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| Section | Weight | Objectives |
|---|---|---|
| Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures |
| Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security - Explain key aspects of digital forensics - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Architecture and Design | 21% | - Summarize basics of cryptographic concepts - Explain the importance of physical security controls - Summarize authentication and authorization design concepts - Explain the importance of security concepts in an enterprise environment - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators associated with network attacks - Compare and contrast types of social engineering attacks - Explain penetration testing concepts - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators to determine the type of attack |
| Implementation | 25% | - Given a scenario, implement secure systems design - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure host settings - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement identity and account management controls - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts |
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NEW QUESTION # 38
Which log type is MOST useful for detecting DNS tunneling?
Answer: B
Explanation:
DNS logs reveal abnormal query lengths and frequencies typical of tunneling.
NEW QUESTION # 39
A security analyst is aware of an active penetration test in the environment. The analyst examines security information and event management (SIEM) log data and notices the following output from the AI system:
Which of the following is the vulnerability that has occurred and the control the analyst should implement?
Answer: D
Explanation:
The log data reveals personally identifiable information (PII) such as name, address, and a full credit card number. This represents a sensitive information disclosure vulnerability. The appropriate control is data masking, which protects sensitive data in logs and outputs while still allowing necessary system monitoring.
NEW QUESTION # 40
Which of the following provides guidance on AI-specific compliance?
Answer: D
Explanation:
Basic Concept: Different regulatory and standards bodies address different aspects of technology governance.
For AI-specific compliance guidance that addresses the unique characteristics of AI systems including transparency, fairness, accountability, and societal impact, a framework specifically designed for AI is required. CompTIA SecAI+ Study Guide identifies OECD as a key source of AI-specific compliance guidance.
Why A is Correct: The OECD AI Principles and Recommendation on AI provide internationally recognized, AI-specific guidance on compliance with responsible AI values including transparency, accountability, robustness, security, safety, and human-centric values. The OECD has developed a dedicated framework specifically addressing the compliance considerations unique to AI systems across sectors and national boundaries, making it the most AI-specific compliance guidance option listed.
Why B is Wrong: ISO 27001 is a general information security management standard addressing broad organizational security controls. It is not AI-specific and does not address the unique compliance considerations of AI transparency, fairness, or algorithmic accountability.
Why C is Wrong: PCI DSS is a payment card industry security standard focused on protecting payment card data. It has no AI-specific compliance provisions and is limited to financial transaction security requirements.
Why D is Wrong: GDPR is a European data protection regulation focused on personal data privacy rights and obligations. While relevant to AI systems that process personal data, GDPR is a privacy regulation rather than AI-specific compliance guidance addressing the full spectrum of AI governance considerations.
NEW QUESTION # 41
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:
Basic Concept: Different ML model architectures are optimized for different data types and tasks.
Unstructured data such as images, raw network packet captures, and visual content requires models capable of automatically extracting hierarchical spatial features. CompTIA SecAI+ covers ML model selection for security tasks under basic AI concepts.
Why B is Correct: Convolutional Neural Networks (CNNs) are specifically designed for pattern recognition in unstructured data, particularly image and grid-structured data. CNNs use convolutional layers to automatically extract local and hierarchical features without requiring manual feature engineering. They excel at recognizing patterns in raw, unstructured inputs, making them the optimal choice for pattern recognition on unstructured datasets in cybersecurity contexts such as image-based malware analysis or visual traffic pattern recognition.
Why A is Wrong: Long Short-Term Memory (LSTM) networks are recurrent neural networks optimized for sequential and time-series data such as network traffic flows over time or log sequences. While they handle unstructured sequential data, they are not specifically designed for spatial pattern recognition in general unstructured data.
Why C is Wrong: Decision trees work on structured, tabular data with defined features. They require feature extraction and engineering before processing unstructured data and are not designed for raw pattern recognition in unstructured inputs.
Why D is Wrong: Logistic regression is a linear classification algorithm that requires structured, numerical input features. It cannot directly process unstructured data and requires extensive preprocessing and feature extraction, making it unsuitable for pattern recognition on raw unstructured datasets.
NEW QUESTION # 42
An administrator, who works for a financial institution, is required to implement data security controls for data at rest within AI systems that involve data disclosure. Which of the following is the most suitable control?
Answer: D
Explanation:
For financial institutions handling AI systems, protecting data at rest against disclosure requires encryption. Encryption ensures that even if the storage medium is accessed or compromised, the data remains unreadable without the proper decryption keys.
NEW QUESTION # 43
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