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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Securing AI Systems40%- 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. Prompt injection, data poisoning, model inversion
  • 2. Threat modeling for AI lifecycles
  • 3. Adversarial example defense
- Secure AI development and operations
  • 1. Secure MLOps and AI pipeline design
  • 2. DevSecOps integration for AI
AI-assisted Security24%- AI in security strategy and operations
  • 1. Threat modeling and risk assessment
  • 2. Compliance monitoring and auditing
- Security automation and orchestration
  • 1. Workflow automation and response playbooks
  • 2. Vulnerability management and assessment
- AI for threat detection and response
  • 1. Accelerated threat hunting
  • 2. Automated incident triage and correlation
  • 3. Anomaly detection and behavioral analysis
AI Governance, Risk and Compliance19%- Compliance and legal requirements
  • 1. Data protection and privacy laws
  • 2. Transparency, accountability and auditability
- Risk management for AI
  • 1. AI risk identification and assessment
  • 2. Risk mitigation and control strategies
- Governance frameworks and policies
  • 1. Organizational AI governance structures
  • 2. Global standards: NIST AI RMF, EU AI Act
  • 3. Responsible AI principles and ethics
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

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CompTIA SecAI+ Certification Exam Sample Questions (Q16-Q21):

NEW QUESTION # 16
An AI architect reviews AI utilization and wants to improve the user experience. Which of the following should the architect review within the logs?

Answer: A

Explanation:
To improve user experience, the architect should review model accuracy in the logs. High accuracy ensures users receive relevant, reliable responses, directly impacting satisfaction and effectiveness of the AI system.


NEW QUESTION # 17
Which of the following is a risk addressed by responsible AI?

Answer: C

Explanation:
Basic Concept: Responsible AI is a governance framework addressing risks that arise from AI systems producing outcomes that are unfair, harmful, or contrary to human values. Different risk types fall under different governance domains - some under responsible AI, others under security or operational management. CompTIA SecAI+ Study Guide covers responsible AI risk categories under Domain 4.
Why C is Correct: Response bias occurs when an AI system ' s outputs are systematically skewed against certain groups, topics, or perspectives, reflecting biases embedded in training data or model design. This is a core risk addressed by responsible AI principles including fairness, non-discrimination, and explainability.
Responsible AI frameworks mandate bias detection, assessment, and mitigation to ensure AI responses treat all users and groups equitably.
Why A is Wrong: Model drift describes the degradation of model performance over time as the distribution of real-world data diverges from the training data distribution. While an important operational concern, model drift is primarily a technical performance risk managed through MLOps and monitoring practices, not a core responsible AI governance concern.
Why B is Wrong: Reputational loss is a business risk consequence that may result from various AI failures including biased outputs or privacy violations. It is an outcome or impact rather than a specific risk category that responsible AI frameworks directly address.
Why D is Wrong: Data poisoning is a security attack where adversaries corrupt AI training data to manipulate model behavior. This is a cybersecurity threat managed through security controls and data integrity protections rather than responsible AI ethical governance frameworks focused on fairness and accountability.


NEW QUESTION # 18
An organization develops a chatbot with the following requirements:
- Does not provide harmful or explicit responses
- Must use clean and professional language
- Ensures that responses are accurate
Which of the following should the organization conduct after the chatbot is fully developed but before a customer-facing deployment?

Answer: C

Explanation:
Guardrail testing and validation ensure the chatbot adheres to safety, language, and accuracy requirements before deployment. This step verifies the model will not generate harmful, explicit, or unprofessional responses in a customer-facing environment.


NEW QUESTION # 19
Which of the following are considered threat intelligence sources? (Choose two.)

Answer: A,B

Explanation:
ISACs and OSINT provide structured and open-source threat intelligence feeds.


NEW QUESTION # 20
Instructions: Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.

Answer:

Explanation:

Explanation:

Basic Concept: This is a Performance-Based Question (PBQ) - a HOTSPOT/simulation item requiring interactive selection in the actual exam. It tests the candidate ' s ability to map appropriate security controls to AI system components such as API gateway, model endpoint, data layer, and authentication layer.
Key Concept - Appropriate Controls by Component: For an API gateway connecting an AI system, typical controls include API key authentication, rate limiting, TLS encryption, and input validation. For the model endpoint, controls include IAM role-based access, audit logging, and guardrails. For data access components, encryption at rest and data masking are appropriate. For the authentication layer, MFA and expiring session tokens are relevant.
Why This Matters: The CompTIA SecAI+ Study Guide emphasizes defense-in-depth for AI system integration, ensuring each architectural layer has dedicated, appropriate security controls. The principle of least privilege should guide access control assignments at each component, while availability controls such as rate limiting protect against abuse.
Reference: CompTIA SecAI+ Exam Objectives Domain 2 (Securing AI Systems) covers AI system component security controls. Candidates should study the mapping of controls to infrastructure components including API gateways, model serving endpoints, data stores, and identity management layers. In the live exam, select the most specific and directly relevant control for each component based on the component ' s function and risk profile.


NEW QUESTION # 21
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