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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Summarize regulations, standards, and frameworks that impact organizations - Compare and contrast various types of security controls |
| Topic 2: Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems - Summarize virtualization and cloud security concepts - Explain the importance of physical security controls - Explain secure application development, deployment, and automation concepts - Explain the importance of security concepts in an enterprise environment |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with application attacks - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks |
| Topic 4: Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure mobile device policies - Given a scenario, implement identity and account management controls - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure systems design - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure host settings |
| Topic 5: Operations and Incident Response | 16% | - 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 - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment |
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NEW QUESTION # 24
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: A
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 # 25
A company introduces a large language model (LLM) in an application in order to monitor for a potential denial-of-service attack.
Which of the following should the company use to measure the utilization of the LLM?
Answer: B
Explanation:
Basic Concept: LLMs process and generate text as discrete units called tokens, which represent words or word pieces. The number of tokens consumed is the primary metric for LLM resource utilization and billing.
Understanding token consumption is essential for both performance monitoring and cost management.
CompTIA SecAI+ Study Guide covers token-based measurement under AI system management.
Why A is Correct: Tokens are the fundamental unit of measurement for LLM utilization. Both input tokens sent in prompts and output tokens generated in responses are counted. Token consumption directly correlates with computational resource usage, response time, and API cost. Monitoring token counts enables the company to establish usage baselines and detect the abnormal token consumption spikes associated with DoS attacks or denial-of-wallet attacks against the LLM.
Why B is Wrong: A transformer is the neural network architecture that underlies modern LLMs. It is an architectural description, not a measurable utilization metric. You cannot quantify LLM resource consumption in " transformers. " Why C is Wrong: Chain of thought is a prompting technique that encourages models to reason step-by-step before providing answers. It is an inference strategy for improving response quality, not a unit of measurement for monitoring LLM utilization or resource consumption.
Why D is Wrong: A prompt is the input text submitted to the LLM. While monitoring prompt volumes provides request count information, tokens provide a more granular and meaningful utilization metric because they capture both the complexity of inputs and the length of generated outputs.
NEW QUESTION # 26
A disgruntled employee changed the company policies that a chatbot references in order to create confusion and disrupt the business.
Which of the following AI-generated vulnerabilities is the employee exploiting?
Answer: A
Explanation:
Basic Concept: AI systems that rely on knowledge bases, vector databases, or reference documents are vulnerable to attacks that corrupt or manipulate that source data. When an adversary deliberately modifies the data an AI uses, this is a form of data poisoning. CompTIA SecAI+ Study Guide covers data poisoning as a core AI vulnerability.
Why C is Correct: Data poisoning is an attack where an adversary intentionally corrupts or manipulates the data that an AI system uses for training, inference, or reference. In this scenario, the employee modified the company policies document that the chatbot uses as its knowledge base, causing the chatbot to provide incorrect, misleading, or confusing information to users. This is a classic indirect data poisoning attack targeting the AI ' s reference data rather than its model weights.
Why A is Wrong: Data reduction refers to techniques that decrease the volume or dimensionality of data for processing efficiency. It is a data engineering concept, not an attack vector or vulnerability classification.
Why B is Wrong: Data masking replaces sensitive data values with anonymized equivalents to protect privacy. It is a data protection control used legitimately, not an attack that an employee would exploit to cause disruption.
Why D is Wrong: Data leaking involves unauthorized disclosure of sensitive information from an AI system or its associated data stores. The employee ' s action of manipulating data is an integrity attack, not a confidentiality violation involving leakage of data to unauthorized parties.
NEW QUESTION # 27
A company discovers that attackers exploited an unpatched vulnerability in a web server. Which control BEST prevents this?
Answer: B
Explanation:
Timely patching directly prevents exploitation of known vulnerabilities.
NEW QUESTION # 28
A line of business wants to onboard an application that uses a custom AI model for employee assessments. The Chief Information Officer (CIO) agrees to allow the engagement to proceed but first wants a threat model. Which of the following is the most appropriate to use for an AI threat model?
Answer: D
Explanation:
ATLAS is specifically designed for creating AI threat models. It catalogs adversarial techniques, tactics, and use cases that target AI systems, making it the most appropriate framework for assessing risks to a custom AI model in employee assessments.
NEW QUESTION # 29
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