CompTIA

CompTIA SecAI+

Prepare for the CompTIA SecAI+ (CY0-001) exam with hands-on labs and real attack scenarios. Learn to secure AI systems, detect threats, and build job-ready skills.

(SECAI-001.AA1) / ISBN : 979-8-90059-107-0
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About This Course

This CompTIA SecAI+ (CY0-001) course gives you a clear path to learn AI security step by step. You work on real attack scenarios, analyze threats, and practice defense techniques used in the field. With hands-on labs and exam-focused questions, you stay focused on what matters for certification and your career. Build AI security skills you can use from day one.

Skills You’ll Get

  • Identify and analyze AI security threats and vulnerabilities
  •  Detect and prevent adversarial attacks and prompt injection
  •  Secure AI models and data pipelines
  •  Apply AI risk management and governance practices
  •  Monitor AI systems for threats and anomalies
  •  Perform incident response for AI-related attacks
  •  Understand AI security frameworks and best practices
Download Course Outline

1

Preface

  • 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 
  • 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

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

1

The Convergence of Artificial Intelligence and Cybersecurity

  • Interaction with a Pre-Trained Model
  • Running Local Inference with Ollama
2

Data Science and Feature Engineering for Security

  • Implementing Cryptographic Data Provenance
  • Architecting and Inspecting a Convolutional Neural Network (CNN)
  • Transforming Logs into Numeric Features
  • Performing a Dataset Poisoning Attack
  • Configuring a Secure RAG Vector Store
  • Implementing LBAC Metadata Tagging in a Secure RAG Pipeline
3

Threat Modeling and Vulnerability Frameworks for AI

4

Attack Vectors and Adversarial Engineering

  • Executing an FGSM Attack
  • Executing a Black-Box Attack Using the HopSkipJump Method
  • Injecting a Backdoor into an ML Model
  • Simulating a Membership Inference Attack
  • Experimenting with a Prompt Injection Attack
5

Security Engineering for AI Systems

  • Building a Semantic Guardrail
  • Training a Neural Network with DP
  • Building a Drift Detector
  • Implementing Adversarial Training
6

Governance, Risk, and Compliance for AI

  • Explaining a Model with SHAP
  • Mitigating Bias Using Fairlearn
  • Simulating an Active Learning Loop
7

AI Application Security and Agent Architectures

  • Implementing a Secure RAG Retrieval Process
  • Exploring Zero-Shot, One-Shot, and Few-Shot Prompting
8

Synthetic Media, Deepfakes, and Multimedia Security

  • Visualizing the Forward Diffusion Process
9

Future Trends and Emerging AI Threats

  • Building a Quantum-Inspired Classifier
10

End-to-End Secure AI Implementation

  • Executing a Red Team Campaign
11

AI Security Operations and Incident Response

  • Building a Low-Code SOAR Automation Playbook
  • Capturing Forensic Snapshots of AI Incidents

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CompTIA SecAI+ certification training teaches you how to secure AI systems from real-world threats. As companies adopt AI, the demand for AI security skills is growing fast. This certification helps you build those skills and stay relevant in cybersecurity.

This course is ideal for cybersecurity professionals, SOC analysts, AI/ML engineers, and beginners who want to learn AI security. If you plan to pass the CompTIA SecAI+ exam or move into AI security roles, this course fits.

This course follows the official exam objectives and includes hands-on labs, practice questions, and real scenarios. You learn concepts and apply them, which helps you prepare faster and perform better in the exam.

Yes. You get 23 hands-on labs and 180+ practice questions. These help you practice real AI security tasks and test your exam readiness.

You learn AI threat detection, adversarial attack prevention, prompt injection defense, AI risk management, and model security. These skills are used in real AI security roles.

Yes. You learn AI governance, risk management, bias, and compliance. These topics are important for securing AI systems in real-world environments.

You learn how to handle adversarial attacks, data poisoning, model vulnerabilities, and AI system risks. The course focuses on practical defense and real-world scenarios.

You can build job-ready AI security skills in a few weeks with consistent practice. The course focuses on real tasks used in AI security roles.

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