CompTIA SecAI+ (SecAI Plus) Exam Syllabus

SecAI+ PDF, CY0-001 Dumps, CY0-001 PDF, SecAI+ VCE, CY0-001 Questions PDF, CompTIA CY0-001 VCE, CompTIA SecAI Plus Dumps, CompTIA SecAI Plus PDF Use this quick start guide to collect all the information about CompTIA SecAI+ (CY0-001) Certification exam. This study guide provides a list of objectives and resources that will help you prepare for items on the CY0-001 CompTIA SecAI+ exam. The Sample Questions will help you identify the type and difficulty level of the questions and the Practice Exams will make you familiar with the format and environment of an exam. You should refer this guide carefully before attempting your actual CompTIA SecAI Plus certification exam.

The CompTIA SecAI+ certification is mainly targeted to those candidates who want to build their career in AI domain. The CompTIA SecAI+ exam verifies that the candidate possesses the fundamental knowledge and proven skills in the area of CompTIA SecAI Plus.

CompTIA SecAI+ Exam Summary:

Exam Name	CompTIA SecAI+
Exam Code	CY0-001
Exam Price	$298 (USD)
Duration	60 mins
Number of Questions	60
Passing Score	600 (on a scale of 100-900)
Schedule Exam	Pearson VUE
Sample Questions	CompTIA SecAI+ Sample Questions
Practice Exam	CompTIA CY0-001 Certification Practice Exam

CompTIA CY0-001 Exam Syllabus Topics:

Topic	Details
Basic AI Concepts Related to Cybersecurity - 17%
Compare and contrast various AI types and techniques used in cybersecurity.	- Types of AI Generative AI Machine learning Statistical learning Transformers Deep learning Generative adversarial networks (GANs) Natural language processing (NLP) Large language models (LLMs) Small language models (SLMs) - Model training techniques Model validation Supervised learning Unsupervised learning Reinforcement learning Federated learning Fine-tuning Epoch Pruning Quantization - Prompt engineering System prompts User prompts One-shot prompting Multi-shot prompting Zero-shot prompting System roles Templates
Explain the importance of data security in relation to AI.	- Data processing Data cleansing Data verification Data lineage Data integrity Data provenance Data augmentation Data balancing - Data types Structured data Semi-structured data Unstructured data - Watermarking - Retrieval-augmented generation (RAG) Vector storage Embeddings
Explain the importance of security throughout the life cycle of AI.	- Business use case Alignment with corporate objectives - Data collection Trustworthiness Authenticity - Data preparation - Model development/selection - Model evaluation - Deployment - Validation - Monitoring and maintenance - Feedback and iteration - Human-centric AI design principles Human-in-the-loop Human oversight Human validation
Securing AI Systems - 40%
Given a scenario, use AI threat-modeling resources.	- Open Worldwide Application Security Project (OWASP) Top 10 LLM Top 10 Machine Learning (ML) Security Top 10 - Massachusetts Institute of Technology (MIT) AI Risk Repository - MITRE Adversarial Threat Landscape for Artificial-Intelligence Systems (ATLAS) - Common Vulnerabilities and Exposures (CVE) AI Working Group - Threat-modeling frameworks
Given a set of requirements, implement security controls for AI systems.	- Model controls Model evaluation Model guardrails Prompt templates - Gateway controls Prompt firewalls Rate limits Token limits Input quotas Data size Quantity Modality limits Endpoint access controls - Guardrail testing and validation
Given a scenario, implement appropriate access controls for AI systems.	- Model access - Data access - Agent access - Network/application programming interface (API) access
Given a scenario, implement data security controls for AI systems.	- Encryption requirements In transit At rest In use - Data safety Data anonymization Data classification labels Data redaction Data masking Data minimization
Given a scenario, implement monitoring and auditing for AI systems.	- Prompt monitoring Query Response - Log monitoring - Log sanitization - Log protection - Response confidence level - Rate monitoring - AI cost monitoring Prompts Storage Response Processing - Auditing for quality and compliance Hallucinations Accuracy Bias and fairness Access
Given a scenario, analyze the evidence of an attack and suggest compensating controls for AI systems.	- Attacks Backdoor attacks Trojan attacks Prompt injection Poisoning Model poisoning Data poisoning Jailbreaking Input manipulation Introducing biases Circumventing AI guardrails Manipulating application integrations Model inversion Model theft AI supply chain attacks Transfer learning attacks Model skewing Output integrity attacks Membership inference Insecure output handling Model denial of service (DoS) Sensitive information disclosure Insecure plug-in design Excessive agency Overreliance - Compensating controls Prompt firewalls Model guardrails Access controls Data integrity controls Encryption Prompt templates Rate limiting Least privilege
AI-assisted Security - 24%
Given a scenario, use AI-enabled tools to facilitate security tasks.	- Tools/applications Integrated development environment (IDE) plug-ins Browser plug-ins Command-line interface (CLI) plug-ins Chatbots Personal assistants Model Context Protocol (MCP) server - Use cases Signature matching Code quality and linting Vulnerability analysis Automated penetration testing Anomaly detection Pattern recognition Incident management Threat modeling Fraud detection Translation Summarization
Explain how AI enables or enhances attack vectors.	- AI-generated content (deepfake) Impersonation Misinformation Disinformation - Adversarial networks - Reconnaissance - Social engineering - Obfuscation - Automated data correlation Automated attack generation Attack vector discovery Payloads Malware Honeypot Distributed denial of service (DDoS)
Given a scenario, use AI to automate security tasks.	- Scripting tools Low-code No-code - Document synthesis and summarization - Incident response ticket management - Change management AI-assisted approvals Automated deployment/rollback - AI agents - Continuous integration and continuous deployment (CI/CD) Code scanning Software composition analysis Unit testing Regression testing Model testing Automated deployment/rollback
AI Governance, Risk, and Compliance - 19%
Explain organizational governance structures that support AI.	- Organizational structures AI Center of Excellence AI policies and procedures - AI-related roles Data scientist AI architect Machine learning engineer Platform engineer MLOps engineer AI security architect AI governance engineer AI risk analyst AI auditor Data engineer
Explain risks associated with AI.	- Responsible AI Fairness Reliability and safety Transparency Privacy and security Differential privacy Explainability Inclusiveness Accountability Consistency Awareness training - Risks Introduction of bias Accidental data leakage Reputational loss Accuracy and performance of the model Intellectual Property (IP)-related risks Autonomous systems - Shadow IT Shadow AI
Summarize the impact of compliance on business use and development of AI.	- European Union (EU) AI Act - Organisation for Economic Co-operation and Development (OECD) standards - International Organization for Standardization (ISO) AI standards - National Institute of Standards and Technology (NIST) AI Risk Management Framework (AIRMF) - Corporate policies Sanctioned vs. unsanctioned Private vs. public models Sensitive data governance - Third-party compliance evaluations - Data sovereignty

To ensure success in CompTIA SecAI Plus certification exam, we recommend authorized training course, practice test and hands-on experience to prepare for CompTIA SecAI+ (CY0-001) exam.

CompTIA SecAI+ (SecAI Plus) Exam Syllabus

CompTIA SecAI+ Exam Summary:

CompTIA CY0-001 Exam Syllabus Topics:

Basic AI Concepts Related to Cybersecurity - 17%

Securing AI Systems - 40%

AI-assisted Security - 24%

AI Governance, Risk, and Compliance - 19%