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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
AI Governance, Risk and Compliance19%- Governance frameworks and policies
  • 1. Responsible AI principles and ethics
  • 2. Organizational AI governance structures
  • 3. Global standards: NIST AI RMF, EU AI Act
- 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
Basic AI Concepts Related to Cybersecurity17%- AI applications in security
  • 1. Security automation and decision support
  • 2. Threat detection and anomaly analysis
- AI-driven threats and risks
  • 1. Automated phishing, polymorphic malware
  • 2. Malicious use of generative AI
  • 3. Adversarial machine learning attacks
- Core AI principles and terminology
  • 1. Machine learning, deep learning, NLP, automation
  • 2. Generative AI concepts and capabilities
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. Compliance monitoring and auditing
  • 2. Threat modeling and risk assessment
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. Model security: access, integrity, anti-tampering
  • 2. Deployment environment security
  • 3. Data protection: integrity, confidentiality, privacy

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CompTIA SecAI+ Certification Exam Sample Questions (Q88-Q93):

NEW QUESTION # 88
A security operations center (SOC) has a very high volume of logs and alerts. The manager proposes the implementation of a machine learning (ML) system to help with triage.
Which of the following tasks is most suitable?

Answer: C

Explanation:
Basic Concept: ML models excel at classification tasks, learning to assign incoming data points to predefined categories based on patterns in training data. In a SOC context, alert classification is the highest-value triage function ML can perform. CompTIA SecAI+ Exam Objectives address AI-assisted security operations under Domain 3.
Why C is Correct: ML-based alert classification automatically analyzes characteristics of each alert and assigns it to a severity category such as critical, high, medium, or low, or to a threat type such as malware or intrusion attempt. This dramatically reduces analyst workload and speeds triage by prioritizing which alerts demand immediate human attention, directly solving the high-volume problem.
Why A is Wrong: Applying filters on specific alerts is a rule-based operation achievable without ML using simple log management tools. It requires no learning capability and does not adapt to new or evolving threats.
Why B is Wrong: Automatically patching systems is a remediation action requiring validated, controlled processes. Having an ML system autonomously patch production systems without human oversight poses unacceptable operational and security risk.
Why D is Wrong: Summarizing alert content is a useful generative AI function but does not provide prioritization value for triage. Classification tells analysts what to act on first; summarization only rephrases existing information.


NEW QUESTION # 89
A vulnerability scan produces many false positives. What does this indicate?

Answer: C

Explanation:
High sensitivity โ†’ catches many issues but lowers accuracy (more false positives).


NEW QUESTION # 90
A company wants to reduce IDS false positives. What tuning should occur FIRST?

Answer: A

Explanation:
A behavioral baseline enables effective tuning and alert reduction.


NEW QUESTION # 91
Faculty members at a university are concerned about potential inherent bias and inconsistency in one department's AI plagiarism detection service.
Which of the following principles will most likely to address their concerns?

Answer: B

Explanation:
Consistency ensures that an AI system applies rules and produces results in a uniform manner across all cases. This principle directly addresses concerns about bias and irregular outcomes in the plagiarism detection service.


NEW QUESTION # 92
Which of the following is an example of how a security analyst uses generative AI in the triage process?

Answer: D

Explanation:
In triage, generative AI is most effective for quickly summarizing and categorizing large volumes of alerts or findings. This helps analysts prioritize incidents and streamline the investigation process.


NEW QUESTION # 93
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