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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
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. Vulnerability management and assessment
  • 2. Workflow automation and response playbooks
- AI for threat detection and response
  • 1. Accelerated threat hunting
  • 2. Automated incident triage and correlation
  • 3. Anomaly detection and behavioral analysis
Securing AI Systems40%- Defending against AI-specific attacks
  • 1. Threat modeling for AI lifecycles
  • 2. Prompt injection, data poisoning, model inversion
  • 3. Adversarial example defense
- 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
Basic AI Concepts Related to Cybersecurity17%- AI-driven threats and risks
  • 1. Malicious use of generative AI
  • 2. Automated phishing, polymorphic malware
  • 3. Adversarial machine learning attacks
- 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 Governance, Risk and Compliance19%- Governance frameworks and policies
  • 1. Organizational AI governance structures
  • 2. Responsible AI principles and ethics
  • 3. Global standards: NIST AI RMF, EU AI Act
- Risk management for AI
  • 1. Risk mitigation and control strategies
  • 2. AI risk identification and assessment
- Compliance and legal requirements
  • 1. Transparency, accountability and auditability
  • 2. Data protection and privacy laws

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CompTIA SecAI+ Certification Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
An administrator, who works for a financial institution, is required to implement data security controls for data at rest within AI systems that involve data disclosure. Which of the following is the most suitable control?

Answer: B

Explanation:
For financial institutions handling AI systems, protecting data at rest against disclosure requires encryption. Encryption ensures that even if the storage medium is accessed or compromised, the data remains unreadable without the proper decryption keys.


NEW QUESTION # 82
Which of the following ensures the integrity of data usage in an AI system?

Answer: D

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 # 83
A detection engineering team wants to use AI to automatically prevent vulnerable code from reaching production.
Which of the following is the most effective way to accomplish this task?

Answer: B

Explanation:
Basic Concept: Preventing vulnerable code from reaching production requires an automated, mandatory gate in the software delivery pipeline. The CI/CD pipeline is the enforcement point where all code must pass before deployment. CompTIA SecAI+ Study Guide covers AI integration in secure development pipelines under AI-assisted security.
Why C is Correct: Implementing an LLM in the CI/CD runner creates a mandatory automated security gate that every code change must pass. The LLM can analyze code for vulnerabilities, insecure patterns, and policy violations, then automatically fail the build if issues are found. This prevents vulnerable code from progressing toward production without human bypass capability, making it the most effective enforcement mechanism.
Why A is Wrong: IDE plug-ins provide warnings to developers during coding, but developers can choose to ignore them and proceed with compilation and commits. Warnings are advisory, not preventive, and cannot guarantee vulnerable code is blocked from the pipeline.
Why B is Wrong: SOAR platforms with ML models are excellent for incident response and security operations automation. However, they are not positioned in the code delivery pipeline and do not gate code from progressing to production.
Why D is Wrong: An agentic penetration testing tool validates vulnerabilities reactively after code is written or deployed. This approach does not intercept code before production deployment and is typically used for post-deployment assessment rather than prevention.


NEW QUESTION # 84
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 # 85
A data set containing medical information is put into a machine learning (ML) model that is designed to predict specific illnesses for a population. In the process of verifying the reliability of the system, the compliance officer realizes that the system cannot reliably predict illnesses for certain segments of the population. Which of the following types of risk is most applicable to this case?

Answer: A

Explanation:
The model's inability to reliably predict illnesses for certain population segments indicates bias in the dataset or training process. This leads to unfair or inaccurate outcomes for specific groups, making bias the most applicable risk in this case.


NEW QUESTION # 86
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