Generative AI skills are becoming an essential part of a cybersecurity professional’s toolkit. Begin by learning how to distinguish generative AI from discriminative AI. You’ll explore real-world generative AI use cases and discover popular generative AI models and tools for text, code, image, audio, and videos.

Next, delve into generative AI prompts engineering concepts, their real-world business uses, and prompt techniques like zero-shot and few-shot, and others. You’ll explore popular prompt engineering tools including IBM Watsonx, Prompt Lab, Spellbook, and Dust.

Then dive into fundamental concepts of generative AI use for cybersecurity. Gain valuable job-ready skills when you apply generative AI techniques to real-world scenarios, including UBEA, threat intelligence, report summarization, and playbooks, and assess their impact and vulnerabilities. Learn how generative AI models can help mitigate attacks, analyze real-world case studies, and learn to identify key implementation factors.

Throughout your learning journey, you’ll create a project portfolio to share your provable skills with potential employers. And earn a shareable course certificate and badge that verifies your achievement.

This course is designed for everyone, including professionals, executives, students, and enthusiasts, interested in learning about generative AI and leveraging its capabilities in their work and lives.   

This course is your first step toward understanding the capabilities of generative AI, powered by different models, including large language models (LLMs).   

In this course, you will learn about the fundamentals and evolution of generative AI. You will explore the capabilities of generative AI in different domains, including text, image, audio, video, virtual worlds, code, and data. You will understand the applications of generative AI across different sectors and industries. You will learn about the capabilities and features of common generative AI models and tools, such as GPT, DALL-E, Stable Diffusion, and Synthesia.    

Hands-on labs, included in the course, provide an opportunity to explore the use cases of generative AI through IBM Generative AI Classroom and popular tools like ChatGPT. You will also hear from the practitioners about the capabilities, applications, and tools of generative AI.

Generative AI: Introduction and Applications

This course is designed for everyone, including professionals, executives, students, and enthusiasts interested in leveraging effective prompt engineering techniques to unlock the full potential of generative artificial intelligence (AI) tools like ChatGPT. 

Prompt engineering is a process to effectively guide generative AI models and control their output to produce desired results. In this course, you will learn the techniques, approaches, and best practices for writing effective prompts. 

You will learn about prompt techniques like zero-shot and few-shot, which can improve the reliability and quality of large language models (LLMs). You will also explore various prompt engineering approaches like Interview Pattern, Chain-of-Thought, and Tree-of-Thought, which aim at generating precise and relevant responses. 

You will be introduced to commonly used prompt engineering tools like IBM watsonx Prompt Lab, Spellbook, and Dust.  

The hands-on labs included in the course offer an opportunity to optimize results by creating effective prompts in the IBM Generative AI Classroom. You will also hear from practitioners about the tools and approaches used in prompt engineering and the art of writing effective prompts.

Generative AI: Prompt Engineering Basics

This short course provides cybersecurity professionals and enthusiasts with the latest Generative AI tools to address complex cybersecurity challenges.

The course focuses on combating the exploitation of undetected vulnerabilities for which organizations increasingly turn to Artificial Intelligence (AI) and Machine Learning (ML). Generative AI, a transformative technology, emerges as a vital cybersecurity tool, detecting and preventing attacks by identifying and neutralizing unknown vulnerabilities before causing significant harm. 

The course explores foundational generative AI principles and their application in real-world cybersecurity, encompassing User and Entity Behavior Analytics (UEBA), threat intelligence, report summarization, playbooks, and its impact on phishing, malware, misinformation, and deepfakes. Additionally, participants learn about potential Natural Language Processing (NLP) attack techniques, like prompt injection, and strategies to mitigate them.  

The course ensures learners stay updated on the latest trends in using generative AI for cyber defense and threat prediction. 

Throughout the course you will be able to apply your knowledge with hands-on labs. Additionally, real-world inspired exercises will help you practice what you have learned. 

This course is suitable for both existing cybersecurity professionals who want to enhance their careers, as well as those aspiring to start a career in this field, such as cybersecurity specialists, cybersecurity analysts, cybersecurity engineers, and so on.  Cybersecurity skills and basic generative AI knowledge is beneficial for this course.

IBM Generative AI for Cybersecurity Professionals

About the Program

Description

Deliverables

0 Credits

Academic Excellence

Outcomes

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Akamai Consulting and Support