically requires an advanced degree. This course will address techniques for identifying novel threats using gen AI. Security researcher might be interested in how AI can be used to analyze attack patterns and create more robust security measures. Learning about AI in this context will provide a thorough background in cutting-edge security research. This course may be useful for a researcher looking into AI applications in cybersecurity.

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Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Mastering Generative AI for Cybersecurity.

Cybersecurity For Dummies

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Provides a broad overview of cybersecurity concepts, making it an excellent resource for those new to the field. It covers essential topics such as network security, data protection, and risk management. Reading this book can help you establish a solid foundation before diving into the more advanced applications of generative AI in cybersecurity. It is particularly helpful for understanding the context in which AI tools are being applied.

Cybersecurity For Dummies

Paperback

AI and Machine Learning for Coders

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Provides a practical introduction to AI and machine learning concepts, focusing on coding examples and real-world applications. It covers essential topics such as neural networks, deep learning, and natural language processing. Reading this book can help you understand the underlying technologies behind generative AI and how to implement them in cybersecurity contexts. It is particularly valuable for those with a coding background who want to apply AI to security challenges.

AI and Machine Learning for Coders

Paperback

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AI and Machine Learning for Coders

Kindle Edition

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