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CompTIA CY0-001 Exam Syllabus Topics:

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

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CompTIA SecAI+ Certification Exam Sample Questions (Q75-Q80):

NEW QUESTION # 75
A disgruntled employee changed the company policies that a chatbot references in order to create confusion and disrupt the business. Which of the following AI-generated vulnerabilities is the employee exploiting?

Answer: C

Explanation:
Altering the source data (policy content) that the chatbot relies on corrupts its knowledge and behavior, which is characteristic of data poisoning attacks.


NEW QUESTION # 76
Customer feedback for an AI chatbot has a high-rate of non-answers, which is causing higher central processing unit (CPU) utilization. Which of the following should be implemented?

Answer: A

Explanation:
Implementing a response confidence level ensures the chatbot only provides answers when the model is sufficiently confident. This reduces irrelevant or empty responses, improving user experience and lowering unnecessary CPU utilization.


NEW QUESTION # 77
Which of the following helps in managing potential security issues related to model training?

Answer: B

Explanation:
Basic Concept: Managing security risks in AI model training requires a comprehensive framework specifically designed for AI risk identification, assessment, and mitigation across the entire AI lifecycle including data collection, training, and deployment. CompTIA SecAI+ Study Guide identifies NIST AI RMF as the primary resource for AI-specific risk management.
Why A is Correct: The NIST AI Risk Management Framework is purpose-built for managing risks throughout the AI lifecycle. It provides structured guidance for identifying, assessing, and mitigating risks specific to AI systems including training data quality, model bias, data poisoning, and training pipeline vulnerabilities. Its AI-specific scope makes it the most appropriate framework for managing model training security issues.
Why B is Wrong: ISO 27001 is an information security management system standard focused on general IT security controls and risk management. It does not specifically address AI model training risks, data pipeline integrity, or ML-specific vulnerabilities.
Why C is Wrong: The OECD provides high-level AI governance principles and policy recommendations at an international level. It offers ethical and policy guidance but does not provide operational risk management guidance for securing AI model training processes.
Why D is Wrong: GDPR is a European data protection regulation focused on personal data privacy, consent, and individual rights. While relevant to training data governance, it does not address the technical security risks of model training pipelines or ML system vulnerabilities.


NEW QUESTION # 78
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: A

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 # 79
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


NEW QUESTION # 80
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