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| Section | Weight | Objectives |
|---|---|---|
| Implementation | 25% | - Given a scenario, implement identity and account management controls - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design - Given a scenario, implement secure network architecture concepts - Given a scenario, implement authentication and authorization solutions |
| Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls |
| Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Explain the security implications of embedded and specialized systems - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts - Explain the importance of security concepts in an enterprise environment - Summarize basics of cryptographic concepts |
| Operations and Incident Response | 16% | - Given a scenario, apply mitigation techniques or controls to secure an environment - Explain key aspects of digital forensics - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security - Summarize the importance of policies, processes, and procedures for incident response |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks |
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NEW QUESTION # 27
Instructions: Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.
Answer:
Explanation:
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a HOTSPOT/simulation item requiring interactive selection in the actual exam. It tests the candidate ' s ability to map appropriate security controls to AI system components such as API gateway, model endpoint, data layer, and authentication layer.
Key Concept - Appropriate Controls by Component: For an API gateway connecting an AI system, typical controls include API key authentication, rate limiting, TLS encryption, and input validation. For the model endpoint, controls include IAM role-based access, audit logging, and guardrails. For data access components, encryption at rest and data masking are appropriate. For the authentication layer, MFA and expiring session tokens are relevant.
Why This Matters: The CompTIA SecAI+ Study Guide emphasizes defense-in-depth for AI system integration, ensuring each architectural layer has dedicated, appropriate security controls. The principle of least privilege should guide access control assignments at each component, while availability controls such as rate limiting protect against abuse.
Reference: CompTIA SecAI+ Exam Objectives Domain 2 (Securing AI Systems) covers AI system component security controls. Candidates should study the mapping of controls to infrastructure components including API gateways, model serving endpoints, data stores, and identity management layers. In the live exam, select the most specific and directly relevant control for each component based on the component ' s function and risk profile.
NEW QUESTION # 28
An attacker successfully completes a denial-of-service (DoS) attack through the context window of an AI system. Thousands of characters are obfuscated and hidden behind an emoji. Which of the following techniques best mitigates this type of attack?
Answer: A
Explanation:
A DoS attack through the context window relies on overwhelming the model with excessive or obfuscated input. Prompt filtering prevents such malicious or oversized inputs from being processed, ensuring that the model only receives safe, properly structured data within acceptable limits.
NEW QUESTION # 29
After the latest software update, a developer receives reports that the system no longer requires reauthentication to display account balances because this issue was present in a previous release. Which of the following should the developer do to best mitigate the risk of recurrence?
Answer: B
Explanation:
Option B is correct because the defect is a regression: a security behavior that should require reauthentication was previously corrected but reappeared after a software update. Automated AI-assisted regression testing in the CI/CD pipeline can repeatedly verify that protected account-balance functions still demand reauthentication before a release is promoted. The test should reproduce the prior defect, fail the build when authentication is bypassed, and preserve the case as a permanent security test. Option A adds an approval step, but an approver may not detect the reintroduced behavior without an executable test. Option C improves release scheduling and traceability but does not prove that security controls still function. Option D uses SAST to inspect source code for known weakness patterns; it may identify some authentication flaws, but it does not reliably validate the end-to-end runtime behavior described here. Regression testing is the direct control for preventing recurrence. The NIST AI Risk Management Framework emphasizes testing AI systems before deployment and regularly during operation, supporting automated security validation within the delivery pipeline.
NEW QUESTION # 30
A security operations center (SOC) analyst needs to automate multiple security tasks by breaking them down into smaller parts.
Which of the following AI tools is the best for this task?
Answer: D
Explanation:
Basic Concept: Modern security operations require automation of complex, multi-step workflows. Different AI architectures have different capabilities. Understanding which AI type is best suited for task decomposition and autonomous execution is fundamental to AI-assisted security operations. CompTIA SecAI+ covers agentic AI capabilities under AI-assisted security.
Why A is Correct: Agentic AI systems are specifically designed to autonomously plan, decompose complex tasks into subtasks, execute multi-step workflows, use tools and APIs, and adapt their approach based on intermediate results. For a SOC analyst needing to automate multiple security tasks as a series of smaller coordinated steps, agentic AI is the ideal architecture as it can orchestrate an entire workflow including threat hunting, alert investigation, log analysis, and response actions.
Why B is Wrong: RAG AI enhances language model responses by retrieving relevant documents from a knowledge base. While useful for answering questions with current information, it is not designed for autonomous multi-step task execution or workflow automation.
Why C is Wrong: Generative AI creates content based on prompts including text, code, and summaries. While it can assist with individual tasks, it requires continuous human prompting for each step rather than autonomously breaking down and executing complex multi-step security workflows.
Why D is Wrong: A chatbot is a conversational interface designed for question-answering or guided dialogue.
It responds reactively to user input rather than proactively planning and executing multi-step automated security workflows.
NEW QUESTION # 31
Which of the following would most likely be used to prove that an image is AI generated?
Answer: C
Explanation:
Watermarking embeds hidden, verifiable markers into AI-generated images. These markers can later be detected to prove the image originated from an AI system, making it the most reliable method for verification.
NEW QUESTION # 32
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