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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Basic AI Concepts Related to Cybersecurity17%- Core AI principles and terminology
  • 1. Machine learning, deep learning, NLP, automation
  • 2. Generative AI concepts and capabilities
- AI-driven threats and risks
  • 1. Malicious use of generative AI
  • 2. Adversarial machine learning attacks
  • 3. Automated phishing, polymorphic malware
- AI applications in security
  • 1. Threat detection and anomaly analysis
  • 2. Security automation and decision support
AI Governance, Risk and Compliance19%- Risk management for AI
  • 1. Risk mitigation and control strategies
  • 2. AI risk identification and assessment
- Compliance and legal requirements
  • 1. Transparency, accountability and auditability
  • 2. Data protection and privacy laws
- Governance frameworks and policies
  • 1. Global standards: NIST AI RMF, EU AI Act
  • 2. Responsible AI principles and ethics
  • 3. Organizational AI governance structures
AI-assisted Security24%- Security automation and orchestration
  • 1. Workflow automation and response playbooks
  • 2. Vulnerability management and assessment
- AI for threat detection and response
  • 1. Automated incident triage and correlation
  • 2. Accelerated threat hunting
  • 3. Anomaly detection and behavioral analysis
- AI in security strategy and operations
  • 1. Compliance monitoring and auditing
  • 2. Threat modeling and risk assessment
Securing AI Systems40%- Security controls for AI systems
  • 1. Deployment environment security
  • 2. Model security: access, integrity, anti-tampering
  • 3. Data protection: integrity, confidentiality, privacy
- 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

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

NEW QUESTION # 16
A security administrator wants to prevent prompt injection attacks and ensure responses have sanitized output.
Which of the following provides a primary compensating control for these requirements?

Answer: D

Explanation:
Basic Concept: Preventing prompt injection and ensuring output sanitization requires a control that can inspect both the semantic content of incoming prompts and the safety of outgoing responses. This requires an intelligent, context-aware filtering layer specifically designed for LLM traffic. CompTIA SecAI+ Study Guide identifies LLM firewalls as a primary control for prompt security and output safety.
Why C is Correct: An LLM firewall is specifically designed to inspect, filter, and sanitize both incoming prompts and outgoing AI responses. It can detect and block prompt injection attempts using pattern matching, semantic analysis, and behavioral heuristics, while also sanitizing output to remove sensitive data, harmful content, or policy violations before responses reach users. This dual capability makes it the primary control addressing both requirements simultaneously.
Why A is Wrong: Least privilege restricts what resources and actions users and systems can access. It reduces the potential impact of successful attacks but does not inspect prompt content for injection attempts or sanitize model outputs.
Why B is Wrong: Encryption protects data confidentiality in transit and at rest. It does not analyze prompt content for malicious patterns or filter AI-generated responses for unsafe content. Encrypted traffic can still carry prompt injection attacks.
Why D is Wrong: Rate limiting controls request frequency. While it can slow down automated injection attack campaigns, it does not inspect the content of individual prompts to detect injections, nor does it sanitize output responses. Malicious prompts can still succeed within rate limits.


NEW QUESTION # 17
A developer is proposing a new AI application for human resources systems. Which of the following are the most important considerations?

Answer: D

Explanation:
Option B is correct because a human resources AI application processes highly sensitive employee and applicant information and may influence consequential employment decisions. Privacy and security therefore determine whether the system can be deployed responsibly. The design must address data minimization, lawful collection, access control, encryption, retention, auditability, secure model integration, and protection against unauthorized disclosure. It should also evaluate whether training data or outputs expose protected or confidential information and whether human review is required before decisions affect candidates or employees. Option A concerns friendliness and presentation, not the protection of people or organizational data. Option C may improve usability, but visual design cannot compensate for insecure handling of resumes, performance records, or identity data. Option D can make outputs easier to consume, yet brevity and summarization do not control privacy risk or system compromise. The NIST AI Risk Management Framework identifies privacy enhancement, security, resilience, accountability, and transparency as characteristics of trustworthy AI, making privacy and security the controlling considerations in this scenario.


NEW QUESTION # 18
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?

Answer: C

Explanation:
Basic Concept: Dynamic Application Security Testing (DAST) tests running applications by sending various inputs to discover vulnerabilities. AI can significantly enhance DAST by intelligently generating diverse, targeted test payloads that traditional tools might miss. CompTIA SecAI+ covers AI augmentation of security testing methodologies.
Why C is Correct: Payload creation is highly suitable for AI automation during DAST. AI can generate diverse, contextually appropriate attack payloads such as SQL injection strings, XSS vectors, command injection attempts, and format string exploits tailored to the specific application ' s behavior observed during testing. AI can learn from the application ' s responses to previous payloads and generate increasingly targeted inputs, discovering vulnerabilities more efficiently than static payload databases.
Why A is Wrong: DDoS attacks are volume-based attacks designed to overwhelm network or application infrastructure. Automating DDoS during DAST is inappropriate as it would disrupt service availability rather than discover application security vulnerabilities, and it is harmful to legitimate operations.
Why B is Wrong: Data poisoning is an attack targeting AI/ML model training data integrity. It is relevant to securing AI systems but is not a DAST technique for testing web or software application security vulnerabilities during dynamic testing.
Why D is Wrong: Threat modeling is a structured analysis process performed before development or testing to identify potential threats and design appropriate countermeasures. It is a planning activity, not an attack technique that can be automated during dynamic application security testing.


NEW QUESTION # 19
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: D

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 # 20
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: C


NEW QUESTION # 21
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