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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Securing AI Systems | 40% | - AI System Protection
|
| Topic 2: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| Topic 4: AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
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NEW QUESTION # 91
A SOC analyst identifies that a user extracted the full system prompt from the company ' s chatbot by prompting it to repeat the last query and provide the entire conversation context. Which of the following mitigations reduces the risk to the AI system?
Answer: A
Explanation:
Basic Concept: System prompt extraction is an attack where users manipulate an LLM into revealing its confidential system instructions. This violates the confidentiality of proprietary prompts and can expose security controls and business logic to adversaries. CompTIA SecAI+ Study Guide identifies guardrails as the primary control for preventing system prompt disclosure.
Why C is Correct: Enhancing model guardrails can specifically include instructions and filters that prevent the model from revealing its system prompt contents, regardless of how users attempt to extract them. Guardrails can detect and block attempts to retrieve conversation history, repeat system-level instructions, or disclose confidential operational context. This directly addresses the demonstrated attack where the user prompted the chatbot to reveal its entire context including the system prompt.
Why A is Wrong: Restricting the LLM ' s access to internal services limits what external resources the model can query. While this reduces the potential impact of system compromise, it does not prevent the model from disclosing its own system prompt in response to carefully crafted user queries.
Why B is Wrong: Data version control tracks changes to datasets and documents over time. It is a data management tool that does not inspect or control what the model discloses in its conversational responses to users.
Why D is Wrong: Segregating and identifying external content is relevant for preventing prompt injection from external data sources. It does not directly prevent a user from successfully prompting the model to reveal its own internal system instructions.
NEW QUESTION # 92
A customer using a travel chatbot reports that the chatbot responds with the following:
def choose_location(location):
if location == " United States of America " :
print( " You have selected an authorized travel destination " )
elif location == " Europe " :
print( " You have selected an unauthorized travel destination " )
Which of the following remediates this issue?
Answer: B
Explanation:
Deploying data loss prevention (DLP) is the best answer because the chatbot has exposed internal application logic rather than returning only the information intended for the customer. This represents a form of sensitive information disclosure , which CompTIA SecAI+ specifically identifies as an AI-system attack or failure condition. The objectives also emphasize data security controls such as classification, redaction, masking, and minimization to prevent inappropriate disclosure.
A DLP control can inspect generated responses for source code, confidential identifiers, credentials, internal business logic, or other restricted information and prevent that content from being returned externally.
Retraining the model to eliminate bias is unrelated because no discriminatory or statistically skewed behavior is shown. Secure output handling is important when model-generated output is consumed by downstream applications and could lead to injection or execution vulnerabilities. Here, however, the immediate issue is disclosure of internal information directly to a user. Cookies also do not address the problem; they are associated with maintaining web sessions and are not a general mitigation for server-side request forgery.
Therefore, DLP most directly addresses the exposed information.
NEW QUESTION # 93
Which of the following certifies an organization in the use of responsible AI?
Answer: B
Explanation:
ISO/IEC 42001 is the correct answer because it is an Artificial Intelligence Management System (AIMS) standard that organizations can implement to demonstrate structured and responsible governance of AI.
CompTIA SecAI+ explicitly includes ISO AI standards within the governance, risk, and compliance domain and expects candidates to distinguish standards from regulatory frameworks. ISO/IEC 42001 specifies requirements for establishing, implementing, maintaining, and continually improving an AI management system. Its scope includes responsible AI use, governance, risk management, accountability, transparency, and organizational controls. ISO describes the standard as applicable to organizations that develop, provide, or use AI systems and specifically identifies responsible AI and AI governance among its benefits. GDPR is legally binding privacy legislation rather than an AI management-system certification. The EU AI Act is also legislation and establishes regulatory requirements based on AI risk. NIST AI RMF provides voluntary risk- management guidance but is not itself an organizational certification standard. Therefore, ISO/IEC 42001 is the option specifically designed to support certifiable organizational AI-management practices.
NEW QUESTION # 94
Which of the following is the primary purpose of validating data for an AI system?
Answer: A
Explanation:
Validating data ensures quality, consistency, and fairness in training sets, helping prevent biased or inaccurate results in AI system outputs.
NEW QUESTION # 95
Which of the following improves the observability and auditing of an AI system?
Answer: C
Explanation:
Basic Concept: Observability in AI systems refers to the ability to monitor, log, trace, and audit the behavior of AI models in production. MLOps is the operational discipline that establishes the processes, tooling, and practices for managing AI systems throughout their lifecycle. CompTIA SecAI+ Study Guide covers MLOps as a key mechanism for AI system transparency and auditability.
Why C is Correct: MLOps implements comprehensive monitoring, logging, versioning, and audit pipelines for AI systems. It provides observability through model performance tracking, data drift detection, prediction logging, lineage tracking, and audit trails. MLOps platforms enable organizations to understand what their AI models are doing, why they are making certain decisions, and how their behavior changes over time, directly improving observability and auditing.
Why A is Wrong: Redeploying a model is an operational action taken to restore a previous version or apply updates. It does not improve monitoring infrastructure, logging capabilities, or auditing frameworks for ongoing observability.
Why B is Wrong: Manual detection relies on human observation to identify issues. It is labor-intensive, inconsistent, and not scalable for AI systems processing high volumes of data. It does not provide systematic observability or comprehensive audit trails.
Why D is Wrong: Anomaly detection identifies unusual patterns in data or behavior. While useful as a monitoring component within an observability strategy, it is a single technique and does not encompass the full observability and auditing capabilities provided by a comprehensive MLOps implementation.
NEW QUESTION # 96
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