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CompTIA CY0-001 Exam Syllabus Topics:

Section	Weight	Objectives
AI Governance, Risk and Compliance	19%	- Compliance and legal requirements 1. Transparency, accountability and auditability 2. Data protection and privacy laws - Risk management for AI 1. Risk mitigation and control strategies 2. AI risk identification and assessment - Governance frameworks and policies 1. Global standards: NIST AI RMF, EU AI Act 2. Responsible AI principles and ethics 3. Organizational AI governance structures
AI-assisted Security	24%	- Security automation and orchestration 1. Vulnerability management and assessment 2. Workflow automation and response playbooks - AI in security strategy and operations 1. Threat modeling and risk assessment 2. Compliance monitoring and auditing - AI for threat detection and response 1. Automated incident triage and correlation 2. Accelerated threat hunting 3. Anomaly detection and behavioral analysis
Securing AI Systems	40%	- Security controls for AI systems 1. Deployment environment security 2. Data protection: integrity, confidentiality, privacy 3. Model security: access, integrity, anti-tampering - Secure AI development and operations 1. DevSecOps integration for AI 2. Secure MLOps and AI pipeline design - Defending against AI-specific attacks 1. Threat modeling for AI lifecycles 2. Prompt injection, data poisoning, model inversion 3. Adversarial example defense
Basic AI Concepts Related to Cybersecurity	17%	- Core AI principles and terminology 1. Generative AI concepts and capabilities 2. Machine learning, deep learning, NLP, automation - AI applications in security 1. Security automation and decision support 2. Threat detection and anomaly analysis - AI-driven threats and risks 1. Automated phishing, polymorphic malware 2. Malicious use of generative AI 3. Adversarial machine learning attacks

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CompTIA SecAI+ Certification Exam Sample Questions (Q160-Q165):

NEW QUESTION # 160
An organization is developing and implementing AI features into a customer service application.
Which of the following practices should the organization put in place before releasing the application for customer trials?

A. Data masking and sanitization
B. Approved AI vendor lists
C. External compliance audits
D. Third-party risk management

Answer: A

Explanation:
Basic Concept: Before deploying AI applications that handle customer data in trials, protecting sensitive information through data masking and sanitization is essential. CompTIA SecAI+ Study Guide emphasizes pre-deployment data security controls as a critical step in the AI development lifecycle.
Why A is Correct: Data masking replaces sensitive real customer data with realistic but fictitious equivalents, while sanitization removes harmful or unwanted data elements. Before customer trials, these techniques prevent exposure of real PII or sensitive information, ensure the trial environment cannot leak production data, and protect the organization from privacy regulation violations. This is the most immediately actionable pre-trial security control.
Why B is Wrong: External compliance audits are formal processes typically conducted post-deployment or at planned intervals to verify regulatory compliance. They are not pre-trial security implementations and cannot prevent data exposure in a trial environment.
Why C is Wrong: Approved AI vendor lists are governance artifacts that manage vendor selection risk at the procurement stage. They do not directly protect customer data within an application being prepared for trials.
Why D is Wrong: Third-party risk management addresses risks from external vendors and partners at a strategic level. While important for overall governance, it does not constitute a direct data security control for a pre-trial release.

NEW QUESTION # 161
A security alert triggers an agentic system. An analyst notices the following payload in the logs. The alert includes multiple shell commands that are not typically run as part of any hardening:

Which of the following is the most effective control to implement?

A. Using only approved libraries when interacting with agentic systems
B. Deprecating model usage and retaining the model with safer parameters
C. Modifying the application to ignore the SECURITY_UPDATE tag
D. Adding logic that includes approved strings before running the shell commands

Answer: D

Explanation:
Basic Concept: Agentic AI systems that execute shell commands based on model-generated output are vulnerable to prompt injection attacks where malicious actors craft inputs that cause the agent to run unauthorized commands. Input validation using allowlists is a critical defense mechanism. CompTIA SecAI+ Study Guide covers agentic AI security controls.
Why A is Correct: Adding logic that validates shell commands against an approved allowlist before execution is the most direct and effective defense. This ensures only pre-approved, safe commands can be executed regardless of what the agentic system ' s model generates, preventing malicious command injection from reaching the operating system. This principle of allowlist-based input validation is a foundational secure agentic AI control.
Why B is Wrong: Deprecating and retraining the model is a lengthy process that addresses root cause training issues but does not provide immediate protection against ongoing injection attacks in the current deployed system.
Why C is Wrong: Modifying the application to ignore a specific tag merely removes one attack surface while leaving the system vulnerable to other injection vectors. It is not a comprehensive defense.
Why D is Wrong: Using only approved libraries controls which code libraries the agentic system can call, but does not validate or restrict the shell commands generated by the model at runtime based on arbitrary user input.

