Programme Parameters
Advanced Postgrad
3 Weeks (30 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
AI in Cybersecurity Operations focuses on how machine learning transforms the day-to-day of a security operations centre. You learn to apply AI across the SOC workflow: detecting threats in logs and network data, triaging and prioritising alerts to cut noise, assisting threat hunting, and driving automated and orchestrated response (SOAR). The course also covers the adversarial reality — attackers adapting to and targeting ML — and the human-in-the-loop balance operations require. You finish able to reason about applying AI across security operations. A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This course covers AI in cybersecurity operations — applying machine learning across the SOC for detection, triage, threat hunting and automated response.
📋 Course Objectives
1. Detect threats in logs and network data.
2. Triage and prioritise alerts to reduce noise.
3. Assist threat hunting with AI.
4. Drive automated response and SOAR.
5. Account for adversarial and human-in-the-loop realities.
👥 Who Should Enroll?
• SOC analysts and security engineers
• Threat-hunting and incident-response teams
• Security data scientists
• Students of cybersecurity operations
🚀 Key Learning Outcomes
• An understanding of AI in security operations.
• A SOC-workflow perspective.
• A defence-in-depth approach.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
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.
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.
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.
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.
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.
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.
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | SIEM |
| Covered Tool / Platform | SOAR |
| Covered Tool / Platform | MITRE ATT&CK |
| Covered Tool / Platform | Machine Learning |
| Covered Tool / Platform | Natural Language Processing (NLP) |
| Covered Tool / Platform | Generative AI (GenAI) |
| Covered Tool / Platform | Large Language Models (LLMs) |
| Covered Tool / Platform | Python |
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