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| Section | Weight | Objectives |
|---|---|---|
| AI-Assisted Security | 24% | - Operational Use of AI
|
| Securing AI Systems | 40% | - Adversarial Defense
|
| AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
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NEW QUESTION # 74
For which of the following situations would a human-in-the-loop be most recommended?
Answer: D
NEW QUESTION # 75
A security administrator must provide access controls for AI systems to list tables.
Which of the following should the administrator implement?
Answer: D
Explanation:
Basic Concept: AI systems interact with different resource layers including models, data stores, and infrastructure. Controlling what data an AI system can access requires implementing access controls at the data layer. CompTIA SecAI+ Study Guide differentiates between model access, data access, and network access controls for AI systems.
Why D is Correct: Data access controls govern what data resources an AI system can interact with, including which databases, tables, and records it can read or modify. To control an AI system ' s ability to list database tables, the administrator must implement data access controls that define precisely which tables the AI can enumerate and query, following the principle of least privilege for data interactions.
Why A is Wrong: Agentic AI access refers to permissions granted to autonomous AI agents to perform actions and use tools. It is a broader concept about what an AI agent can do operationally rather than a specific data-layer access control mechanism.
Why B is Wrong: A Network Access Control List controls network traffic at the IP and port level, determining which hosts can communicate with which network resources. It operates at the network layer and cannot enforce fine-grained control over which database tables an AI system is allowed to list.
Why C is Wrong: Model access controls govern who and what can interact with the AI model itself - who can query it, update it, or access its parameters. This is distinct from data access, which controls what the model can read from data stores during operation.
NEW QUESTION # 76
A machine learning (ML) engineer is working with a security engineer to identify the best practices for securing a system with various AI models.
Which of the following actions should the engineers suggest?
Answer: B
Explanation:
Basic Concept: Securing AI systems requires a structured, end-to-end approach that addresses security at every phase of the AI model ' s lifecycle from data collection through training, testing, deployment, and ongoing monitoring. CompTIA SecAI+ Study Guide identifies the Model Development Life Cycle as the foundational framework for AI system security.
Why B is Correct: A secure Model Development Life Cycle (MDLC) integrates security practices at every stage of AI model development specifically tailored to ML workflows. It encompasses secure data handling, training data validation, model testing for adversarial robustness, secure deployment practices, and ongoing monitoring. Unlike generic software development lifecycles, the MDLC addresses ML-specific risks such as data poisoning, model drift, and adversarial attacks.
Why A is Wrong: Guardrail testing and security validation are important components of the MDLC but represent only the testing phase. They do not encompass the full lifecycle of security practices needed from data acquisition through production monitoring.
Why C is Wrong: Implementing comprehensive security architecture is a broad statement that describes an outcome rather than a specific actionable practice. It does not provide the structured, ML-specific guidance of an MDLC.
Why D is Wrong: A secure SDLC is designed for traditional software development and covers code security, testing, and deployment. While relevant to AI application development, it does not specifically address ML model-specific risks such as training data security, model integrity, and inference-time attacks.
NEW QUESTION # 77
Faculty members at a university are concerned about potential inherent bias and inconsistency in one department ' s AI plagiarism detection service.
Which of the following principles will most likely address their concerns?
Answer: A
Explanation:
Basic Concept: Responsible AI principles each address different aspects of trustworthy AI behavior. When stakeholders are concerned about both bias and inconsistency - specifically that the same or equivalent work might receive different treatment from the AI system - the principle of consistency is most directly relevant.
CompTIA SecAI+ covers responsible AI principles under governance.
Why C is Correct: Consistency in AI systems means the model applies the same rules, standards, and decision criteria uniformly across all inputs and user groups without variation based on characteristics unrelated to the task. An AI plagiarism detection system that produces inconsistent results across different student submissions or demographic groups fails the consistency principle, which directly addresses both the bias concern (differential treatment) and inconsistency concern the faculty have raised.
Why A is Wrong: Transparency relates to openness about how the AI system works and what data it uses.
While valuable for understanding the system, transparency alone does not ensure that the system applies its rules uniformly or consistently.
Why B is Wrong: Explainability means the system can articulate why it made a particular decision. While useful for understanding individual cases, it does not guarantee that decisions are made with equal consistency across different submissions or groups.
Why D is Wrong: Accountability identifies who is responsible for AI system decisions and outcomes. It is a governance principle about ownership and responsibility rather than about ensuring uniform application of evaluation criteria.
NEW QUESTION # 78
A security administrator wants to prevent prompt injection attacks and ensure responses have sanitized output.
Which of the following provides a primary compensating control for these requirements?
Answer: B
Explanation:
Basic Concept: Preventing prompt injection and ensuring output sanitization requires a control that can inspect both the semantic content of incoming prompts and the safety of outgoing responses. This requires an intelligent, context-aware filtering layer specifically designed for LLM traffic. CompTIA SecAI+ Study Guide identifies LLM firewalls as a primary control for prompt security and output safety.
Why C is Correct: An LLM firewall is specifically designed to inspect, filter, and sanitize both incoming prompts and outgoing AI responses. It can detect and block prompt injection attempts using pattern matching, semantic analysis, and behavioral heuristics, while also sanitizing output to remove sensitive data, harmful content, or policy violations before responses reach users. This dual capability makes it the primary control addressing both requirements simultaneously.
Why A is Wrong: Least privilege restricts what resources and actions users and systems can access. It reduces the potential impact of successful attacks but does not inspect prompt content for injection attempts or sanitize model outputs.
Why B is Wrong: Encryption protects data confidentiality in transit and at rest. It does not analyze prompt content for malicious patterns or filter AI-generated responses for unsafe content. Encrypted traffic can still carry prompt injection attacks.
Why D is Wrong: Rate limiting controls request frequency. While it can slow down automated injection attack campaigns, it does not inspect the content of individual prompts to detect injections, nor does it sanitize output responses. Malicious prompts can still succeed within rate limits.
NEW QUESTION # 79
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