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| Section | Weight | Objectives |
|---|---|---|
| Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators to determine the type of attack - 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 associated with application attacks - Explain threat actor types and attributes - Explain vulnerability scanning concepts |
| Architecture and Design | 21% | - Explain secure application development, deployment, and automation concepts - Summarize basics of cryptographic concepts - Explain the importance of security concepts in an enterprise environment - Explain the importance of physical security controls - Summarize authentication and authorization design concepts - Given a scenario, implement cybersecurity resilience - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems |
| Implementation | 25% | - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts - 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 |
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NEW QUESTION # 151
A security alert triggers an agentic system. An analyst notices the following payload in the logs. The alert includes multiple shell commands that are not typically run as part of any hardening:
Which of the following is the most effective control to implement?
Answer: C
Explanation:
Basic Concept: Agentic AI systems that execute shell commands based on model-generated output are vulnerable to prompt injection attacks where malicious actors craft inputs that cause the agent to run unauthorized commands. Input validation using allowlists is a critical defense mechanism. CompTIA SecAI+ Study Guide covers agentic AI security controls.
Why A is Correct: Adding logic that validates shell commands against an approved allowlist before execution is the most direct and effective defense. This ensures only pre-approved, safe commands can be executed regardless of what the agentic system ' s model generates, preventing malicious command injection from reaching the operating system. This principle of allowlist-based input validation is a foundational secure agentic AI control.
Why B is Wrong: Deprecating and retraining the model is a lengthy process that addresses root cause training issues but does not provide immediate protection against ongoing injection attacks in the current deployed system.
Why C is Wrong: Modifying the application to ignore a specific tag merely removes one attack surface while leaving the system vulnerable to other injection vectors. It is not a comprehensive defense.
Why D is Wrong: Using only approved libraries controls which code libraries the agentic system can call, but does not validate or restrict the shell commands generated by the model at runtime based on arbitrary user input.
NEW QUESTION # 152
A financial organization implements a new AI-based fraud detection system to flag suspicious transactions. A security analyst discovers that it occasionally blocks legitimate transactions.
Which of the following is the best recommendation?
Answer: B
Explanation:
Basic Concept: When an AI fraud detection model produces false positives (blocking legitimate transactions), this indicates the model ' s decision boundary is insufficiently calibrated. The model needs improved training data to better distinguish fraudulent from legitimate transactions. CompTIA SecAI+ covers model performance improvement under AI-assisted security.
Why A is Correct: Retraining the model with more data and recent transaction patterns directly addresses the root cause of false positives. Additional representative legitimate transaction data helps the model learn more accurate decision boundaries, reducing false positives while maintaining detection sensitivity for actual fraud.
This improves model accuracy without abandoning AI-based detection.
Why B is Wrong: Token usage and rate limits are cost and resource management controls for LLM APIs.
They have no relevance to improving the accuracy of a fraud detection ML model that incorrectly classifies legitimate transactions.
Why C is Wrong: Encrypting data and applying access controls are data security measures that protect confidentiality and integrity. They do not address model classification accuracy or the false positive problem in fraud detection.
Why D is Wrong: Rolling back to a traditional system abandons the capabilities of AI-based fraud detection.
The appropriate response to model performance issues is to improve the model through retraining, not to regress to less capable detection approaches.
NEW QUESTION # 153
Which of the following would most likely be used to prove that an image is AI generated?
Answer: B
Explanation:
Basic Concept: As AI-generated images become increasingly indistinguishable from authentic photographs, technical mechanisms are needed to definitively identify synthetic content. Embedding verifiable markers in AI-generated content at creation time provides a reliable provenance trail. CompTIA SecAI+ Study Guide covers digital content authentication and AI provenance controls.
Why D is Correct: Watermarking embeds invisible or visible identifying markers into AI-generated images at the point of creation. These watermarks can be cryptographic, steganographic, or perceptual in nature and survive compression, cropping, and other common image manipulations. When an image ' s provenance is questioned, watermark detection tools can analyze it to confirm AI generation. This is the primary technical mechanism for proving AI image origin as supported by the Content Authenticity Initiative and C2PA standards.
