Duration 5 Days – 40 hrs.

 

Overview

 

The CompTIA Security Analytics & AI (SecAI+) Certification Training Course equips cybersecurity professionals with the knowledge and skills to leverage artificial intelligence (AI) and data analytics in strengthening security operations.

 

This course focuses on integrating AI-driven threat detection, behavioral analytics, machine learning models, and security automation into modern cybersecurity frameworks. Participants will learn how to analyze large datasets, detect anomalies, respond to threats proactively, and implement AI-powered security solutions.

 

Designed to align with CompTIA SecAI+ certification objectives, this training combines theory, real-world scenarios, and hands-on exercises to prepare participants for both practical application and certification success.

 

Objectives

 

• Understand the fundamentals of AI and machine learning in cybersecurity
• Apply security analytics techniques to detect and respond to threats
• Analyze and interpret security data from multiple sources
• Identify anomalies using behavioral analytics and AI models
• Implement AI-driven threat intelligence and incident response
• Evaluate risks associated with AI in cybersecurity environments
• Design and integrate automated security solutions
• Prepare effectively for the CompTIA SecAI+ certification exam

 

Target Audience

 

• Cybersecurity Analysts and Engineers
• Security Operations Center (SOC) Analysts
• Threat Intelligence Analysts
• IT Security Professionals
• Data Analysts working in cybersecurity
• Risk and Compliance Professionals
• AI/ML Engineers interested in security applications

 

Prerequisites

 

• Basic understanding of cybersecurity concepts (e.g., threats, vulnerabilities, controls)
• Familiarity with networking fundamentals
• Knowledge of Security+ or equivalent experience is recommended
• Basic understanding of data analysis or scripting (Python is a plus)

 

Course Outline

 

Module 1: Introduction to AI in Cybersecurity

 

• Overview of AI and Machine Learning concepts
• Role of AI in modern cybersecurity
• Security Analytics vs Traditional Security Approaches
• Use cases of AI in threat detection and prevention

 

Module 2: Security Data Fundamentals

 

• Types of security data (logs, network data, endpoint data)
• Data collection, normalization, and aggregation
• SIEM and data pipelines
• Data quality and integrity considerations

 

Module 3: Foundations of Machine Learning for Security

 

• Supervised vs Unsupervised Learning
• Classification, clustering, and regression
• Feature engineering and model training basics
• Model evaluation and accuracy metrics

 

Module 4: Behavioral Analytics and Anomaly Detection

 

• User and Entity Behavior Analytics (UEBA)
• Detecting insider threats and abnormal patterns
• Baseline behavior modeling
• Anomaly detection techniques

 

Module 5: AI-Driven Threat Detection

 

• Threat intelligence integration
• Malware detection using machine learning
• Phishing detection and classification
• Network traffic analysis using AI

 

Module 6: Security Automation and Orchestration

 

• Introduction to SOAR (Security Orchestration, Automation, and Response)
• Automating incident response workflows
• AI-assisted decision-making in SOC
• Reducing false positives with AI

 

Module 7: Risk Management and AI Security

 

• Risks and limitations of AI in cybersecurity
• Adversarial attacks on AI models
• Ethical considerations in AI usage
• Governance and compliance for AI security

 

Module 8: Implementing AI in Security Operations

 

• Integrating AI tools into existing security frameworks
• Use of AI in cloud security and DevSecOps
• Case studies of AI-driven security implementations
• Performance tuning and optimization

 

Module 9: Incident Response Using AI

 

• AI-assisted threat hunting
• Real-time detection and response strategies
• Incident analysis using analytics platforms
• Post-incident review and improvement

 

Module 10: SecAI+ Certification Exam Preparation

 

• Overview of CompTIA SecAI+ exam domains
• Sample questions and exam techniques
• Hands-on labs and scenario-based exercises
• Final assessment and readiness evaluation