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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Securing AI Systems40%- Security controls for AI systems
  • 1. Model security: access, integrity, anti-tampering
  • 2. Data protection: integrity, confidentiality, privacy
  • 3. Deployment environment security
- 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
Topic 2: AI-assisted Security24%- AI in security strategy and operations
  • 1. Compliance monitoring and auditing
  • 2. Threat modeling and risk assessment
- AI for threat detection and response
  • 1. Accelerated threat hunting
  • 2. Anomaly detection and behavioral analysis
  • 3. Automated incident triage and correlation
- Security automation and orchestration
  • 1. Vulnerability management and assessment
  • 2. Workflow automation and response playbooks
Topic 3: 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. Transparency, accountability and auditability
  • 2. Data protection and privacy laws
- Governance frameworks and policies
  • 1. Responsible AI principles and ethics
  • 2. Global standards: NIST AI RMF, EU AI Act
  • 3. Organizational AI governance structures
Topic 4: Basic AI Concepts Related to Cybersecurity17%- AI-driven threats and risks
  • 1. Automated phishing, polymorphic malware
  • 2. Adversarial machine learning attacks
  • 3. Malicious use of generative AI
- AI applications in security
  • 1. Security automation and decision support
  • 2. Threat detection and anomaly analysis
- Core AI principles and terminology
  • 1. Machine learning, deep learning, NLP, automation
  • 2. Generative AI concepts and capabilities

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CompTIA SecAI+ Certification Exam Sample Questions (Q150-Q155):

NEW QUESTION # 150
Which of the following should an auditor reference when reviewing a company ' s human resources AI systems for legal non-compliance?

Answer: B

Explanation:
Basic Concept: Various regulatory frameworks govern AI use in different contexts. For auditing legal compliance in high-risk AI applications such as employment and HR, binding regulatory legislation takes precedence over voluntary standards. CompTIA SecAI+ Exam Objectives cover AI governance and compliance frameworks under Domain 4.
Why C is Correct: The EU AI Act is the world ' s first comprehensive, legally binding AI regulation. It explicitly classifies AI systems used in employment, worker management, and recruitment as high-risk AI systems, subjecting them to strict compliance requirements including conformity assessments, transparency obligations, and human oversight mandates. An auditor reviewing HR AI for legal non-compliance must reference this binding legislation.
Why A is Wrong: The OECD AI Principles are non-binding international guidelines promoting responsible AI. They offer policy guidance but carry no legal enforcement power for compliance auditing.
Why B is Wrong: The NIST AI RMF is a voluntary, risk management-focused framework. It is not a legal compliance standard and cannot be used to assess legal non-compliance.
Why D is Wrong: ISO standards such as ISO 42001 are voluntary international best practice standards. They are not legal compliance instruments with enforceable penalties for HR AI systems.


NEW QUESTION # 151
A company is adopting AI and wants to create policies and procedures that include a structure for evaluating, publishing, and approving patterns for AI usage.
Which of the following should the company establish to meet this goal?

Answer: D

Explanation:
Basic Concept: Successful AI adoption at an organizational level requires a centralized governance body that standardizes AI practices, promotes best practices, and ensures consistent, safe, and effective AI deployment across the organization. CompTIA SecAI+ Study Guide covers AI organizational governance structures under Domain 4.
Why A is Correct: An AI Center of Excellence (CoE) is an organizational unit specifically designed to govern, standardize, and advance AI adoption. It develops and publishes policies, creates approved patterns for AI usage, evaluates new AI use cases, provides expert guidance, and maintains governance oversight. The CoE exactly matches the described need for a structure to evaluate, publish, and approve AI usage patterns across the organization.
Why B is Wrong: An AI legal affairs office focuses on legal compliance, intellectual property, and regulatory matters related to AI. While important for legal risk management, it does not fulfill the broader governance mandate of establishing and approving AI usage patterns and best practices across the organization.
Why C is Wrong: An AI audit department conducts post-implementation reviews and compliance assessments of existing AI systems. It is a retrospective and oversight function rather than a proactive body for developing and approving AI usage patterns and policies.
Why D is Wrong: An AI data science division is a technical team focused on building AI models and solutions. It is a development function rather than a governance structure designed to create policies, evaluate AI patterns, and provide cross-organizational oversight of AI adoption.


NEW QUESTION # 152
A cybersecurity administrator must examine the cost of AI and implement controls so the research environment operates within a specified budget.
Which of the following controls is best for this situation?

Answer: C

Explanation:
Basic Concept: Operating AI systems within a budget requires direct control over the primary cost driver of LLM usage. For research environments where users may run extensive queries, token consumption management is the most effective budget control mechanism. CompTIA SecAI+ Study Guide covers token limits as the key cost management control for AI environments.
Why D is Correct: Token limits set hard caps on the maximum tokens consumed per request and per session, directly controlling the per-interaction cost of LLM API usage. In a research environment where users may submit complex, multi-part queries generating long responses, token limits prevent any single interaction from consuming disproportionate budget and enable the administrator to enforce aggregate budget constraints across all users and research activities.
Why A is Wrong: Prompt firewalls inspect and filter prompt content for security and policy compliance. They are security controls designed to prevent malicious or policy-violating prompts, not financial controls for managing token consumption or enforcing budget limits.
Why B is Wrong: API access controls manage authentication and authorization for API interactions, governing who can connect to the AI API. While restricting API access could limit who uses the system, it does not control how much budget individual authorized users consume through their research queries.
Why C is Wrong: Model guardrails enforce content policy and behavioral constraints on model inputs and outputs. They ensure safe and appropriate responses but do not limit the computational resources or tokens consumed by interactions, making them unsuitable as budget enforcement controls.


NEW QUESTION # 153
What control reduces the impact radius when a single host is compromised?

Answer: B

Explanation:
Segmentation isolates systems and limits lateral movement.


NEW QUESTION # 154
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: A

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 # 155
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