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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Governance, Risk and Compliance19%- Governance frameworks and policies
  • 1. Global standards: NIST AI RMF, EU AI Act
  • 2. Responsible AI principles and ethics
  • 3. Organizational AI governance structures
- 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
Topic 2: Basic AI Concepts Related to Cybersecurity17%- AI applications in security
  • 1. Security automation and decision support
  • 2. Threat detection and anomaly analysis
- Core AI principles and terminology
  • 1. Generative AI concepts and capabilities
  • 2. Machine learning, deep learning, NLP, automation
- AI-driven threats and risks
  • 1. Adversarial machine learning attacks
  • 2. Malicious use of generative AI
  • 3. Automated phishing, polymorphic malware
Topic 3: AI-assisted Security24%- AI in security strategy and operations
  • 1. Compliance monitoring and auditing
  • 2. Threat modeling and risk assessment
- AI for threat detection and response
  • 1. Automated incident triage and correlation
  • 2. Accelerated threat hunting
  • 3. Anomaly detection and behavioral analysis
- Security automation and orchestration
  • 1. Vulnerability management and assessment
  • 2. Workflow automation and response playbooks
Topic 4: Securing AI Systems40%- Defending against AI-specific attacks
  • 1. Adversarial example defense
  • 2. Prompt injection, data poisoning, model inversion
  • 3. Threat modeling for AI lifecycles
- 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. Data protection: integrity, confidentiality, privacy
  • 3. Model security: access, integrity, anti-tampering

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CompTIA SecAI+ Certification Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
Which of the following is most resistant to AI manipulation?

Answer: D

Explanation:
Reducing the attack surface limits the number of potential entry points that attackers or manipulated AI inputs can exploit. This proactive security measure is more resistant to AI-driven manipulation than reactive controls like payload scanning or antivirus.


NEW QUESTION # 83
Which IR document defines who must be contacted during a breach and within what timeframe?

Answer: B

Explanation:
The communications plan specifies notification roles and timelines.


NEW QUESTION # 84
Which of the following is the primary purpose of validating data for an AI system?

Answer: B

Explanation:
Basic Concept: Data validation is a critical step in the AI development pipeline that involves verifying that the data used for training and inference meets quality standards, is representative, and is free from systematic errors that could introduce bias. The quality of training data directly determines the quality and fairness of model outputs. CompTIA SecAI+ Study Guide covers data validation under AI development and responsible AI principles.
Why D is Correct: The primary purpose of data validation for an AI system is to ensure that the data is accurate, representative, and free from systematic errors that would cause the model to produce biased or discriminatory outcomes. Validating data checks for class imbalance, demographic underrepresentation, labeling errors, and corrupted values that could embed biases into the model. This ensures the AI system produces fair, accurate, and trustworthy outputs across all user groups.
Why A is Wrong: Automating the process is a benefit of using automated data validation tools but is not the primary purpose of validation itself. The automation serves the validation goal rather than being the reason validation is performed.
Why B is Wrong: Reducing resource consumption is an engineering optimization concern. Data validation may reduce resource waste by preventing training on poor-quality data, but this is a secondary benefit, not the primary purpose.
Why C is Wrong: Optimizing storage databases is a database engineering concern about performance and efficiency. Data validation examines data quality and representativeness for AI purposes, not database architecture optimization.


NEW QUESTION # 85
Which of the following is required first in order to send a prompt query and response in a language model (LLM) system when authentication is enabled?

Answer: D

Explanation:
When authentication is enabled, the first requirement for sending a prompt query and receiving a response is endpoint access control. It ensures only authenticated and authorized users or systems can interact with the LLM endpoint.


NEW QUESTION # 86
A security administrator must provide access controls for AI systems to list tables.
Which of the following should the administrator implement?

Answer: C

Explanation:
Basic Concept: AI systems interact with different resource layers including models, data stores, and infrastructure. Controlling what data an AI system can access requires implementing access controls at the data layer. CompTIA SecAI+ Study Guide differentiates between model access, data access, and network access controls for AI systems.
Why D is Correct: Data access controls govern what data resources an AI system can interact with, including which databases, tables, and records it can read or modify. To control an AI system ' s ability to list database tables, the administrator must implement data access controls that define precisely which tables the AI can enumerate and query, following the principle of least privilege for data interactions.
Why A is Wrong: Agentic AI access refers to permissions granted to autonomous AI agents to perform actions and use tools. It is a broader concept about what an AI agent can do operationally rather than a specific data-layer access control mechanism.
Why B is Wrong: A Network Access Control List controls network traffic at the IP and port level, determining which hosts can communicate with which network resources. It operates at the network layer and cannot enforce fine-grained control over which database tables an AI system is allowed to list.
Why C is Wrong: Model access controls govern who and what can interact with the AI model itself - who can query it, update it, or access its parameters. This is distinct from data access, which controls what the model can read from data stores during operation.


NEW QUESTION # 87
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