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| Section | Weight | Objectives |
|---|---|---|
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Explain penetration testing concepts - Given a scenario, analyze potential indicators to determine the type of attack - Explain threat actor types and attributes |
| Implementation | 25% | - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure systems design - Given a scenario, implement secure mobile device policies - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, implement public key infrastructure (PKI) |
| Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience - Explain the security implications of embedded and specialized systems - Summarize virtualization and cloud security concepts - Explain secure application development, deployment, and automation concepts - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts |
| Governance, Risk, and Compliance | 14% | - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts |
| Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation |
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NEW QUESTION # 31
A security operations center (SOC) has a very high volume of logs and alerts. The manager proposes the implementation of machine learning (ML) system to help with triage. Which of the following tasks is most suitable?
Answer: B
Explanation:
Machine learning is best suited for analyzing large volumes of security data and distinguishing between true threats and false positives. By identifying and classifying alerts, the ML system helps the SOC prioritize incidents and reduce analyst workload.
NEW QUESTION # 32
A security administrator wants to prevent prompt injection attacks and ensure responses have sanitized output.
Which of the following provides a primary compensating control for these requirements?
Answer: A
Explanation:
Basic Concept: Preventing prompt injection and ensuring output sanitization requires a control that can inspect both the semantic content of incoming prompts and the safety of outgoing responses. This requires an intelligent, context-aware filtering layer specifically designed for LLM traffic. CompTIA SecAI+ Study Guide identifies LLM firewalls as a primary control for prompt security and output safety.
Why C is Correct: An LLM firewall is specifically designed to inspect, filter, and sanitize both incoming prompts and outgoing AI responses. It can detect and block prompt injection attempts using pattern matching, semantic analysis, and behavioral heuristics, while also sanitizing output to remove sensitive data, harmful content, or policy violations before responses reach users. This dual capability makes it the primary control addressing both requirements simultaneously.
Why A is Wrong: Least privilege restricts what resources and actions users and systems can access. It reduces the potential impact of successful attacks but does not inspect prompt content for injection attempts or sanitize model outputs.
Why B is Wrong: Encryption protects data confidentiality in transit and at rest. It does not analyze prompt content for malicious patterns or filter AI-generated responses for unsafe content. Encrypted traffic can still carry prompt injection attacks.
Why D is Wrong: Rate limiting controls request frequency. While it can slow down automated injection attack campaigns, it does not inspect the content of individual prompts to detect injections, nor does it sanitize output responses. Malicious prompts can still succeed within rate limits.
NEW QUESTION # 33
A healthcare company deploys an AI chatbot that implements retrieval-augmented generation (RAG) using the company ' s historical data set. The chatbot output contains patient information.
Which of the following is the most effective technique to mitigate this vulnerability?
Answer: B
Explanation:
Basic Concept: When an AI chatbot powered by RAG retrieves and outputs sensitive patient information such as names, medical histories, or identifiers, the risk of Protected Health Information (PHI) disclosure must be mitigated. CompTIA SecAI+ Study Guide covers data protection techniques for AI systems handling sensitive health data.
Why A is Correct: Masking replaces sensitive data values such as patient names, dates of birth, medical record numbers, and diagnoses with redacted or anonymized equivalents in the chatbot ' s output. Even if the RAG system retrieves records containing patient information, masking ensures that the sensitive fields are obscured before the response is presented to the user. This directly prevents PHI disclosure while allowing the chatbot to provide useful responses based on the underlying data patterns.
Why B is Wrong: Classification involves categorizing data by its sensitivity level such as public, internal, confidential, or restricted. While it identifies which data requires protection, classification alone does not transform or obscure the sensitive values in chatbot outputs.
Why C is Wrong: Data minimization is a privacy principle that limits data collection to only what is necessary for the specified purpose. While valuable as a design principle when building the RAG knowledge base, it is a data governance strategy rather than a technical output control that can be applied to mitigate existing PHI disclosure in chatbot responses.
Why D is Wrong: Normalization is a data processing technique that scales numerical values to a standard range or standardizes data formats. It is a preprocessing step for improving model training efficiency, not a data protection technique for preventing patient information disclosure in AI outputs.
NEW QUESTION # 34
Which of the following requires developers to harden infrastructure to protect AI systems?
Answer: A
Explanation:
Configuration standards define how infrastructure and systems must be securely set up and maintained. By following these standards, developers harden the environment that supports AI systems, reducing risks from misconfigurations and vulnerabilities.
NEW QUESTION # 35
Which of the following International Organization for Standardization (ISO) standards contains compliance requirements for building an AI management system?
Answer: B
Explanation:
Basic Concept: ISO develops management system standards for various organizational domains. For organizations building and managing AI systems in a structured, compliant manner, the appropriate ISO standard must specifically address the unique requirements of AI management systems including risk governance, lifecycle management, and accountability. CompTIA SecAI+ Exam Objectives cover ISO standards applicable to AI governance.
Why D is Correct: ISO 42001 (Artificial Intelligence Management System - AIMS) is the international standard specifically designed for building and managing AI management systems. It provides compliance requirements and guidance for establishing, implementing, maintaining, and continually improving an organization ' s AI management system, addressing AI-specific concerns including risk management, AI system governance, accountability, transparency, and continuous improvement for AI applications.
Why A is Wrong: ISO 20000 is the Information Technology Service Management (ITSM) standard covering IT service delivery processes, SLAs, incident management, and change management. It is not an AI management standard and does not address AI-specific governance requirements.
Why B is Wrong: ISO 27001 is the Information Security Management System (ISMS) standard addressing organizational information security risk management through controls and policies. While relevant to data security in AI systems, it does not contain requirements specifically for building an AI management system.
Why C is Wrong: ISO 27018 is a code of practice for protection of personally identifiable information (PII) in public cloud computing environments, extending ISO 27001 for cloud privacy. It addresses cloud PII protection rather than AI system management compliance requirements.
NEW QUESTION # 36
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