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CompTIA CY0-001 Exam Syllabus Topics:

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

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CompTIA SecAI+ Certification Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?

Answer: A

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 # 93
An AI security architect needs to conduct a security review of a new AI chatbot. Which of the following resources would the architect most likely rely on to perform a threat assessment and ensure comprehensive coverage during the review?

Answer: C

Explanation:
The OWASP Top 10 for Large Language Model Applications is the most directly applicable resource because the system under review is an AI chatbot based on LLM technology. CompTIA SecAI+ Objective 2.1 explicitly identifies the OWASP Top 10, including the LLM Top 10, as an AI threat-modeling resource. It addresses security risks specifically associated with LLM applications, including prompt injection, sensitive information disclosure, insecure output handling, excessive agency, and other AI-specific application threats.
ISO 27001 provides a broader information security management framework and is valuable for organizational security governance, but it is not specifically designed to enumerate LLM attack vectors. The EU AI Act primarily addresses regulatory obligations, risk classifications, and governance requirements rather than serving as a technical LLM threat-assessment checklist. Option A is also not the appropriate LLM-specific resource. For a security architect who needs systematic technical coverage of threats affecting an AI chatbot, the OWASP LLM Top 10 provides the most targeted framework among the listed options.


NEW QUESTION # 94
Part 1: Use drop-down menu to select the most appropriate protocol or cipher for each system component.
Part 2: Use the drop-down menu to select the most appropriate technique to apply to the modified data.
An engineer is analyzing findings from a penetration test that indicate insufficient data encryption. The engineer must implement data security.

Answer:

Explanation:

Explanation:
See Explanation

Basic Concept: This is a Performance-Based Question (PBQ) - a simulation requiring interactive protocol and cipher selection in the actual exam. It tests the candidate ' s ability to select appropriate encryption protocols for different AI system components and apply proper data security techniques for sensitive AI data.
Key Concept - Encryption by Component: For data in transit between AI components, TLS 1.3 is the current standard protocol. For API communications, HTTPS with TLS 1.3 and certificate pinning is appropriate. For database connections used by AI systems, TLS with strong cipher suites such as AES-256-GCM should be applied. For data at rest in model stores and training data repositories, AES-256 encryption is standard. For authentication tokens and keys, RSA-2048 or ECC P-256 are appropriate asymmetric options.
Key Concept - Data Techniques: For sensitive AI training data that has been modified, tokenization replaces sensitive values with non-sensitive tokens. Masking obscures sensitive fields in outputs. Hashing provides one-way verification for data integrity.
Reference: CompTIA SecAI+ Study Guide Domain 2 covers encryption requirements for AI system data security. Candidates should know standard encryption protocols (TLS 1.3, AES-256), when to apply each cipher type, and which data security techniques such as masking, tokenization, and encryption at rest are appropriate for different AI system data categories and sensitivity levels.


NEW QUESTION # 95
An analyst wants to develop a solution to review security information and event management (SIEM) logs for endpoint analytics. Which of the following is a benefit of a small language model (SLM) compared to a large language model (LLM) when cost efficiency is a consideration?

Answer: A

Explanation:
Domain-specific specialization is a major advantage of using a small language model when cost efficiency matters. CompTIA SecAI+ requires candidates to distinguish both LLMs and SLMs within natural language processing concepts. An SLM can be trained or fine-tuned for a narrowly defined task such as interpreting SIEM events, endpoint terminology, security alerts, and incident patterns. Because it does not require the enormous parameter count and broad general-purpose knowledge of a large model, an SLM can provide efficient task-specific inference while requiring less memory and computational capacity. This can reduce infrastructure and operational costs substantially. Broad dataset training is characteristic of general-purpose foundation models and tends to increase rather than reduce training and compute requirements. Data aggregation is a preprocessing or analytics function and is not an inherent SLM advantage. Data privacy can improve when smaller models are deployed locally, but local deployment is an architectural choice rather than the defining cost-related benefit represented by the available choices. A model concentrated on domain- specific cybersecurity information can therefore deliver the required SIEM-analysis capability more economically than a general-purpose LLM.


NEW QUESTION # 96
A cybersecurity analyst must use pattern recognition on a data set containing unstructured data.
Which of the following models is the best for this task?

Answer: C

Explanation:
Convolutional neural networks (CNNs) are highly effective at detecting patterns in unstructured data such as images, audio, or text embeddings. Their ability to automatically learn spatial or hierarchical features makes them the best choice for pattern recognition on unstructured datasets.


NEW QUESTION # 97
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