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| Section | Weight | Objectives |
|---|---|---|
| Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement secure network architecture concepts - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement identity and account management controls - Given a scenario, implement secure systems design - Given a scenario, implement secure mobile device policies |
| Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use data sources to support an investigation |
| Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with application attacks |
| Architecture and Design | 21% | - Summarize virtualization and cloud security concepts - Explain the importance of physical security controls - Explain the security implications of embedded and specialized systems - Explain the importance of security concepts in an enterprise environment - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Given a scenario, implement cybersecurity resilience - Summarize authentication and authorization design concepts |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures |
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質問 # 20
Which of the following corporate policies would a security architect most likely reference when configuring an AI system to conduct input filtering?
正解：B
解説：
Data governance is the most relevant corporate policy because input filtering depends on knowing what information may enter the AI system, how that information is classified, and which categories of sensitive or regulated data must be blocked, redacted, masked, or otherwise controlled. CompTIA SecAI+ specifically includes sensitive data governance under corporate AI policies and separately identifies data classification labels, redaction, masking, minimization, and related data-safety controls. A security architect can therefore translate the organization ' s data governance rules into technical input-filtering requirements, such as preventing credentials, customer PII, financial information, confidential intellectual property, or restricted datasets from being submitted to a model. A public-model usage policy governs whether and how employees may use externally hosted models, but it is narrower than the underlying data-handling rules. Third-party compliance evaluates external providers and contractual or regulatory obligations. Terms of use describe permitted user behavior but normally do not provide the detailed data-classification requirements required to implement technical filtering. Data governance is therefore the authoritative basis for determining what input the AI system should accept or reject.
質問 # 21
Which of the following are considered threat intelligence sources? (Choose two.)
正解：B、D
解説：
ISACs and OSINT provide structured and open-source threat intelligence feeds.
質問 # 22
A team of data scientists is ready to release a model for enterprise use. The team wants to protect the model from unintentional changes or tampering. Which of the following is the most appropriate action?
正解：A
解説：
Protecting the model from tampering requires controlled and auditable access. By integrating the model with an API secured by IAM roles, only authenticated and authorized users or systems can interact with it, ensuring integrity and preventing unauthorized changes.
質問 # 23
An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?
正解：A
解説：
Basic Concept: Data integrity ensures that data has not been tampered with, corrupted, or modified during storage or transmission. For AI training data that is procured from external sources, cryptographic integrity verification is essential to confirm the data arrived unmodified. CompTIA SecAI+ Study Guide covers data integrity controls for AI data pipelines.
Why A is Correct: Implementing checksums provides cryptographic verification of data integrity. A checksum or hash value such as SHA-256 is computed from the dataset at the source. The receiver computes the same hash and compares it to the provided value. Any modification to the data during transit or storage will produce a different hash, immediately detecting tampering or corruption. This is the most reliable, automated, and scalable strategy for ensuring the integrity of procured training data.
Why B is Wrong: Human evaluation can verify data quality and relevance but is impractical for verifying integrity across large datasets of medical records. Human reviewers cannot detect subtle bit-level corruption or intentional small modifications, and the process is not scalable.
Why C is Wrong: Querying the model tests model performance rather than verifying the integrity of the underlying training data. The model cannot tell you whether its training data was modified after collection or during procurement.
Why D is Wrong: Log monitoring tracks system activities and events over time. While useful for auditing access to data, it cannot retroactively confirm that data content has not been modified and does not provide cryptographic integrity guarantees.
質問 # 24
A data scientist asks about controls for public vs. private models used for training. A security agent responds by listing several factors to evaluate when making that determination. Which of the following is the most critical control?
正解：A
解説：
Regulatory requirements are the most critical consideration because they can establish mandatory restrictions on how organizational, personal, regulated, or otherwise sensitive information may be processed by AI systems. CompTIA SecAI+ places private versus public models directly within the corporate-policy portion of AI governance, risk, and compliance and connects those decisions with sensitive data governance, third-party evaluations, and regulatory obligations. A public model may send data to infrastructure controlled by an external provider, potentially creating concerns involving data residency, retention, confidentiality, processing purposes, and cross-border transfers. A private model can provide greater organizational control over these factors. Model security remains essential, but a technically secure architecture can still be noncompliant if it violates applicable legal or regulatory obligations. Applicability determines whether a model is suitable for the intended use case but is not a governing compliance control. Responsible AI principles provide important guidance around fairness, safety, transparency, privacy, and accountability, but binding regulatory obligations generally take precedence when determining permissible deployment conditions. Therefore, regulations are the controlling consideration when deciding whether public or private AI models are acceptable for sensitive training workloads.
質問 # 25
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