CompTIA SecAI+ Training in Global

CompTIA SecAI+ is a comprehensive three-day instructor-led course designed for IT and Cyber Security Professionals who want to develop expertise in Artificial Intelligence Security (AI Security). As a vendor-neutral certification, it helps organizations address emerging security challenges associated with AI adoption while maximizing the benefits of AI technologies.

Participants gain practical knowledge of Machine Learning, Deep Learning, Generative AI, and modern security methodologies. The course focuses on protecting AI Models, data pipelines, and deployment environments while leveraging AI-driven technologies to improve Threat Detection, Incident Response, and overall Security Operations.

• 3-4 years of IT experience
• Approximately 2 years of hands-on Cyber Security experience
• CompTIA Security+ or equivalent knowledge recommended

This course is ideal for:

• Cyber Security Analysts
• Security Engineers
• Security Operations Center (SOC) Professionals
• Threat Intelligence Analysts
• DevSecOps Engineers
• Software Developers
• Security Architects
• IT professionals working with AI Systems

After completing this course, participants will be able to:

• Apply Artificial Intelligence concepts within cyber security environments.
• Implement effective AI Security Controls.
• Identify and mitigate AI Threats and vulnerabilities.
• Use AI-powered solutions for Threat Detection and response.
• Securely integrate AI into DevSecOps and enterprise architectures.
• Apply Risk Management, Compliance, and AI Governance frameworks.

Module 1: AI and Data Foundations for Cyber Security

• Machine Learning and Deep Learning concepts
• Generative AI technologies
• Transformer architectures and Natural Language Processing (NLP)
• AI model training methodologies
• Prompt Engineering techniques
• Data integrity and protection controls
• Retrieval-Augmented Generation (RAG) fundamentals

Module 2: Threat Modelling and Securing AI Systems

• Threat Modelling frameworks
• AI attack surfaces and vulnerabilities
• Implementation of AI Security Controls
• Secure configuration and guardrails
• Validation and testing of security measures

Module 3: Access Control for AI Systems

• Access Control models
• API and network security
• Data Protection and encryption strategies
• Monitoring and logging practices
• Compliance and auditing procedures

Module 4: AI Threats and Defensive Controls

• Adversarial AI attacks
• Model Poisoning techniques
• Model theft and inversion threats
• Ethical AI development principles
• Mitigation and compensating controls
• Post-incident investigation and analysis

Module 5: Leveraging AI in Security Operations

• AI-powered Threat Detection
• Security analytics and assessment
• AI-driven vulnerability management
• Security automation workflows
• Incident Response optimization
• DevSecOps integration practices

Module 6: AI Governance, Risk Management and Compliance

• AI Governance frameworks
• Organizational roles and responsibilities
• Responsible AI principles
• Risk Management methodologies
• Regulatory and Compliance requirements
• Policy development and reporting mechanisms