CompTIA SecAI+ Exam Prep: Secure and Defend Real AI Systems!

This course contains the use of artificial intelligence.

CompTIA SecAI+ is a new certification focused squarely on the intersection of artificial intelligence and cybersecurity: securing AI systems, understanding the unique threats AI introduces, governing AI responsibly, and using AI to strengthen defensive security operations. This course is a complete, structured exam-prep pathway covering every major domain a SecAI+ candidate needs, built for security professionals who want both certification readiness and real, applicable skills.

Across seven comprehensive parts, you will start with the foundations of AI security and key terminology, then move into the full landscape of threats and attacks against AI systems – prompt injection, data poisoning, adversarial examples, model extraction, and backdoor attacks – explained with real mechanisms, not just definitions. You will learn how to secure the AI development lifecycle end to end, from data handling and model training through deployment and monitoring, and how to apply governance, risk, and compliance frameworks including the NIST AI RMF and the EU AI Act to real organizational decisions.

The later parts of the course turn to defense: how security teams use AI for threat detection, anomaly detection, and automated incident response, and how to run AI incident response effectively when something does go wrong. The course closes with a dedicated exam-readiness part – study strategy, common exam pitfalls, and applying SecAI+ knowledge to your actual day-to-day role – so you finish not just informed, but genuinely ready for the exam and the job.

This course contains the use of artificial intelligence.

Curriculum Outline

Part 1: Foundations and Key AI Security Concepts

• What Is SecAI+? Understanding the Exam and Why It Matters
• Core AI and Machine Learning Concepts Every Security Professional Needs
• The Unique Security Challenges AI Systems Introduce
• Key Terminology and the SecAI+ Exam Domain Structure

Part 2: Threats and Attacks Against AI Systems

• Prompt Injection and Adversarial Manipulation of AI Systems
• Data Poisoning and Training-Data Integrity Attacks
• Adversarial Examples, Model Extraction, and Backdoor Attacks

Part 3: Securing the AI Development Lifecycle

• Securing Data Collection, Labeling, and Training Pipelines
• Model Hardening, Access Control, and Secure Deployment
• Monitoring, Logging, and Ongoing Model Risk Management

Part 4: AI Governance, Risk, and Compliance

• AI Governance Frameworks: NIST AI RMF and Organizational Oversight
• Global AI Regulation: the EU AI Act and Emerging Compliance Requirements
• Building an AI Risk and Compliance Program in Practice

Part 5: Using AI for Defensive Security Operations

• AI-Powered Threat Detection and Security Anomaly Analysis
• AI-Driven Security Operations Center (SOC) Workflows
• Practical Limits and Pitfalls of AI in Defensive Security

Part 6: AI Incident Response and Resilience

• Preparing for and Detecting AI-Related Security Incidents
• Responding to and Containing AI System Compromises
• Post-Incident Review and Strengthening AI Defenses

Part 7: Exam Readiness and Real-World Application

• SecAI+ Exam Domains and Strategic Study Methods
• Common Exam Pitfalls and How to Avoid Them
• Applying SecAI+ Knowledge to Your Real Job Role

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