Why A is Wrong: Human validation relies on subjective visual inspection by experts to determine if an image appears AI-generated. Modern AI image generation has advanced to the point where human reviewers frequently cannot distinguish synthetic from authentic images, making this approach unreliable for definitive proof.
Why B is Wrong: Guardrails are input and output filtering controls for AI systems. They restrict what content an AI can generate but do not create verifiable proof of an image ' s AI origin after generation.
Why C is Wrong: Diffusion refers to the diffusion model architecture used to generate images. It describes the creation process, not a verification mechanism. Knowing that diffusion models were used does not help prove a specific image was AI-generated after the fact.
NEW QUESTION # 154
An IT company implements an adaptable chatbot that learns from user prompts. Based on the conversation shown - where User 2 injected false information about a company acquisition that caused the chatbot to give incorrect responses to User 3 - which of the following compensating controls should an administrator implement to mitigate the issue?
Answer: C
Explanation:
Basic Concept: A chatbot that learns from user prompts is vulnerable to data poisoning through conversational injection. Malicious users can deliberately introduce false information that the chatbot incorporates into its knowledge, corrupting responses for subsequent users. CompTIA SecAI+ Study Guide identifies this as a real-time data poisoning vector requiring guardrail controls.
Why D is Correct: Guardrails prevent the chatbot from accepting and incorporating unverified, irrelevant, or potentially malicious information injected by users. They enforce boundaries on what the chatbot can learn from user interactions, validate that information aligns with the system ' s purpose and known facts, and block outputs based on poisoned knowledge. Guardrails are specifically designed to prevent the type of conversational data poisoning demonstrated where a user ' s false claim corrupted the model ' s subsequent responses.
Why A is Wrong: Data encryption protects the confidentiality of data in transit and at rest. It does not prevent a chatbot from accepting and acting on false information that users deliberately inject into the conversation.
Why B is Wrong: API rate limiting restricts the frequency of requests. While it can limit the number of poisoning attempts a single user can make, it does not prevent the chatbot from learning from and propagating false information when requests are made at an acceptable rate.
Why C is Wrong: Transfer learning is a training technique that adapts knowledge from one domain to another.
It is a model development approach, not a runtime control that prevents users from injecting false information into a deployed chatbot.
NEW QUESTION # 155
Customer feedback for an AI chatbot has a high-rate of non-answers, which is causing higher central processing unit (CPU) utilization.
Which of the following should be implemented?
Answer: A
Explanation:
Basic Concept: AI chatbots that generate non-answers - responses that do not actually address user questions
- consume CPU resources for processing without delivering value. This can indicate the model is attempting to generate responses for queries outside its knowledge domain or confidence threshold. CompTIA SecAI+ Study Guide covers AI performance optimization and response quality management.
Why B is Correct: Implementing a response confidence level threshold allows the chatbot to recognize when it lacks sufficient confidence to provide a meaningful answer and respond accordingly, either with a helpful redirect or a clear indication that it cannot answer the query. This reduces the costly processing cycles spent generating poor-quality non-answers, lowers CPU utilization from failed response generation, and improves customer experience by setting appropriate expectations rather than returning unhelpful responses.
Why A is Wrong: Guardrails filter content for safety and policy compliance. They prevent harmful or out-of- policy responses but do not address the underlying issue of the model generating low-confidence non-answers to legitimate customer queries.
Why C is Wrong: Prompt logging records user inputs for analysis and auditing. While useful for identifying what types of questions cause non-answers, logging alone does not solve the problem or reduce CPU utilization from failed response generation.
Why D is Wrong: Cost monitoring tracks AI system expenditure. It can identify that costs are high due to excessive CPU usage but does not implement a solution to reduce the non-answer rate or improve response generation efficiency.
NEW QUESTION # 156
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