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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures |
| Topic 2: Attacks, Threats, and Vulnerabilities | 24% | - 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 to determine the type of attack - Given a scenario, analyze potential indicators associated with application attacks - Explain vulnerability scanning concepts - Explain threat actor types and attributes |
| Topic 3: Implementation | 25% | - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure systems design - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts |
| Topic 4: Operations and Incident Response | 16% | - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Topic 5: Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Explain the importance of security concepts in an enterprise environment - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Explain the importance of physical security controls |
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NEW QUESTION # 21
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: C
Explanation:
False positives occur when the AI model lacks sufficient or representative training data.
Retraining the model with more diverse and recent transaction patterns improves accuracy, reducing the chance of legitimate transactions being incorrectly flagged.
NEW QUESTION # 22
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 # 23
A cybersecurity engineer implements a large language model (LLM) tool to automate an incident management workflow. However, the output contains items that do not exist in the real world. Which of the following is a technique to address this issue?
Answer: A
Explanation:
The described problem is hallucination: the LLM is generating plausible-looking information that is not grounded in real-world facts. Retrieval-augmented generation is the best option because RAG supplements the model ' s prompt with information retrieved from an authoritative external knowledge source before the response is generated. This allows incident- management responses to be grounded in actual tickets, security documentation, threat intelligence, asset information, or other trusted records instead of relying exclusively on the model ' s internal parameters. CompTIA SecAI+ explicitly includes RAG, vector storage, and embeddings within its AI data concepts and also identifies hallucinations as an issue that organizations must monitor and audit. Watermarking helps identify AI-generated content but does not improve factual grounding. Output filtering can block prohibited or unsafe output patterns, but it cannot reliably supply missing factual information. Data rebalancing addresses uneven distributions in training datasets and is unrelated to grounding an LLM ' s incident-management responses. RAG therefore most directly reduces unsupported or fabricated information by giving the model relevant factual context at inference time.
NEW QUESTION # 24
An internal user enters a client credit card number into an internal generative machine learning (ML) model:
#User prompt: Customer Jane Doe has a new credit card that she wants to add to her account. The number is
5555-5555-5555-5555
Which of the following is the most effective way to prevent prompt injection attacks against a large language model (LLM)?
Answer: D
Explanation:
Basic Concept: Prompt injection occurs when malicious content embedded in user input manipulates an LLM
' s behavior, causing it to leak sensitive data, bypass restrictions, or execute unintended actions. Preventing such attacks requires mechanisms that inspect and filter content at the prompt level. CompTIA SecAI+ covers LLM-specific security controls extensively.
Why A is Correct: Guardrails are purpose-built controls that inspect, filter, and constrain both input prompts and output responses in LLM systems. They can detect sensitive data patterns such as credit card numbers, block prompt injection payloads, enforce content policies, and prevent the model from processing or outputting restricted information. Guardrails are the primary LLM-native defense against prompt injection as cited in the CompTIA SecAI+ Study Guide.
Why B is Wrong: Antivirus software detects known malware signatures in files and executables. It does not inspect or understand the semantic content of LLM prompts and cannot detect or block prompt injection attacks.
Why C is Wrong: A WAF operates at the HTTP layer inspecting web requests and responses against rule sets.
While it can block some patterns, it lacks the contextual intelligence to understand LLM prompt semantics and cannot prevent sophisticated injection attacks.
Why D is Wrong: Role-based access control manages who can access which resources. It controls authorization but does not inspect the content of prompts to prevent injection attacks once a user has legitimate access.
NEW QUESTION # 25
Which of the following is a risk addressed by responsible AI?
Answer: D
Explanation:
Basic Concept: Responsible AI is a governance framework addressing risks that arise from AI systems producing outcomes that are unfair, harmful, or contrary to human values. Different risk types fall under different governance domains - some under responsible AI, others under security or operational management. CompTIA SecAI+ Study Guide covers responsible AI risk categories under Domain 4.
Why C is Correct: Response bias occurs when an AI system ' s outputs are systematically skewed against certain groups, topics, or perspectives, reflecting biases embedded in training data or model design. This is a core risk addressed by responsible AI principles including fairness, non-discrimination, and explainability.
Responsible AI frameworks mandate bias detection, assessment, and mitigation to ensure AI responses treat all users and groups equitably.
Why A is Wrong: Model drift describes the degradation of model performance over time as the distribution of real-world data diverges from the training data distribution. While an important operational concern, model drift is primarily a technical performance risk managed through MLOps and monitoring practices, not a core responsible AI governance concern.
Why B is Wrong: Reputational loss is a business risk consequence that may result from various AI failures including biased outputs or privacy violations. It is an outcome or impact rather than a specific risk category that responsible AI frameworks directly address.
Why D is Wrong: Data poisoning is a security attack where adversaries corrupt AI training data to manipulate model behavior. This is a cybersecurity threat managed through security controls and data integrity protections rather than responsible AI ethical governance frameworks focused on fairness and accountability.
NEW QUESTION # 26
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