| Attribute | Detail |
|---|---|
| Format | Online (e-LMS) |
| Level | Moderate |
| Duration | 3 Weeks |
| Certification | e-Certification + e-Marksheet |
| Fee | ₹10749 / $124 |
| Tools | SIEM SOAR MITRE ATT&CK Machine Learning Natural Language Processing (NLP) Generative AI (GenAI) Large Language Models (LLMs) Python |
About the AI in Cybersecurity Operations Course
AI in Cybersecurity Operations is a practical, expert-led course designed for those working in or aspiring to enter the cybersecurity domain.
The course explores how AI and machine learning are transforming the cybersecurity lifecycle—from threat intelligence and anomaly detection to automated response and predictive defense. Participants will learn to apply AI tools and techniques to enhance SOC (Security Operations Center) workflows, identify malicious behavior, and reduce incident response times.
Program Highlights
• Comprehensive coverage of AI in Cybersecurity Operations from fundamentals to advanced applications
• Hands-on projects and real-world case studies in Cybersecurity
• Expert-curated curriculum aligned with current industry standards
• Access to recorded lectures and e-LMS platform for flexible, self-paced learning
• e-Certification and e-Marksheet upon successful completion
• Dedicated mentor support and interactive doubt-clearing sessions
• Practical experience with tools: SIEM, SOAR, MITRE ATT&CK, Machine Learning
• Career-oriented training for academic and professional growth in Cybersecurity
Course Curriculum
Module 1: Cybersecurity Essentials for AI Practitioners
- Explore the current threat landscape and core cyber defense principles.
- Understand SOC (Security Operations Center) workflows and critical roles.
- Identify common attack vectors and tactics using MITRE ATT&CK framework.
- Analyze diverse data sources in cybersecurity, including logs, alerts, and SIEMs.
Module 2: Introduction to AI in Cybersecurity
- Examine the limitations of traditional detection systems and the necessity of AI.
- Discover key AI techniques: anomaly detection, NLP, and machine learning classification.
- Investigate practical use cases in threat detection, alert triage, and fraud prevention.
- Evaluate real-world case studies comparing AI and human analytical capabilities.
Module 3: Data-Driven Threat Detection
- Master techniques for collecting and preprocessing diverse security data.
- Implement feature engineering strategies for network and log data.
- Apply unsupervised learning methods for effective anomaly detection.
- Utilize supervised learning algorithms for malware and intrusion detection.
Module 4: AI Pipeline Design for SOCs
- Integrate AI models seamlessly into existing SOC tooling (SIEM, SOAR).
- Develop strategies for alert prioritization and noise reduction using machine learning.
- Leverage Natural Language Processing (NLP) for real-time threat intelligence.
- Evaluate model performance and implement false positive reduction techniques.
Module 5: Automation, Response, and AI Agents
- Design and implement AI-driven incident response playbooks.
- Understand and utilize Security Orchestration, Automation, and Response (SOAR) systems.
- Explore the application of Generative AI and Large Language Models (LLMs) in cyber operations (e.g., log analysis).
- Develop capabilities for autonomous threat hunting and employing AI co-pilots.
Module 6: Risk, Compliance, and Future Trends
- Navigate governance and compliance frameworks for AI-supported security.
- Address ethical challenges inherent in automated defense systems.
- Analyze adversarial machine learning techniques in cybersecurity.
- Project future trends, including the AI arms race and evolving cyber threats.
Tools, Techniques, or Platforms Covered
SIEM SOAR MITRE ATT&CK Machine Learning Natural Language Processing (NLP) Generative AI (GenAI) Large Language Models (LLMs) Python
Real-World Applications
- Apply AI in Cybersecurity Operations skills directly to academic research, thesis work, and publications
- Build a professional portfolio showcasing practical Cybersecurity competencies
- Solve industry-relevant problems using AI in Cybersecurity Operations methodologies and tools
- Contribute to open-source projects and collaborative research in Cybersecurity
- Prepare for competitive examinations, interviews, and professional certifications in Cybersecurity
Who Should Attend & Prerequisites
- Industry-recognized e-Certification + e-Marksheet from NSTC
- Hands-on training with practical projects and industrial datasets
- Dedicated expert mentorship and doubt resolution
Certification
