CSRP | Artificial Intelligence for Cybersecurity

Artificial Intelligence for Cybersecurity – Full Course Syllabus

“Harnessing AI to Predict, Prevent, and Defend Against Cyber Threats”

Part of the CSRP Bootcamp | Phase 2 – Cybersecurity Technologies

Target Audience: Aspiring cybersecurity practitioners with foundational knowledge in cybersecurity tools and networking. This course is designed to introduce students to the application of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity.
Course Duration: 18 Lessons (84 Total Hours)

• 12 Instructor-Led Lessons: 4 hours each (48 hours)
• 6 Asynchronous Self-Study Lessons: 6 hours each (36 hours)
  Course Format: Blended Learning (Instructor-Led + Self-Study)
  Outcome: Gain practical skills in leveraging AI and ML to detect threats, prevent cyberattacks, and enhance security operations.

Course Overview:

The Artificial Intelligence for Cybersecurity course focuses on how AI and Machine Learning are transforming cybersecurity practices. Students will explore how AI is used for threat detection, anomaly detection, incident response, and predictive analytics, while understanding the limitations and ethical considerations of AI in security.

By the end of this course, students will:
✔ Understand AI and ML fundamentals and how they apply to cybersecurity
✔ Use AI to detect anomalies, malware, and advanced persistent threats (APTs)
✔ Build basic machine learning models for threat prediction and log analysis
✔ Explore the role of AI in modern SOCs (Security Operations Centers)
✔ Apply AI tools to real-world cybersecurity challenges

Course Objectives:

By the end of this course, students will be able to:

• Understand the core principles of Artificial Intelligence and Machine Learning
• Apply AI models to detect malware, phishing attacks, and network anomalies
• Use Python and AI frameworks (TensorFlow, Scikit-learn) for data analysis
• Leverage AI in threat hunting, incident response, and automated security operations
• Evaluate the ethical considerations and risks associated with AI in cybersecurity