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.
What you'll learn
Distinguish between generative AI and discriminative AI
Explore real-world use cases for generative AI in cybersecurity
Apply prompt engineering techniques to generate relevant results
Utilize tools for text, code, image, audio, and video generation
Assess the impact of generative AI on cybersecurity vulnerabilities
Create a project portfolio to demonstrate acquired skills
Course objectives
Equip students with job-ready skills in generative AI for cybersecurity
Enable practical application of AI techniques to real-world cybersecurity challenges
Foster understanding of prompt engineering and its implications for business