Generative AI: Boost Your Cybersecurity Career

Go to Course: https://www.coursera.org/learn/generative-ai-boost-your-cybersecurity-career

Introduction

Explain the fundamental concepts of Generative AI and its significance in cybersecurity.

Apply generative AI techniques to real-world cybersecurity scenarios, including UBEA, threat intelligence, report summarization, and playbooks.

Assess the use of generative AI in cybersecurity against threats, like phishing and malware, and understand potential NLP-based attack techniques.

Mitigate attacks on generative AI models and analyze real-world case studies, identifying key success factors in implementation.

Syllabus

Get Started with Gen AI in Cybersecurity

In this module, you will learn to use generative AI for cybersecurity. First, you will explore the dependencies on training data quality used in generative AI models. You learn to address transparency challenges in Large Language Models (LLMs). The lesson further covers types of cybersecurity analytics and how generative AI enhances cybersecurity analytics, including incident response and forensic analysis. Then, you will learn EDR and SIEM functions for effective threat detection and response. Additionally, you will learn the importance of cybersecurity playbooks for strategic frameworks for incident report summarization using generative AI.

In this module, you will learn to use generative AI for cybersecurity. Lesson 1 emphasizes generative AI’s role in automating incident analysis and integrating it into cybersecurity for efficient responses. It discusses how generative AI complements traditional vulnerability management. It includes threat hunting and streamlining incident response communication. The lesson highlights collaborative synergy for a robust defense against evolving cybersecurity threats. Lesson 2 highlights proactive threat detection and generative AI integration for efficient automation. It underscores the significance of generative AI in advancing cybersecurity and shaping the future of threat detection, concluding with QRadar Suite’s capabilities. Lesson 3 discusses the challenges of generative AI, such as risk exploration, ChatGPT management, mass adoption, and privacy concerns. Security oversights risk data and revenue loss, necessitating a security-by-design approach and ethical considerations in cybersecurity.

In this module, you will demonstrate your skills through hands-on exercises. You will develop proficiency in utilizing generative AI for cybersecurity in a given project. The project will have five distinct exercises. In exercise 1, you will explore the capability of a Generative AI platform to detect spam mail. In exercise 2, you will employ generative AI techniques to examine the code structures of malware programs. In exercise 3, you will analyze network logs with generative AI to identify potential security threats. In exercise 4, you will learn generative AI for crafting detailed incident reports, covering the identification, containment, eradication, and recovery phases. In the fifth exercise, you will create a detailed playbook for responding to malware attacks.