AI in Cybersecurity Operations

Defend Smarter—Harness AI to Revolutionize Cybersecurity Operations

MODE
Mode(Online) TYPE
Mentor Based LEVEL
Moderate

Skills you will gain:

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:

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

What you will learn?

Week 1: Foundations of AI and Cybersecurity

Module 1: Cybersecurity Essentials for AI Practitioners

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)

Module 2: Introduction to AI in Cybersecurity

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

Week 2: Building AI Models for Security Operations

Module 3: Data-Driven Threat Detection

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

Module 4: AI Pipeline Design for SOCs

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

Week 3: Operationalizing and Governing AI in Cybersecurity

Module 5: Automation, Response, and AI Agents

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

Module 6: Risk, Compliance, and Future Trends

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

Intended For :

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