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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI Governance, Risk and Compliance19%- Governance frameworks and policies
  • 1. Organizational AI governance structures
  • 2. Global standards: NIST AI RMF, EU AI Act
  • 3. Responsible AI principles and ethics
- Compliance and legal requirements
  • 1. Data protection and privacy laws
  • 2. Transparency, accountability and auditability
- Risk management for AI
  • 1. Risk mitigation and control strategies
  • 2. AI risk identification and assessment
Topic 2: 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. Security automation and decision support
  • 2. Threat detection and anomaly analysis
- AI-driven threats and risks
  • 1. Adversarial machine learning attacks
  • 2. Malicious use of generative AI
  • 3. Automated phishing, polymorphic malware
Topic 3: Securing AI Systems40%- Defending against AI-specific attacks
  • 1. Threat modeling for AI lifecycles
  • 2. Prompt injection, data poisoning, model inversion
  • 3. Adversarial example defense
- Secure AI development and operations
  • 1. Secure MLOps and AI pipeline design
  • 2. DevSecOps integration for AI
- Security controls for AI systems
  • 1. Model security: access, integrity, anti-tampering
  • 2. Data protection: integrity, confidentiality, privacy
  • 3. Deployment environment security
Topic 4: 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. Vulnerability management and assessment
  • 2. Workflow automation and response playbooks
- AI for threat detection and response
  • 1. Accelerated threat hunting
  • 2. Anomaly detection and behavioral analysis
  • 3. Automated incident triage and correlation

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CompTIA SecAI+ Certification Exam Sample Questions (Q33-Q38):

NEW QUESTION # 33
Which of the following helps in managing potential security issues related to model training?

Answer: B

Explanation:
The NIST AI RMF provides structured guidance for identifying, assessing, and managing risks specific to AI systems, including those arising during model training. It addresses issues like bias, security, and data integrity, making it the best framework for managing training-related security concerns.


NEW QUESTION # 34
An airline corporation wants to implement a chatbot application using a large language model (LLM) so its customers:
- Can ask question and receive answers about flight details.
- Have the option to upload files.
Which of the following security controls should the airline use to protect against malicious input and unauthorized use beyond the service-level agreement? (Choose two.)

Answer: A,C

Explanation:
Prompt guardrails are needed to prevent malicious or manipulated inputs (prompt injection) from causing the chatbot to provide harmful, misleading, or unauthorized responses.
Model token quotas limit the amount of input/output a user can generate, preventing abuse or excessive usage beyond the service-level agreement (SLA).


NEW QUESTION # 35
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: D

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 # 36
A customer using a travel chatbot reports that the chatbot responds with the following:
def choose_location(location):
if location == " United States of America " :
print( " You have selected an authorized travel destination " )
elif location == " Europe " :
print( " You have selected an unauthorized travel destination " )
Which of the following remediates this issue?

Answer: D

Explanation:
Deploying data loss prevention (DLP) is the best answer because the chatbot has exposed internal application logic rather than returning only the information intended for the customer. This represents a form of sensitive information disclosure , which CompTIA SecAI+ specifically identifies as an AI-system attack or failure condition. The objectives also emphasize data security controls such as classification, redaction, masking, and minimization to prevent inappropriate disclosure.
A DLP control can inspect generated responses for source code, confidential identifiers, credentials, internal business logic, or other restricted information and prevent that content from being returned externally.
Retraining the model to eliminate bias is unrelated because no discriminatory or statistically skewed behavior is shown. Secure output handling is important when model-generated output is consumed by downstream applications and could lead to injection or execution vulnerabilities. Here, however, the immediate issue is disclosure of internal information directly to a user. Cookies also do not address the problem; they are associated with maintaining web sessions and are not a general mitigation for server-side request forgery.
Therefore, DLP most directly addresses the exposed information.


NEW QUESTION # 37
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 # 38
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