CompTIA

CompTIA SecAI+

(SECAI-001.AA1)
Lessons
Lab
TestPrep
AI Tutor (Add-on)
Get A Free Trial

Skills You’ll Get

Download Course Outline

1

Preface

  • To the Student: How to Use This Course
  • Prerequisites and Expectations
  • A Note on the Industry
2

The Convergence of Artificial Intelligence and Cybersecurity

  • Core Concepts of Artificial Intelligence
  • The Dual Reality of AI in Security
  • AI Paradigms for Security Professionals
  • Modern AI Architectures and Security Implications
  • The AI Development Lifecycle (Model Development Lifecycle – MDLC)
  • Hands-On Practice: Establishing the AI Security Lab
  • Summary and Exam Essentials
3

Data Science and Feature Engineering for Security

  • Data Security Foundations and the AI Lifecycle
  • Deep Learning Architectures and Component Analysis
  • Data as the New Attack Surface
  • Secure Retrieval-Augmented Generation (RAG) Architectures
  • Building a Secure Data Pipeline
  • Summary and Exam Essentials
4

Threat Modeling and Vulnerability Frameworks for AI

  • The Necessity of Structured Risk Assessment
  • Thinking Like an AI Adversary
  • The OWASP Top 10 for Large Language Models
  • The MITRE ATLAS Framework
  • Applying STRIDE to AI Workflows
  • Conducting an AI Threat Modeling Workshop
  • Summary and Exam Essentials
5

Attack Vectors and Adversarial Engineering

  • Introduction to Adversarial Machine Learning
  • Gradient-Based Evasion Attacks
  • Black-Box Attacks and Oracle Abuse
  • Data Poisoning and Backdoor Attacks
  • Privacy Attacks
  • Generative AI Attacks
  • Advanced Threats: Manipulation, Theft, and Overreliance
  • Adversarial Networks and AI-Enhanced Attacks
  • Summary and Exam Essentials
6

Security Engineering for AI Systems

  • Adversarial Training and Model Hardening
  • Input Guardrails and Sanitization
  • Access Control for AI Systems
  • Secure MLOps
  • Privacy-Preserving Machine Learning (PPML)
  • Watermarking and Detection
  • Continuous Monitoring and AI Observability
  • Prompt Monitoring and Log Protection
  • Summary and Exam Essentials
7

Governance, Risk, and Compliance for AI

  • Introduction to AI Governance and Regulation
  • Explainability and Interpretability (XAI)
  • Fairness, Bias, and Ethics in AI
  • AI Auditing and Documentation Standards
  • The Role of the Human in the Loop (HITL)
  • AI Incident Response and Forensics
  • Summary and Exam Essentials
8

AI Application Security and Agent Architectures

  • Introduction to Agents and RAG Workflows
  • Secure Prompt Engineering and System Prompts
  • Sandboxing and Isolation for AI Agents
  • Identity Management and Authorization for AI Agents
  • Red Teaming and Adversarial Testing for Agents
  • AI Tooling Interfaces Used by Security Teams
  • Secure Deployment Strategies for AI Systems
  • Summary and Exam Essentials
9

Synthetic Media, Deepfakes, and Multimedia Security

  • Foundations of Generative AI: GANs and Diffusion Models
  • Audio Synthesis and Voice Cloning
  • Multimedia Content Provenance and Watermarking
  • Adversarial Attacks on Multimedia Systems
  • Deepfake Detection Technologies and Forensics
  • Ethical and Legal Implications of Synthetic Media
  • Summary and Exam Essentials
10

Future Trends and Emerging AI Threats

  • Introduction to Quantum Computing and AI
  • Quantum Machine Learning and Adversarial Intelligence
  • Autonomous Agents and Swarm Intelligence Security
  • Neuromorphic Computing and Spiking Neural Networks
  • AI Governance and the Future of Work
  • AI in Defense and Kinetic Operations
  • Summary and Exam Essentials
11

Capstone Project: End-to-End Secure AI Implementation

  • Project Scope and Architecture Design
  • Data Pipeline and Vector Database Implementation
  • Model Hardening and Guardrail Integration
  • Red Teaming and Adversarial Simulation
  • Deployment, Monitoring, and Incident Response
  • Personal Assistants in Security Operations
  • System Cards, Documentation, and Executive Reporting
  • Summary and Exam Essentials
12

AI Security Operations and Incident Response

  • Designing the AI Security Operations Center (AISOC)
  • AI Incident Response and Forensics
  • AI Vulnerability Management and Model Remediation
  • Adversarial Machine Learning Defense Strategies
  • AI Supply Chain Security and SBOMs
  • Continuous Security Monitoring and Compliance
  • AI-Related Roles and Accountability in Security Programs
  • Responsible AI as a Security Discipline
  • Summary and Exam Essentials
13

Enterprise AI Strategy and Leadership

  • Developing an AI Security Strategy
  • Regulatory Compliance and Legal Frameworks
  • Ethics, Bias Mitigation, and Fairness Engineering
  • AI Workforce Security and Culture
  • Future-Proofing
  • Third-Party Risk Management (TPRM) and AI Procurement
  • Summary and Exam Essentials

Any questions?
Check out the FAQs

Still have unanswered questions and need to get in touch?

Contact Us Now

Related Courses

All Courses
We use cookies for improving site experience and analytics. Learn More