AED
UAD Dirham
Code: AED
Mentor Based
AI in Cybersecurity Operations
Defend Smarter—Harness AI to Revolutionize Cybersecurity Operations
Early access to the e-LMS platform is included
About This 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.
Aim
To equip cybersecurity professionals and IT teams with the knowledge and skills to integrate Artificial Intelligence into security operations, enabling faster threat detection, intelligent response, and robust defense strategies in today’s evolving threat landscape.
Program Objectives
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To bridge cybersecurity knowledge with cutting-edge AI methods
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To upskill professionals in operational AI tool deployment
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To accelerate detection, response, and defense using intelligent systems
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To build strategic readiness for AI-integrated cyber threats
Program Structure
Week 1: Foundations of AI and Cybersecurity
Module 1: Cybersecurity Essentials for AI Practitioners
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Chapter 1.1: Threat Landscape and Cyber Defense Basics
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Chapter 1.2: SOC (Security Operations Center) Workflows and Roles
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Chapter 1.3: Common Attack Vectors and Tactics (MITRE ATT&CK)
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Chapter 1.4: Data Sources in Cybersecurity (Logs, Alerts, SIEMs)
Module 2: Introduction to AI in Cybersecurity
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Chapter 2.1: Why AI? Gaps in Traditional Detection Systems
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Chapter 2.2: Key AI Techniques: Anomaly Detection, NLP, and ML Classification
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Chapter 2.3: Use Cases – Threat Detection, Alert Triage, and Fraud Prevention
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Chapter 2.4: Real-World Case Studies – AI vs. Human Analysts
Week 2: Building AI Models for Security Operations
Module 3: Data-Driven Threat Detection
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Chapter 3.1: Collecting and Preprocessing Security Data
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Chapter 3.2: Feature Engineering for Network and Log Data
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Chapter 3.3: Unsupervised Learning for Anomaly Detection
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Chapter 3.4: Supervised Learning for Malware and Intrusion Detection
Module 4: AI Pipeline Design for SOCs
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Chapter 4.1: Model Integration into SOC Tooling (SIEM, SOAR)
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Chapter 4.2: Alert Prioritization and Noise Reduction Using ML
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Chapter 4.3: Real-Time Threat Intelligence with NLP
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Chapter 4.4: Model Evaluation and False Positive Reduction Strategies
Week 3: Operationalizing and Governing AI in Cybersecurity
Module 5: Automation, Response, and AI Agents
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Chapter 5.1: AI-Driven Incident Response and Playbooks
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Chapter 5.2: Security Orchestration, Automation, and Response (SOAR) Systems
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Chapter 5.3: GenAI and LLMs in Cyber Operations (e.g., Log Analysis, Report Writing)
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Chapter 5.4: Autonomous Threat Hunting and AI Co-pilots
Module 6: Risk, Compliance, and Future Trends
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Chapter 6.1: Governance and Compliance in AI-Supported Security
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Chapter 6.2: Ethical Challenges in Automated Defense Systems
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Chapter 6.3: Adversarial ML in Cybersecurity
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Chapter 6.4: Future Outlook – AI Arms Race and Evolving Threats
Who Should Enrol?
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Cybersecurity analysts, SOC engineers, network and system administrators
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AI/ML practitioners interested in cybersecurity applications
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Professionals and students with backgrounds in computer science or IT
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Basic knowledge of security concepts and Python recommended
Program Outcomes
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Understand and apply machine learning techniques to security data
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Integrate AI tools into SOC environments for detection and response
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Automate threat classification and anomaly detection tasks
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Evaluate and deploy AI-powered security platforms
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Balance innovation with responsible, explainable AI practices
Fee Structure
Discounted: ₹21499 | $249
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- One-on-one project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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