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CompTIA CY0-001 Exam Syllabus Topics:

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

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CY0-001 Exam Questions & CY0-001 Study Materials & CY0-001 Dumps Torrent

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CompTIA SecAI+ Certification Exam Sample Questions (Q76-Q81):

NEW QUESTION # 76
An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?

Answer: D

Explanation:
Checksums ensure data integrity by detecting any unauthorized or accidental changes to the dataset. This provides a reliable way to confirm that sensitive attributes such as age, ethnicity, and diabetes status remain unaltered during development.


NEW QUESTION # 77
An organization deploys a browser-based AI plug-in to detect malicious websites and phishing links in corporate email.
Which of the following techniques is used in this AI plug-in?

Answer: C

Explanation:
Basic Concept: AI-based security tools for detecting malicious websites and phishing links operate by analyzing URLs, page content, and link characteristics against known malicious patterns and behavioral signatures. CompTIA SecAI+ Study Guide covers pattern recognition and signature matching as fundamental AI-assisted threat detection techniques.
Why B is Correct: Pattern recognition and signature matching are the core techniques used in malicious website and phishing link detection. The AI plug-in uses pattern recognition to identify characteristics of phishing pages such as login form structures mimicking legitimate sites, suspicious domain patterns, and redirect behaviors. Signature matching compares URLs and page content against databases of known malicious sites and phishing infrastructure. Together these techniques enable accurate detection of threats in email links before users click them.
Why A is Wrong: Code quality testing analyzes source code for bugs, vulnerabilities, and adherence to coding standards during software development. It has no application for detecting malicious websites or phishing links in real-time email scanning.
Why C is Wrong: Automated penetration testing proactively exploits vulnerabilities to assess security posture.
It is an offensive security assessment technique, not a real-time threat detection technique for identifying malicious links in email.
Why D is Wrong: Automated incident response executes predefined response actions when security incidents are detected, such as isolating endpoints or blocking users. It operates after threats are detected, not during the detection phase that identifies malicious websites and links.


NEW QUESTION # 78
An automobile manufacturer implements a chatbot to assist with configuration options for customer automobiles. Given a customer ' s prompt, the chatbot gives offensive responses.
Which of the following describes this behavior?

Answer: D


NEW QUESTION # 79
SIMULATION
Instructions
Click the (+) to assign each threat category into its appropriate framework.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
An architect is modeling an agentic system to meet security standards.

Answer:

Explanation:

Explanation:
MAESTRO: Overreliance, Insecure plug-in design
OWASP Top 10: Broken access control, Identification and authentication failures OWASP Top 10 LLM: Prompt injection, Model denial of service STRIDE: Elevation of privilege, Repudiation, Supply chain vulnerabilities, Insecure design MAESTRO addresses AI-specific misuse such as excessive trust in outputs (overreliance) and unsafe plug-in design.
OWASP Top 10 maps to classic web threats: broken access control and authentication failures are key risks.
OWASP Top 10 LLM focuses on LLM-related threats like prompt injection and denial of service targeting the model.
STRIDE categories cover privilege escalation, repudiation (denying actions), supply chain risks, and insecure design flaws.


NEW QUESTION # 80
During an update, an AI system flags some potential compatibility issues and provides recommendations. An administrator reviews the recommendations before addressing the issues.
Which of the following processes describes this scenario?

Answer: B

Explanation:
Basic Concept: Human-in-the-loop is a design pattern where AI systems generate recommendations or decisions but require human review and approval before those recommendations are acted upon. This approach maintains human oversight and accountability in AI-assisted workflows. CompTIA SecAI+ Study Guide covers human-in-the-loop as a key responsible AI principle and operational pattern.
Why C is Correct: The scenario precisely describes the human-in-the-loop pattern: the AI system identifies potential issues and provides recommendations, but an administrator must review those recommendations before any action is taken. This deliberate inclusion of human judgment in the AI ' s decision or recommendation workflow ensures human oversight is maintained, which is the defining characteristic of the human-in-the-loop process.
Why A is Wrong: Data validation verifies that data meets expected quality standards and formats before being used in AI processing. It is a data quality control activity, not a workflow pattern describing human review of AI recommendations.
Why B is Wrong: Data preparation involves transforming raw data into a format suitable for AI model training or inference. It encompasses cleaning, normalizing, and formatting data, not the process of human review of AI-generated recommendations during system updates.
Why D is Wrong: Model evaluation assesses a model ' s performance against metrics such as accuracy, precision, and recall on test datasets. It is a technical assessment of model quality, not a workflow process where humans review AI-generated recommendations before acting on them.


NEW QUESTION # 81
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