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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Basic AI Concepts Related to Cybersecurity17%- AI-driven threats and risks
  • 1. Adversarial machine learning attacks
  • 2. Automated phishing, polymorphic malware
  • 3. Malicious use of generative AI
- Core AI principles and terminology
  • 1. Generative AI concepts and capabilities
  • 2. Machine learning, deep learning, NLP, automation
- AI applications in security
  • 1. Threat detection and anomaly analysis
  • 2. Security automation and decision support
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. Threat modeling and risk assessment
  • 2. Compliance monitoring and auditing
Securing AI Systems40%- Security controls for AI systems
  • 1. Deployment environment security
  • 2. Data protection: integrity, confidentiality, privacy
  • 3. Model security: access, integrity, anti-tampering
- Secure AI development and operations
  • 1. DevSecOps integration for AI
  • 2. Secure MLOps and AI pipeline design
- Defending against AI-specific attacks
  • 1. Adversarial example defense
  • 2. Prompt injection, data poisoning, model inversion
  • 3. Threat modeling for AI lifecycles
AI Governance, Risk and Compliance19%- Compliance and legal requirements
  • 1. Data protection and privacy laws
  • 2. Transparency, accountability and auditability
- Governance frameworks and policies
  • 1. Global standards: NIST AI RMF, EU AI Act
  • 2. Organizational AI governance structures
  • 3. Responsible AI principles and ethics
- Risk management for AI
  • 1. Risk mitigation and control strategies
  • 2. AI risk identification and assessment

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CompTIA SecAI+ Certification Exam Sample Questions (Q20-Q25):

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
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?

Answer: C

Explanation:
During DAST, automating the generation of diverse, targeted attack payloads lets testers probe runtime inputs (e.g., XSS, SQLi, command injection) more thoroughly and discover vulnerabilities that manual or static tests might miss.


NEW QUESTION # 22
Which of the following technologies is used in deepfake?

Answer: B

Explanation:
Deepfakes are primarily created using GANs, where two neural networks (a generator and a discriminator) compete to produce highly realistic synthetic media, such as manipulated videos or images.


NEW QUESTION # 23
An AI security team must assess the probability of an attack on its new system and the impact associated with such an attack. Which of the following threat-modeling resources best addresses the threat landscape for machine learning (ML)?

Answer: C

Explanation:
MITRE ATLAS is specifically designed to capture adversarial tactics, techniques, and procedures (TTPs) targeting machine learning systems. It helps organizations assess both the probability and impact of AI/ML-related attacks, making it the most relevant threat-modeling resource.


NEW QUESTION # 24
A vulnerability scan produces many false positives. What does this indicate?

Answer: C

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


NEW QUESTION # 25
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