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 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.
Applied Learning Project
This Specialization emphasizes applied learning and includes a series of hands-on activities and projects. In these exercises, you’ll take the theory and skills you’ve gained and practice them with real-world scenarios.
Projects include:
Generate Text, Images, and Code using Generative AI
Apply Prompt Engineering Techniques and Best Practices’
Use Generative AI in Cybersecurity for content filtering, threat analysis, and automated response generation