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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
- Governance frameworks and policies
  • 1. Responsible AI principles and ethics
  • 2. Global standards: NIST AI RMF, EU AI Act
  • 3. Organizational AI governance structures
- Compliance and legal requirements
  • 1. Transparency, accountability and auditability
  • 2. Data protection and privacy laws
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 applications in security
  • 1. Security automation and decision support
  • 2. Threat detection and anomaly analysis
- AI-driven threats and risks
  • 1. Adversarial machine learning attacks
  • 2. Automated phishing, polymorphic malware
  • 3. Malicious use of generative AI
Securing AI Systems40%- Defending against AI-specific attacks
  • 1. Threat modeling for AI lifecycles
  • 2. Adversarial example defense
  • 3. Prompt injection, data poisoning, model inversion
- 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
AI-assisted Security24%- Security automation and orchestration
  • 1. Vulnerability management and assessment
  • 2. Workflow automation and response playbooks
- AI for threat detection and response
  • 1. Automated incident triage and correlation
  • 2. Anomaly detection and behavioral analysis
  • 3. Accelerated threat hunting
- AI in security strategy and operations
  • 1. Threat modeling and risk assessment
  • 2. Compliance monitoring and auditing

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CompTIA SecAI+ Certification Exam Sample Questions (Q91-Q96):

NEW QUESTION # 91
A customer using a travel chatbot reports that the chatbot responds with the following:
def choose_location(location):
if location == " United States of America " :
print( " You have selected an authorized travel destination " )
elif location == " Europe " :
print( " You have selected an unauthorized travel destination " )
Which of the following remediates this issue?

Answer: D

Explanation:
Deploying data loss prevention (DLP) is the best answer because the chatbot has exposed internal application logic rather than returning only the information intended for the customer. This represents a form of sensitive information disclosure , which CompTIA SecAI+ specifically identifies as an AI-system attack or failure condition. The objectives also emphasize data security controls such as classification, redaction, masking, and minimization to prevent inappropriate disclosure.
A DLP control can inspect generated responses for source code, confidential identifiers, credentials, internal business logic, or other restricted information and prevent that content from being returned externally.
Retraining the model to eliminate bias is unrelated because no discriminatory or statistically skewed behavior is shown. Secure output handling is important when model-generated output is consumed by downstream applications and could lead to injection or execution vulnerabilities. Here, however, the immediate issue is disclosure of internal information directly to a user. Cookies also do not address the problem; they are associated with maintaining web sessions and are not a general mitigation for server-side request forgery.
Therefore, DLP most directly addresses the exposed information.


NEW QUESTION # 92
A security team is using an AI-based tool to try to bypass organizational boundaries. The team uses AI to look at the current state and suggest different attack vectors based on the outcome of the previous ones. Which of the following techniques is the team most likely using?

Answer: A

Explanation:
The described behavior - iteratively assessing the environment and adapting attack paths based on prior outcomes - matches automated penetration testing, where AI-driven tools simulate attack chains and adjust tactics dynamically.


NEW QUESTION # 93
Which of the following ensures the integrity of data usage in an AI system?

Answer: C

Explanation:
Basic Concept: Data integrity in AI systems requires not only that data is accurate at a point in time, but that its entire history of transformation and usage can be traced and verified. Tracking how data has been used and transformed throughout the AI system lifecycle provides ongoing integrity assurance. CompTIA SecAI+ Study Guide covers data governance controls including lineage for AI integrity.
Why D is Correct: Data lineage tracks and documents the complete journey of data from its origin through every transformation, processing step, and use within an AI system. By recording what happened to the data, when, by whom, and through which processes, data lineage provides the audit trail needed to ensure data integrity throughout the AI system ' s data usage lifecycle. It enables verification that data has been used as intended and has not been improperly modified at any stage.
Why A is Wrong: Data masking replaces sensitive data values with anonymized equivalents to protect privacy. It is a confidentiality control that modifies data values rather than a mechanism for ensuring or tracking data integrity across the system.
Why B is Wrong: Data cleansing removes or corrects errors, inconsistencies, and noise in datasets to improve data quality. It is a data preparation activity that improves data accuracy at a point in time but does not track data usage or provide ongoing integrity assurance throughout the AI system lifecycle.
Why C is Wrong: Data verification confirms that data meets expected quality standards and validates its accuracy at a specific check point. While important for quality assurance, it provides a point-in-time check rather than continuous tracking of data usage and transformations as data lineage does.


NEW QUESTION # 94
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?

Answer: B

Explanation:
Basic Concept: Security posture management for AI systems involves assessing and improving the overall security state of AI deployments, including identifying risks, implementing controls, and maintaining ongoing compliance. Appropriate frameworks provide structure for this assessment. CompTIA SecAI+ Study Guide identifies NIST AI RMF as the primary framework for AI risk and posture management.
Why B is Correct: The NIST AI Risk Management Framework provides comprehensive, actionable guidance for managing and improving AI security and risk posture across the entire AI lifecycle. It includes the GOVERN, MAP, MEASURE, and MANAGE functions that directly address posture management activities including risk identification, assessment, and control implementation for ML use cases. Its technical depth and ML-specific guidance make it ideal for this summarization task.
Why A is Wrong: OECD standards provide high-level policy principles for AI governance at an international level. They lack the technical specificity and operational guidance needed to summarize posture management impact on a specific ML use case.
Why C is Wrong: The EU AI Act is a regulatory compliance framework establishing legal requirements for AI systems. While it addresses risk management, its focus is on legal compliance rather than technical posture management guidance for ML systems.
Why D is Wrong: A Generative Adversarial Network is an AI architecture for generating synthetic data, not a framework or standard. It has no relevance as a reference for AI security posture management.


NEW QUESTION # 95
A SOC analyst notices a sudden spike in outbound traffic from a server. The traffic is being sent continuously to an unknown external IP address. Which of the following BEST describes this behavior?

Answer: C

Explanation:
A sudden and sustained outbound transfer to an unknown IP is a common sign of data exfiltration.


NEW QUESTION # 96
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