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| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Operations and Incident Response | 16% | - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Summarize the importance of policies, processes, and procedures for incident response |
| Architecture and Design | 21% | - Explain secure application development, deployment, and automation concepts - Explain the importance of physical security controls - Explain the security implications of embedded and specialized systems - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Summarize basics of cryptographic concepts |
| Implementation | 25% | - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure mobile device policies - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure systems design |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with application attacks - Explain threat actor types and attributes |
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NEW QUESTION # 99
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
Answer: C
Explanation:
Basic Concept: Reconnaissance is the information gathering phase of an attack. LLMs can be enhanced to perform more effective reconnaissance by giving them access to current, specific information beyond their training data cutoff. CompTIA SecAI+ covers AI augmentation techniques including RAG under AI-assisted security.
Why A is Correct: RAG enhances an LLM by connecting it to an external knowledge base or real-time data sources that it can query during inference. For reconnaissance purposes, a RAG-enabled LLM can access up- to-date organizational information, technical documentation, and intelligence feeds that go beyond its static training data. This makes the LLM significantly more capable for gathering current, targeted intelligence about specific organizations or infrastructure.
Why B is Wrong: Creating a web scraper is a basic data collection technique. While AI can help write scraper code, the scraper itself is a simple script that does not enhance the LLM ' s intelligence or reasoning capabilities for sophisticated reconnaissance.
Why C is Wrong: Instructing an AI assistant to query as an administrator is a prompt manipulation attempt.
An LLM cannot actually gain elevated permissions through a prompt instruction; this describes social engineering or privilege escalation via prompting, not a performance enhancement technique.
Why D is Wrong: Prompting a chatbot to describe naming patterns is a basic use of an existing LLM ' s knowledge. It does not strengthen or enhance the model ' s capabilities; it merely queries what the model already knows from training data, which may be outdated or generic.
NEW QUESTION # 100
A company launches an AI application to monitor cloud misconfiguration and compliance. The AI application is shutting down development servers and opening ports during a client demonstration. Which of the following actions should the company take to return to normal operations and prevent future issues?
Answer: B
NEW QUESTION # 101
A company develops an AI model to diagnose patients. Hospitals access the model through an integrated application programming interface (API). The security team performs a denial-of- service (DoS) attack via brute force on the model. Which of the following controls would have prevented this issue?
Answer: C
Explanation:
Rate limiting restricts the number of API requests within a specific timeframe, preventing brute- force attempts that can overwhelm the AI model and cause denial-of-service conditions.
NEW QUESTION # 102
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?
Answer: A
Explanation:
Basic Concept: Dynamic Application Security Testing (DAST) tests running applications by sending various inputs to discover vulnerabilities. AI can significantly enhance DAST by intelligently generating diverse, targeted test payloads that traditional tools might miss. CompTIA SecAI+ covers AI augmentation of security testing methodologies.
Why C is Correct: Payload creation is highly suitable for AI automation during DAST. AI can generate diverse, contextually appropriate attack payloads such as SQL injection strings, XSS vectors, command injection attempts, and format string exploits tailored to the specific application ' s behavior observed during testing. AI can learn from the application ' s responses to previous payloads and generate increasingly targeted inputs, discovering vulnerabilities more efficiently than static payload databases.
Why A is Wrong: DDoS attacks are volume-based attacks designed to overwhelm network or application infrastructure. Automating DDoS during DAST is inappropriate as it would disrupt service availability rather than discover application security vulnerabilities, and it is harmful to legitimate operations.
Why B is Wrong: Data poisoning is an attack targeting AI/ML model training data integrity. It is relevant to securing AI systems but is not a DAST technique for testing web or software application security vulnerabilities during dynamic testing.
Why D is Wrong: Threat modeling is a structured analysis process performed before development or testing to identify potential threats and design appropriate countermeasures. It is a planning activity, not an attack technique that can be automated during dynamic application security testing.
NEW QUESTION # 103
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:
Basic Concept: Data at rest refers to inactive data stored in databases or storage media. Protecting it from unauthorized disclosure is a fundamental data security principle covered in the CompTIA SecAI+ Study Guide under securing AI data pipelines.
Why C is Correct: Encryption protects data at rest by rendering it unreadable to unauthorized parties without the appropriate decryption key. In a financial institution with sensitive data, encryption at rest (e.g., AES-256) is the primary control against data disclosure. Even if storage media is physically compromised, encrypted data remains unintelligible. CompTIA SecAI+ Exam Objectives highlight encryption as the primary confidentiality control for stored AI data.
Why A is Wrong: Data lineage tracks the origin and movement of data throughout its lifecycle. It improves traceability and auditability but does not prevent unauthorized disclosure of data at rest.
Why B is Wrong: Rate limits control the number of API requests within a time period. They protect against abuse and denial-of-service scenarios, not data-at-rest confidentiality.
Why D is Wrong: Data masking replaces sensitive values with fictitious substitutes, useful during development or testing. For actual production data at rest in AI systems handling real financial records, encryption provides stronger and more comprehensive confidentiality.
NEW QUESTION # 104
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