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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Basic AI Concepts Related to Cybersecurity17%- AI and Machine Learning Fundamentals
  • 1. Supervised, unsupervised, reinforcement learning
    • 2. Neural networks and deep learning basics
      - Generative AI Concepts
      • 1. Prompting and AI interaction fundamentals
        • 2. LLMs and generative AI basics
          - AI Threat Landscape
          • 1. AI-driven cyber threats (phishing, malware automation)
            • 2. Adversarial AI and model manipulation
              Topic 2: AI Governance, Risk, and Compliance19%- Risk and Compliance
              • 1. Ethical AI and responsible usage policies
                • 2. Regulatory compliance for AI systems
                  - AI Governance Frameworks
                  • 1. ISO/IEC AI governance alignment
                    • 2. NIST AI RMF concepts
                      Topic 3: AI-Assisted Security24%- Operational Use of AI
                      • 1. AI-enabled threat intelligence analysis
                        • 2. Incident response acceleration
                          - Security Operations Enhancement
                          • 1. AI-driven threat detection and anomaly detection
                            • 2. SOC automation and alert correlation
                              Topic 4: Securing AI Systems40%- AI System Protection
                              • 1. Secure AI pipelines and deployment environments
                                • 2. Data protection and model security
                                  - Adversarial Defense
                                  • 1. Data poisoning mitigation
                                    • 2. Model extraction and inference attack defense

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                                      CY0-001 Fragenpool & CY0-001 Prüfungsfragen

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                                      CompTIA SecAI+ Certification Exam CY0-001 Prüfungsfragen mit Lösungen (Q65-Q70):

                                      65. Frage
                                      A group of security engineers is developing a security incident and event management (SIEM) system that will:
                                      - Be able to ingest data from multiple structured and unstructured
                                      sources.
                                      - Have a chatbot integrated with a large language model (LLM) that the
                                      security analyst can interact with.
                                      - Provide insights from the SIEM alert data.
                                      Which of the following techniques should the security engineers consider before collecting the data from the respective sources?

                                      Antwort: D

                                      Begründung:
                                      Data cleansing ensures that structured and unstructured data ingested into the SIEM is accurate, consistent, and free from errors or irrelevant information. This step is critical before integrating with an LLM chatbot, as clean data improves the reliability and quality of insights generated.


                                      66. Frage
                                      Which of the following is most resistant to AI manipulation?

                                      Antwort: D


                                      67. Frage
                                      A disgruntled employee changed the company policies that a chatbot references in order to create confusion and disrupt the business. Which of the following AI-generated vulnerabilities is the employee exploiting?

                                      Antwort: A

                                      Begründung:
                                      Altering the source data (policy content) that the chatbot relies on corrupts its knowledge and behavior, which is characteristic of data poisoning attacks.


                                      68. Frage
                                      A company is adopting AI and wants to create policies and procedures that include a structure for evaluating, publishing, and approving patterns for AI usage.
                                      Which of the following should the company establish to meet this goal?

                                      Antwort: D

                                      Begründung:
                                      Basic Concept: Successful AI adoption at an organizational level requires a centralized governance body that standardizes AI practices, promotes best practices, and ensures consistent, safe, and effective AI deployment across the organization. CompTIA SecAI+ Study Guide covers AI organizational governance structures under Domain 4.
                                      Why A is Correct: An AI Center of Excellence (CoE) is an organizational unit specifically designed to govern, standardize, and advance AI adoption. It develops and publishes policies, creates approved patterns for AI usage, evaluates new AI use cases, provides expert guidance, and maintains governance oversight. The CoE exactly matches the described need for a structure to evaluate, publish, and approve AI usage patterns across the organization.
                                      Why B is Wrong: An AI legal affairs office focuses on legal compliance, intellectual property, and regulatory matters related to AI. While important for legal risk management, it does not fulfill the broader governance mandate of establishing and approving AI usage patterns and best practices across the organization.
                                      Why C is Wrong: An AI audit department conducts post-implementation reviews and compliance assessments of existing AI systems. It is a retrospective and oversight function rather than a proactive body for developing and approving AI usage patterns and policies.
                                      Why D is Wrong: An AI data science division is a technical team focused on building AI models and solutions. It is a development function rather than a governance structure designed to create policies, evaluate AI patterns, and provide cross-organizational oversight of AI adoption.


                                      69. Frage
                                      Users report that the output of a generative AI application seems unrelated to the prompts and contains offensive content. A security team investigates and determines that there was an on-path attack.
                                      Which of the following is the most likely attack method?

                                      Antwort: B

                                      Begründung:
                                      Basic Concept: An on-path (formerly man-in-the-middle) attack intercepts communication between two parties, allowing the attacker to read, modify, or inject content. In the context of a generative AI application, an on-path attack on the session between user and AI service can manipulate prompts being sent to the model or responses being returned to users. CompTIA SecAI+ covers AI-specific attack vectors under securing AI systems.
                                      Why B is Correct: Session hijacking involves an attacker taking control of an active user session by capturing or forging session tokens. In this attack, the attacker intercepts the communication channel between users and the AI application, allowing them to modify prompts sent to the model or replace legitimate model responses with offensive content. This explains why outputs seem unrelated to prompts and contain offensive material.
                                      Why A is Wrong: Application server hijacking involves gaining unauthorized control of the server hosting the application. While severe, this would typically manifest as complete service disruption or data exfiltration rather than targeted modification of individual user session content.
                                      Why C is Wrong: Domain hijacking involves unauthorized transfer of a domain name registration, redirecting all users to a different IP address. This would affect all users simultaneously and typically redirect to a completely different site rather than manipulating individual AI responses.
                                      Why D is Wrong: Model hijacking refers to attacks that steal or replicate an AI model, not to intercepting and modifying the communication between users and an existing model during active sessions.


                                      70. Frage
                                      ......

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