Essential artificial intelligence definitions, skills, and guidance for ethical generative AI use in this 6 page laminated reference perfect for CompTIA® AI competency certification. Not only for exam prep, this reference is a perfect quick and handy desktop tool written by author George Antoniou, PhD. A professor with 30 years of experience in information security, risk management, and quality assurance compliance as well as strategic planning and enterprise level security architecture, plus security operations and policy development in small-medium-large, national and multi-national complex information security technology infrastructures. Curating the essential content with his experience and providing succinct answers with examples formatted for easy access makes this a valuable tool. We guarantee you will find answers faster than any book or website for a price that makes it easy to add this to your toolbox. Based on studies regarding retention with screens verses tactile in-hand study tools, along with the growing sales of our computer titles, print is not dead – give it a try.
Definitions & Explanations with Examples within each Domain
Domain 1: Foundations
Core vocabulary—how models learn, evaluating them with sound metrics, and avoiding pitfalls like overfitting
Essential data and optimization practices to interpret model behavior in general and security contexts
Domain 2: Generative AI Frontiers
Generative AI systems; LLMs, diffusion models, GANs, and multimodal systems and the parameters and techniques that shape their outputs
Security and trust concerns; fine-tuning approaches and responsible alignment practices
Insight into how GenAI is built, its risks and frontiers
Domain 3: Prompt Engineering & Security
Prompt engineering (designing inputs to guide GenAI systems)
Prompting strategies and techniques for chaining prompts and managing context windows
Security issues and mitigation approaches
Controlling outputs and understanding vulnerabilities to engineer safer and more effective AI interactions
Domain 4: AI-Enhanced SIEM
How AI enhances Security and Information Event Management (SIEM) systems
Foundations of log management, data normalization, and event correlation; AI-driven anomaly detection, UEBA, and risk scoring to detect threats more effectively
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
Domain 5: AI in IAM
How AI enhances Identity and Access Management (IAM) systems
AI-driven methods (adaptive and behavioral) for authentication and advanced access controls
How AI improves fraud detection, insider threat monitoring, identity proofing, and continuous authentication
Governance and compliance issues to ensure IAM systems remain secure, transparent, and aligned with regulatory standards
Domain 6: Securing AI Systems & Models
Security of AI models and systems across their lifecycle
Adversarial threats and defensive strategies
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
How to secure AI systems from both technical and governance perspectives
