AI in Cybersecurity Operations

• 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

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