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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.
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.
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Professional Certification Program
To bridge cybersecurity knowledge with cutting-edge AI methods
To upskill professionals in operational AI tool deployment
To accelerate detection, response, and defense using intelligent systems
To build strategic readiness for AI-integrated cyber threats
Chapter 1.1: Threat Landscape and Cyber Defense Basics
Chapter 1.2: SOC (Security Operations Center) Workflows and Roles
Chapter 1.3: Common Attack Vectors and Tactics (MITRE ATT&CK)
Chapter 1.4: Data Sources in Cybersecurity (Logs, Alerts, SIEMs)
Chapter 2.1: Why AI? Gaps in Traditional Detection Systems
Chapter 2.2: Key AI Techniques: Anomaly Detection, NLP, and ML Classification
Chapter 2.3: Use Cases – Threat Detection, Alert Triage, and Fraud Prevention
Chapter 2.4: Real-World Case Studies – AI vs. Human Analysts
Chapter 3.1: Collecting and Preprocessing Security Data
Chapter 3.2: Feature Engineering for Network and Log Data
Chapter 3.3: Unsupervised Learning for Anomaly Detection
Chapter 3.4: Supervised Learning for Malware and Intrusion Detection
Chapter 4.1: Model Integration into SOC Tooling (SIEM, SOAR)
Chapter 4.2: Alert Prioritization and Noise Reduction Using ML
Chapter 4.3: Real-Time Threat Intelligence with NLP
Chapter 4.4: Model Evaluation and False Positive Reduction Strategies
Chapter 5.1: AI-Driven Incident Response and Playbooks
Chapter 5.2: Security Orchestration, Automation, and Response (SOAR) Systems
Chapter 5.3: GenAI and LLMs in Cyber Operations (e.g., Log Analysis, Report Writing)
Chapter 5.4: Autonomous Threat Hunting and AI Co-pilots
Chapter 6.1: Governance and Compliance in AI-Supported Security
Chapter 6.2: Ethical Challenges in Automated Defense Systems
Chapter 6.3: Adversarial ML in Cybersecurity
Chapter 6.4: Future Outlook – AI Arms Race and Evolving Threats
~Video content aligned with weekly modules
Theme: Foundations of AI and Cybersecurity
Introduction to AI in Cybersecurity Operations
Cyber Threat Landscape: Common Attack Types and Patterns
Anatomy of a SOC: People, Processes, and Platforms
Understanding Log Data and Security Alerts
Why Traditional Rules-Based Systems Fail at Scale
AI Fundamentals for Cybersecurity: Supervised vs. Unsupervised Learning
Use Cases: Threat Detection, Phishing, Malware, Fraud
Case Study: AI vs. Human Analyst in Alert Triage
Week 1 Summary and Threat Scoping Exercise
Theme: Building AI-Powered Detection Systems
Preparing Security Data for ML Models
Feature Engineering from Log Files and Network Traffic
Building Anomaly Detection Models (Isolation Forests, Autoencoders)
Malware Detection with Classification Models
Evaluating Model Accuracy and Managing False Positives
Integrating ML Models with SIEM Tools (Splunk, Elastic, Sentinel)
Alert Prioritization Using AI Scoring
NLP for Security: Log Analysis and Threat Report Parsing
Week 2 Walkthrough: Creating a Triage Model Pipeline
Theme: Deployment, Automation, and Governance
Deploying AI Models into Security Workflows
Introduction to SOAR Platforms and Workflow Automation
Using GenAI/LLMs in Security: Summarizing Logs and Writing Reports
Building AI-Driven Playbooks for Incident Response
Adversarial Attacks on AI Security Models
Governance, Bias, and Ethics in Automated Defense
Monitoring, Feedback Loops, and Continuous Learning
Future Trends: Autonomous Threat Hunting and AI Co-Pilots
Capstone Briefing: Designing an AI-Enhanced SOC
Title: Intelligence-Driven Defense: Why AI is Reshaping Security Operations
Duration: 60 minutes
Focus: How AI augments SOC operations, threat detection, and analyst workflows
Guest: Chief Information Security Officer (CISO) or Threat Intelligence Lead
Interactive: Live walkthrough of a real-world security alert lifecycle with audience Q&A
Title: Building AI Detection Systems that Actually Work
Duration: 75 minutes
Focus: Designing, validating, and deploying machine learning models in a SOC environment
Guest: Security Data Scientist / Head of ML Security Engineering
Interactive: Hands-on demo: feature engineering and alert classification with feedback
Title: Automation and Adversaries: Operating AI Defenses at Scale
Duration: 90 minutes
Focus: SOAR integration, adversarial threats, and ethical boundaries of AI in cyber defense
Guest Panel: SOC Manager + MLOps Engineer + Cybersecurity Ethics Expert
Interactive: Capstone critique: reviewing student-designed AI+SOAR response frameworks
Cybersecurity analysts, SOC engineers, network and system administrators
AI/ML practitioners interested in cybersecurity applications
Professionals and students with backgrounds in computer science or IT
Basic knowledge of security concepts and Python recommended
Understand and apply machine learning techniques to security data
Integrate AI tools into SOC environments for detection and response
Automate threat classification and anomaly detection tasks
Evaluate and deploy AI-powered security platforms
Balance innovation with responsible, explainable AI practices
Fee: INR 21499 USD 249
We are excited to announce that we now accept payments in over 20 global currencies, in addition to USD. Check out our list to see if your preferred currency is supported. Enjoy the convenience and flexibility of paying in your local currency!
List of Currencies
AI-powered threat detection and defense
SOC optimization and threat response
Security AI research and product development
AI Cybersecurity Engineer
Threat Intelligence Analyst (AI-enhanced)
SOC Analyst (Level 2/3 with AI toolsets)
AI/ML Engineer – Cybersecurity Focus
Cyber Defense Automation Specialist
Security Architect with AI Integration
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