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CompTIA CY0-001 Exam Syllabus Topics:

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

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CompTIA SecAI+ Certification Exam Sample Questions (Q86-Q91):

NEW QUESTION # 86
A cybersecurity analyst wants to choose a machine learning (ML) model to classify log entries while providing the best explainability. Which of the following models should the analyst use?

A. Generative adversarial network (GAN)
B. Neural networks
C. Large language model (LLM)
D. Decision trees

Answer: D

Explanation:
Decision trees provide clear and interpretable decision paths, making them highly explainable compared to complex models like neural networks or GANs. This makes them the best choice for classifying log entries when explainability is a priority.

NEW QUESTION # 87
For which of the following situations would a human-in-the-loop be most recommended?

A. A chatbot that provides answers based on frequently asked questions
B. A large language model (LLM) that summarizes large blocks of text
C. Agentic AI that automates system patching for an organization
D. A generative model that creates graphics for websites

Answer: C

Explanation:
Agentic AI that can automatically patch organizational systems represents the highest-impact and highest-risk scenario, making human-in-the-loop control the most appropriate. CompTIA SecAI+ identifies human-in-the- loop, human oversight, and human validation as human-centric AI design principles. Agentic systems differ from passive AI because they can take actions through tools, APIs, scripts, or other integrated systems.
Automated patching can modify production hosts, restart services, introduce incompatibilities, cause outages, or affect business-critical applications. Requiring human authorization before consequential actions provides an important control point for validating the recommendation, affected assets, timing, dependencies, and rollback plan. A summarization model normally produces content for review without directly modifying systems. A graphics-generation model likewise creates content rather than performing privileged infrastructure changes. An FAQ chatbot is generally constrained to answering questions and typically has limited operational impact. Human oversight can still be valuable in all three situations, but the potential operational consequences are considerably greater when autonomous AI can change production systems.
Therefore, agentic AI performing patching is the scenario where human-in-the-loop governance is most strongly warranted.

NEW QUESTION # 88
A security architect performs threat modeling of an AI system. The architect needs to determine which attacks can be performed against the system.
Which of the following actions should the architect take next?

A. Identify trust boundaries and perform threat modeling with Open Worldwide Application Security Project (OWASP) Top 10.
B. Analyze MITRE Adversarial Threat Landscape for AI Systems (ATLAS) for tactics, techniques, and procedures (TTPs).
C. Leverage a large language model (LLM) to map likely attack paths based on the code base.
D. Quantify the risk of known vulnerabilities identified in the AI system.

Answer: B

Explanation:
Basic Concept: AI-specific threat modeling requires consulting resources that catalogue adversarial attacks specifically developed for AI and ML systems. General cybersecurity frameworks may miss AI-unique attack vectors such as model inversion, data poisoning, and adversarial examples. CompTIA SecAI+ Study Guide identifies MITRE ATLAS as the authoritative source for AI system TTPs.
Why D is Correct: MITRE ATLAS provides a comprehensive, curated knowledge base of adversarial tactics, techniques, and procedures specifically targeting AI and ML systems, derived from real-world attack case studies. Analyzing ATLAS enables the architect to enumerate realistic AI-specific attacks applicable to the system being threat-modeled, which directly answers the question of which attacks can be performed.
Why A is Wrong: Using an LLM to map attack paths introduces uncertainty and potential hallucination risk.
LLMs may generate plausible-sounding but inaccurate attack paths and cannot guarantee comprehensive coverage of AI-specific attack techniques.
Why B is Wrong: Quantifying risk of known vulnerabilities is a risk assessment step that occurs after identifying which attacks are possible. The architect must first identify attack possibilities before quantifying their risk impact.
Why C is Wrong: OWASP Top 10 covers web application vulnerabilities and, in its LLM edition, certain LLM-specific risks. However, MITRE ATLAS provides a more comprehensive and structured catalog of AI and ML-specific adversarial TTPs for systematic threat modeling.

NEW QUESTION # 89
Which of the following requires developers to harden infrastructure to protect AI systems?

A. Development guidelines
B. Configuration standards
C. Acceptable use policies
D. Intake processes

Answer: B

Explanation:
Basic Concept: Infrastructure hardening for AI systems involves applying security baseline settings and eliminating unnecessary attack surfaces. CompTIA SecAI+ Exam Objectives identify configuration standards as the specific governance instrument that mandates infrastructure hardening requirements for AI deployments.
Why D is Correct: Configuration standards are formal, technical documents specifying exact security settings, baseline configurations, and hardening requirements that developers and administrators must implement to protect systems including AI infrastructure. They establish enforceable rules such as disabling unnecessary services, applying least-privilege access, and enforcing secure communication protocols specifically for AI systems.
Why A is Wrong: Intake processes govern how new projects, systems, or requests are evaluated and onboarded into an organization. They are procedural checkpoints for initial assessment, not technical hardening directives for developers.
Why B is Wrong: Acceptable use policies define appropriate ways employees and users may use organizational systems and AI tools. They are behavioral guidelines aimed at end users, not technical requirements instructing developers to secure infrastructure.
Why C is Wrong: Development guidelines provide best practices and recommendations for software development and may include security considerations. However, they are advisory in nature and broader in scope than the specific mandatory infrastructure-hardening requirements found in configuration standards.

NEW QUESTION # 90
Which of the following International Organization for Standardization (ISO) standards should be selected for certification to use for third-party assurance for responsible AI practices?

A. 0
B. 1
C. 2
D. 3

Answer: B

Explanation:
Basic Concept: ISO develops international standards for management systems across various domains. For organizations seeking third-party certification demonstrating commitment to responsible AI governance practices, the appropriate ISO standard must specifically address AI management systems. CompTIA SecAI+ Exam Objectives cover ISO standards relevant to AI governance under Domain 4.
Why D is Correct: ISO 42001 is the International Standard for Artificial Intelligence Management Systems (AIMS). It provides a framework for establishing, implementing, maintaining, and continually improving an AI management system within organizations. ISO 42001 certification provides third-party assurance specifically for responsible AI practices including risk management, transparency, human oversight, and ethical AI governance - directly answering the question.
Why A is Wrong: ISO 20000 is the standard for IT Service Management (ITSM). It provides requirements for establishing and maintaining a service management system for IT services. It does not address AI governance or responsible AI practices.
Why B is Wrong: ISO 27001 is the standard for Information Security Management Systems (ISMS). It addresses general information security risk management, not AI-specific governance or responsible AI practices such as fairness, transparency, and AI lifecycle management.
Why C is Wrong: ISO 27701 extends ISO 27001 to address Privacy Information Management (PIMS), covering personal data protection requirements aligned with GDPR. While relevant to data privacy in AI systems, it does not specifically certify responsible AI governance practices.

NEW QUESTION # 91
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