CompTIA - AI Essentials

<ul><br> <li>Definitions & Explanations with Examples within each Domain</li><br> <li>Domain 1: Foundations<br> <ul><br> <li>Core vocabulary—how models learn, evaluating them with sound metrics, and avoiding pitfalls like overfitting</li><br> <li>Essential data and optimization practices to interpret model behavior in general and security contexts</li><br> </ul><br> </li><br> <li>Domain 2: Generative AI Frontiers<br> <ul><br> <li>Generative AI systems; LLMs, diffusion models, GANs, and multimodal systems and the parameters and techniques that shape their outputs</li><br> <li>Security and trust concerns; fine-tuning approaches and responsible alignment practices</li><br> <li>Insight into how GenAI is built, its risks and frontiers</li><br> </ul><br> </li><br> <li>Domain 3: Prompt Engineering & Security<br> <ul><br> <li>Prompt engineering (designing inputs to guide GenAI systems)</li><br> <li>Prompting strategies and techniques for chaining prompts and managing context windows</li><br> <li>Security issues and mitigation approaches</li><br> <li>Controlling outputs and understanding vulnerabilities to engineer safer and more effective AI interactions</li><br> </ul><br> </li><br> <li>Domain 4: AI-Enhanced SIEM<br> <ul><br> <li>How AI enhances Security and Information Event Management (SIEM) systems</li><br> <li>Foundations of log management, data normalization, and event correlation; AI-driven anomaly detection, UEBA, and risk scoring to detect threats more effectively</li><br> <li>How SOAR and playbooks automate response; threat intelligence and ATT&CK mapping enrich context; and explainability, feedback loops, and data privacy concerns ensure SIEM remains trustworthy and effective in mod­ern SOC environments</li><br> </ul><br> </li><br> <li>Domain 5: AI in IAM<br> <ul><br> <li>How AI enhances Identity and Access Management (IAM) systems</li><br> <li>AI-driven methods (adaptive and behavioral) for authentication and advanced access controls</li><br> <li>How AI improves fraud detection, insider threat monitoring, identity proofing, and continuous authen­tication</li><br> <li>Governance and compliance issues to ensure IAM systems remain secure, transparent, and aligned with regulatory standards</li><br> </ul><br> </li><br> <li>Domain 6: Securing AI Systems & Models<br> <ul><br> <li>Security of AI models and systems across their life­cycle</li><br> <li>Adversarial threats and defensive strategies</li><br> <li>Operational practices like secure deployment, drift monitoring, red teaming, and MLOps security; gov­ernance frameworks like MITRE ATLAS, OWASP Top 10 for LLM Applications (LLM Top 10); and responsible disclosure</li><br> <li>How to secure AI systems from both technical and governance perspectives</li><br> </ul><br> </li><br></ul><br>