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.
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
