CompTIA SecAI+ (CY0-001) Cert Prep
5h 53mIntermediate2026-01-30
Authors
Mike Chapple
Teaching Professor at the University of Notre Dame
Frederick Nwanganga
Information Technology Professional and Teacher
Course details
Dive into the links between AI and cybersecurity as you prepare for the CompTIA SecAI+ (CY0-001) exam. Mike Chapple—a teaching professor of information technology, analytics, and operations—teaches foundational AI concepts related to cybersecurity, focusing on the history, terminology, and security responsibilities within the AI lifecycle. Delve into domain-specific knowledge, such as securing AI systems against attack vectors like data poisoning and improper model access. Explore AI-assisted security techniques and AI governance, risk, and compliance frameworks. Tackle practical skills, including threat detection, AI-enabled penetration testing, and automating core security tasks. Plus, prepare to earn a certification that validates your capability to protect AI-integrated systems effectively. This course is designed for IT professionals who are familiar with cybersecurity.
Concepts
Introduction
- About the SecAI+ exam
The SecAI+ Exam
- The SecAI+ exam
- Careers in AI security
- The Value of Certification
- Study resources
Inside the SecAI+ Exam
- In-person exam environment
- At-home testing
- SecAI+ question types
- Passing the SecAI+ exam
- Exam tips
Domain 1 - Basic AI Concepts Related to Cybersecurity
- Overview of the Basic AI Concepts Related to Cybersecurity Domain
Types of AI
- Types of AI
- Machine learning
- Deep learning
- Natural language processing
- LLMs vs. SLMs
- Generative AI
Model Training Techniques
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Federated learning
- Model validation
- Model fine-tuning
Prompt Engineering
- Prompt engineering
- Prompt roles
- Zero-shot prompting
- One-shot prompting
- Multi-shot prompting
Data Processing
- Data types
- Data cleansing
- Data verification
- Data integrity
- Data lineage and provenance
- Data augmentation
- Data balancing
- Watermarking
Retrieval-Augmented Generation (RAG)
- Retrieval-Augmented Generation (RAG)
- Securing the knowledge store
- Integrity of retrieved data
- RAG privacy considerations
Security in the AI Lifecycle
- The AI Lifecycle
- Business alignment in the AI lifecycle
- Data collection
- Data Preparation
- Model development and selection
- Model evaluation and validation
- Model deployment and integration
- Monitoring and Maintenance
Human-Centric AI Design
- Human-Centric AI Design
- Human-in-the-loop
- Human Oversight
- Human validation
Domain 2 - Securing AI Systems
- Overview of the Securing AI Systems domain
Data and Training Attacks
- Data poisoning
- Model poisoning
- Introducing biases
- Transfer learning attacks
- Model skewing
- Backdoor and trojan attacks
Prompt and Input Manipulation Attacks
- Circumventing guardrails and jailbreaking
- Input manipulation
- Prompt injection
Model Extraction and Information Leakage Attacks
- Model inversion
- Membership Inference
- Model Theft
- Sensitive Information Disclosure
Integration and Operational Attacks
- Manipulating application integrations
- AI Supply Chain Attacks
- Insecure plug-in design
- Insecure output handling
- Output integrity attacks
- Model denial of service
- Excessive agency
- Overreliance
- AI hallucinations
AI Security Controls
- Model risk assessment
- Model Guardrails
- Prompt templates
- Guardrail Testing and Validation
AI Access Controls
- Prompt firewalls
- Limits and quotas
- Model access controls
- Data Access Controls
- Agent access controls
- Network and API Access Controls
Encryption and Data Safety
- Data encryption
- Data classification labeling
- Data minimization
- Data redaction
- Data masking
- Data anonymization
AI Monitoring and Auditing
- Monitoring prompts and responses
- Log monitoring
- Rate and Cost Monitoring
- Auditing for AI hallucinations
- Auditing for accuracy
- Auditing for Bias and Fairness
- Auditing access and security compliance
Domain 3 - AI-Assisted Security
- Overview of the AI-assisted Security Ddmain
AI-Assisted Security Tools
- AI in security tools
- IDE plug-ins
- Browser plug-ins
- CLI plug-ins
- Chatbots and Personal Assistants
- MCP Servers
AI Security Use Cases
- Threat detection and prevention
- Secure code development
- Penetration testing
- Incident response and management
- Language-driven security operations
AI-Enabled Attacks
- Deepfake content
- Adversarial networks
- Reconnaissance
- Social engineering
- Obfuscation
- Automated data correlation
- Automated attack generation
Automating Security Tasks
- Scripting tools
- Document synthesis and summarization
- Incident response ticket management
- Change management
- AI agents
- AI in the CI CD pipeline
Domain 4 - AI Governance, Risk, and Compliance
- Overview of the AI Governance, Risk, and Compliance domain
AI Governance
- Governing AI
- Organizing for AI
- AI policies and procedures
- AI-Related Roles
AI Risks
- Responsible AI
- AI risks
- Introduction of bias
- Accidental data leakage
- Reputational loss
- Accuracy and performance of the model
- Intellectual property risks
- Autonomous systems
- Shadow IT and shadow AI
- Awareness training
AI Compliance
- AI compliance
- EU AI Act
- OECD standards
- ISO AI Standards
- NIST AI Risk Management Framework
- Corporate AI policies
- Third-party compliance evaluations
- Data Sovereignty
What's Next
- Preparing for the exam