NEW QUESTION # 162
An organization wants to reduce vulnerabilities after deployment. The organization decides to incorporate an AI-assisted early detection and vulnerability identification process in its development workflow.
Which of the following AI-assisted functions is the best option?

A. Incident management
B. System auditing
C. Code linting
D. Automated deployment/rollback

Answer: C

Explanation:
Basic Concept: Reducing post-deployment vulnerabilities requires catching security issues as early as possible in the development workflow. AI-assisted tools that analyze code during development provide the earliest possible intervention point. CompTIA SecAI+ Study Guide covers AI integration in secure development under AI-assisted security.
Why A is Correct: AI-assisted code linting analyzes source code in real time during development to identify security vulnerabilities, insecure coding patterns, policy violations, and quality issues before code is compiled or committed. By catching vulnerabilities at the coding stage - the earliest possible point in the development workflow - AI code linting prevents vulnerable code from progressing to testing, staging, or production, directly reducing post-deployment vulnerabilities at their source.
Why B is Wrong: Incident management handles security events and incidents after they have occurred in production. It is a reactive capability focused on response and recovery rather than early-stage vulnerability identification in the development workflow.
Why C is Wrong: Automated deployment/rollback automates the process of pushing code to production and reverting to previous versions when issues are detected post-deployment. It is a deployment safety mechanism rather than an early detection tool during the development phase.
Why D is Wrong: System auditing reviews and records system activities and configurations for compliance verification. It is primarily a detective and compliance control for systems that are already deployed, not an early development-phase vulnerability identification tool.

NEW QUESTION # 163
Which of the following should an auditor reference when reviewing a company ' s human resources AI systems for legal non-compliance?

A. European Union (EU) AI Act
B. International Organization for Standardization (ISO)
C. National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF)
D. Organization for Economic Cooperation and Development (OECD) standard

Answer: A

Explanation:
Basic Concept: Various regulatory frameworks govern AI use in different contexts. For auditing legal compliance in high-risk AI applications such as employment and HR, binding regulatory legislation takes precedence over voluntary standards. CompTIA SecAI+ Exam Objectives cover AI governance and compliance frameworks under Domain 4.
Why C is Correct: The EU AI Act is the world ' s first comprehensive, legally binding AI regulation. It explicitly classifies AI systems used in employment, worker management, and recruitment as high-risk AI systems, subjecting them to strict compliance requirements including conformity assessments, transparency obligations, and human oversight mandates. An auditor reviewing HR AI for legal non-compliance must reference this binding legislation.
Why A is Wrong: The OECD AI Principles are non-binding international guidelines promoting responsible AI. They offer policy guidance but carry no legal enforcement power for compliance auditing.
Why B is Wrong: The NIST AI RMF is a voluntary, risk management-focused framework. It is not a legal compliance standard and cannot be used to assess legal non-compliance.
Why D is Wrong: ISO standards such as ISO 42001 are voluntary international best practice standards. They are not legal compliance instruments with enforceable penalties for HR AI systems.

NEW QUESTION # 164
A security consultant must summarize the impact of posture management on a machine learning (ML) use case. Which of the following is the most appropriate reference for this purpose?

A. National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF)
B. European Union AI Act
C. Generative adversarial network (GAN)
D. Organization for Economic Co-operation and Development (OECD) standards

Answer: A

Explanation:
The NIST AI RMF provides structured guidance for assessing and managing risks across the AI lifecycle, including posture management. It helps organizations align AI security practices with governance, resilience, and trustworthiness requirements.

NEW QUESTION # 165
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