IBM
Generative AI: Boost Your Cybersecurity Career
IBM

Generative AI: Boost Your Cybersecurity Career

This course is part of IBM Generative AI for Cybersecurity Professionals Specialization

Taught in English

Some content may not be translated

Rav Ahuja
Dr. Manish Kumar

Instructors: Rav Ahuja

Included with Coursera Plus

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

9 hours (approximately)
Flexible schedule
Learn at your own pace

What you'll learn

  • 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.

Details to know

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Recently updated!

February 2024

Assessments

8 assignments

Course

Gain insight into a topic and learn the fundamentals

Intermediate level

Recommended experience

9 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the IBM Generative AI for Cybersecurity Professionals Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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Earn a career certificate

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There are 3 modules in this course

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.

What's included

11 videos3 readings3 assignments3 app items6 plugins

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.

What's included

10 videos3 readings4 assignments1 app item8 plugins

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.

What's included

3 readings1 assignment4 plugins

Instructors

Rav Ahuja
IBM
52 Courses2,395,388 learners

Offered by

IBM

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