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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
AI-assisted Security24%- AI for threat detection and response
  • 1. Accelerated threat hunting
  • 2. Automated incident triage and correlation
  • 3. Anomaly detection and behavioral analysis
- AI in security strategy and operations
  • 1. Threat modeling and risk assessment
  • 2. Compliance monitoring and auditing
- Security automation and orchestration
  • 1. Workflow automation and response playbooks
  • 2. Vulnerability management and assessment
Basic AI Concepts Related to Cybersecurity17%- Core AI principles and terminology
  • 1. Generative AI concepts and capabilities
  • 2. Machine learning, deep learning, NLP, automation
- AI-driven threats and risks
  • 1. Automated phishing, polymorphic malware
  • 2. Adversarial machine learning attacks
  • 3. Malicious use of generative AI
- AI applications in security
  • 1. Threat detection and anomaly analysis
  • 2. Security automation and decision support
Securing AI Systems40%- Security controls for AI systems
  • 1. Deployment environment security
  • 2. Model security: access, integrity, anti-tampering
  • 3. Data protection: integrity, confidentiality, privacy
- Secure AI development and operations
  • 1. Secure MLOps and AI pipeline design
  • 2. DevSecOps integration for AI
- Defending against AI-specific attacks
  • 1. Prompt injection, data poisoning, model inversion
  • 2. Threat modeling for AI lifecycles
  • 3. Adversarial example defense
AI Governance, Risk and Compliance19%- Risk management for AI
  • 1. AI risk identification and assessment
  • 2. Risk mitigation and control strategies
- Compliance and legal requirements
  • 1. Transparency, accountability and auditability
  • 2. Data protection and privacy laws
- Governance frameworks and policies
  • 1. Global standards: NIST AI RMF, EU AI Act
  • 2. Organizational AI governance structures
  • 3. Responsible AI principles and ethics

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CompTIA SecAI+ Certification Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
A security operations center (SOC) analyst needs to automate multiple security tasks by breaking them down into smaller parts.
Which of the following AI tools is the best for this task?

Answer: C

Explanation:
Basic Concept: Modern security operations require automation of complex, multi-step workflows. Different AI architectures have different capabilities. Understanding which AI type is best suited for task decomposition and autonomous execution is fundamental to AI-assisted security operations. CompTIA SecAI+ covers agentic AI capabilities under AI-assisted security.
Why A is Correct: Agentic AI systems are specifically designed to autonomously plan, decompose complex tasks into subtasks, execute multi-step workflows, use tools and APIs, and adapt their approach based on intermediate results. For a SOC analyst needing to automate multiple security tasks as a series of smaller coordinated steps, agentic AI is the ideal architecture as it can orchestrate an entire workflow including threat hunting, alert investigation, log analysis, and response actions.
Why B is Wrong: RAG AI enhances language model responses by retrieving relevant documents from a knowledge base. While useful for answering questions with current information, it is not designed for autonomous multi-step task execution or workflow automation.
Why C is Wrong: Generative AI creates content based on prompts including text, code, and summaries. While it can assist with individual tasks, it requires continuous human prompting for each step rather than autonomously breaking down and executing complex multi-step security workflows.
Why D is Wrong: A chatbot is a conversational interface designed for question-answering or guided dialogue.
It responds reactively to user input rather than proactively planning and executing multi-step automated security workflows.


NEW QUESTION # 11
A line of business wants to onboard an application that uses a custom AI model for employee assessments. The Chief Information Officer (CIO) agrees to allow the engagement to proceed but first wants a threat model. Which of the following is the most appropriate to use for an AI threat model?

Answer: A

Explanation:
ATLAS is specifically designed for creating AI threat models. It catalogs adversarial techniques, tactics, and use cases that target AI systems, making it the most appropriate framework for assessing risks to a custom AI model in employee assessments.


NEW QUESTION # 12
What control reduces the impact radius when a single host is compromised?

Answer: B

Explanation:
Segmentation isolates systems and limits lateral movement.


NEW QUESTION # 13
A cybersecurity analyst must use pattern recognition on a data set containing unstructured data.
Which of the following models is the best for this task?

Answer: C

Explanation:
Convolutional neural networks (CNNs) are highly effective at detecting patterns in unstructured data such as images, audio, or text embeddings. Their ability to automatically learn spatial or hierarchical features makes them the best choice for pattern recognition on unstructured datasets.


NEW QUESTION # 14
Developers introduce new features to their generative AI product in an effort to stand out from the competition and offer more value to customers.
Which of the following most accurately explains the risks when enabling more functionality?

Answer: B

Explanation:
Basic Concept: The relationship between AI system capabilities and security risk is a fundamental concept in AI governance. As AI models gain more functionality and capabilities, their potential for misuse, unintended consequences, and attack surface expansion grows proportionally. CompTIA SecAI+ Study Guide addresses capability-risk proportionality under AI governance.
Why D is Correct: The risks of a generative AI product are proportional to its capabilities. Each new feature expands what the model can do, which simultaneously expands what adversaries can manipulate it to do, what sensitive operations it can be directed to perform, and what unintended harm it can cause. A model that can generate text, images, execute code, and call external APIs has dramatically greater risk potential than one that can only generate text. Risk grows with capability scope.
Why A is Wrong: Risks do not remain constant when new features are added. New features introduce new attack vectors, expand the model ' s action space, and create new opportunities for misuse. Each addition fundamentally changes the system ' s risk profile.
Why B is Wrong: While risks do increase with new features, saying they simply increase does not capture the precise relationship. The increase is proportional to the nature and scope of the capabilities added, not a uniform increment for any feature addition.
Why C is Wrong: While risks can be measured qualitatively, stating that risks are measured qualitatively is a statement about measurement methodology rather than an explanation of how risks change when functionality is enabled. It does not accurately describe the relationship between capability and risk.


NEW QUESTION # 15
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