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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
| Topic 2: AI-Assisted Security | 24% | - Security Operations Enhancement
|
| Topic 3: Securing AI Systems | 40% | - AI System Protection
|
| Topic 4: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
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NEW QUESTION # 72
User experience is declining since the launch of a large language model (LLM) in internal networks. Which of the following should be the highest priority for the prompt engineers?
Answer: C
Explanation:
When user experience is declining after an LLM launch, the top priority for prompt engineers is quality control. Ensuring prompts produce accurate, relevant, and safe outputs directly improves usability and restores user trust.
NEW QUESTION # 73
A security administrator sees suspicious queries on AI logs.
Which of the following should the administrator implement to address this issue?
Answer: C
Explanation:
Basic Concept: Suspicious queries in AI system logs indicate that potentially malicious or policy-violating prompts are reaching the AI model. Proactively intercepting and filtering suspicious prompts before they are processed requires a prompt-level security control. CompTIA SecAI+ Study Guide identifies prompt firewalls as the appropriate control for blocking suspicious AI queries.
Why A is Correct: A prompt firewall analyzes incoming queries using a combination of pattern matching, semantic analysis, and policy rules to identify and block suspicious prompts before they reach the AI model.
It can detect prompt injection attempts, jailbreaking patterns, sensitive data extraction queries, and other suspicious prompt characteristics. By intercepting malicious prompts at the perimeter, it prevents them from influencing model behavior or extracting sensitive information.
Why B is Wrong: Data size controls limit the volume or size of data in requests. While controlling input size can prevent some attacks, it does not analyze the content or semantics of queries to detect suspicious patterns.
A small suspicious prompt can be just as harmful as a large one.
Why C is Wrong: Rate limiting controls the frequency of requests from a source. While it can slow down automated attack campaigns, it does not inspect query content for suspicious patterns and allows suspicious queries through as long as they are submitted below the rate threshold.
Why D is Wrong: Agentic AI is an AI architecture for autonomous multi-step task execution. It is a type of AI system, not a security control for filtering suspicious queries from an existing AI system ' s logs.
NEW QUESTION # 74
Part 1: Use drop-down menu to select the most appropriate protocol or cipher for each system component.
Part 2: Use the drop-down menu to select the most appropriate technique to apply to the modified data.
An engineer is analyzing findings from a penetration test that indicate insufficient data encryption. The engineer must implement data security.
Answer:
Explanation:
Explanation:
See Explanation
Basic Concept: This is a Performance-Based Question (PBQ) - a simulation requiring interactive protocol and cipher selection in the actual exam. It tests the candidate ' s ability to select appropriate encryption protocols for different AI system components and apply proper data security techniques for sensitive AI data.
Key Concept - Encryption by Component: For data in transit between AI components, TLS 1.3 is the current standard protocol. For API communications, HTTPS with TLS 1.3 and certificate pinning is appropriate. For database connections used by AI systems, TLS with strong cipher suites such as AES-256-GCM should be applied. For data at rest in model stores and training data repositories, AES-256 encryption is standard. For authentication tokens and keys, RSA-2048 or ECC P-256 are appropriate asymmetric options.
Key Concept - Data Techniques: For sensitive AI training data that has been modified, tokenization replaces sensitive values with non-sensitive tokens. Masking obscures sensitive fields in outputs. Hashing provides one-way verification for data integrity.
Reference: CompTIA SecAI+ Study Guide Domain 2 covers encryption requirements for AI system data security. Candidates should know standard encryption protocols (TLS 1.3, AES-256), when to apply each cipher type, and which data security techniques such as masking, tokenization, and encryption at rest are appropriate for different AI system data categories and sensitivity levels.
NEW QUESTION # 75
Which of the following is the primary security risk when deploying AI models in production?
Answer: A
Explanation:
Basic Concept: When AI models are deployed in production, they interact with real data including sensitive business information, personal data, and confidential records. The intersection of AI capabilities and sensitive data creates significant security risks. CompTIA SecAI+ Exam Objectives identify data exposure as the primary production security risk for AI deployments.
Why D is Correct: Data exposure is the primary security risk in production AI deployments. AI models in production process sensitive data through queries and responses, and vulnerabilities such as prompt injection, model inversion attacks, insecure output handling, and misconfigured access controls can expose confidential training data, user PII, proprietary information, or system credentials. The consequences include regulatory violations, legal liability, and reputational damage, making data exposure the most critical ongoing security concern.
Why A is Wrong: GPU acceleration is a performance optimization technique that uses graphics processors for faster AI computation. While hardware security is important, GPU acceleration itself is not a security risk - it is a performance feature that does not inherently expose data.
Why B is Wrong: Model overfitting is a model quality issue where a model performs poorly on new data after memorizing training data too specifically. While it can indirectly contribute to data memorization, it is primarily a performance and generalization concern during development rather than a primary production security risk.
Why C is Wrong: Model encryption is a security control used to protect AI model weights from unauthorized access, not a risk itself. Framing a protection mechanism as a primary risk conflates controls with threats.
NEW QUESTION # 76
A security analyst finds that the AI system is under a denial-of-wallet attack. Which of the following should the analyst enforce to protect the company? (Choose two.)
Answer: D,F
Explanation:
API rate controls limit the number of requests within a set timeframe, preventing attackers from overloading the system and driving up costs.
Output token controls restrict the length of responses, reducing unnecessary token usage that attackers might exploit in a denial-of-wallet attack.
NEW QUESTION # 77
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