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| Section | Weight | Objectives |
|---|---|---|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Securing AI Systems | 40% | - Adversarial Defense
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI and Machine Learning Fundamentals
|
| AI-Assisted Security | 24% | - Operational Use of AI
|
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NEW QUESTION # 34
Instructions: Click the (+) to assign each threat category into its appropriate framework.
An architect is modeling an agentic system to meet security standards.
Answer:
Explanation:
See Explanation below for complete solution for this PBQ.
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a simulation item requiring interactive drag- and-drop assignment of threat categories to appropriate frameworks in the actual exam. It tests knowledge of how different AI threat frameworks categorize and address specific threat types for agentic systems.
Key Concept - Framework-to-Threat Mapping: MITRE ATLAS covers ML-specific adversarial tactics such as model evasion, data poisoning, model extraction, and prompt injection for agentic systems. OWASP LLM Top 10 addresses application-level LLM vulnerabilities such as insecure output handling, excessive agency, and supply chain risks. NIST AI RMF addresses governance-level risks across the AI lifecycle. STRIDE addresses architectural threats including spoofing, tampering, repudiation, information disclosure, DoS, and elevation of privilege.
Why This Matters: Agentic AI systems have a unique threat landscape combining traditional software vulnerabilities with AI-specific attacks. Correctly mapping threat categories to frameworks is essential for comprehensive threat modeling of systems that autonomously execute multi-step tasks with tool access and real-world consequences.
Reference: CompTIA SecAI+ Study Guide Domain 4 covers AI governance frameworks and their specific threat categories. Candidates should understand the scope and focus areas of MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF, and traditional security frameworks as they apply to agentic AI system security modeling.
NEW QUESTION # 35
An engineer is analyzing findings from a penetration test that indicate insufficient data encryption. The report also indicates that additional controls must be placed on the data. To protect against loss of intellectual property, the engineer must implement data security.
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.
Answer:
Explanation:
Explanation:
Part 1 - Select the protocol or cipher
System component
Select
API gateway
TLS 1.2
Database storage
CAMELLIA
AI model storage
HMAC-SHA512
Part 2 - Select the data-protection technique
Enter the techniques from the first row to the fifth row in this exact order:
Row Data modification Select
1 The name field is removed, while patient_id and dob remain De-identification
2 ip_addr and name are removed, leaving cookie and uid Anonymization
3 The card number is replaced by 0x0193828829 Tokenization
4 sensitivity: " SECRET " is added Classification
5 Part of the PIN is replaced by XXXX Masking
See Explanation
Why these are the intended answers
API gateway # TLS 1.2
The API gateway handles data moving between the client and backend systems. TLS is the only listed transport-security protocol and therefore protects the data in transit . OWASP recommends encrypting sensitive data in transit by using TLS with secure parameters.
Database storage # CAMELLIA
CAMELLIA is a symmetric block cipher suitable for encrypting substantial quantities of stored data. It is therefore the valid storage-encryption choice among the listed options.
Do not select AES-512 . AES-512 is not a standardized AES variant. NIST defines only AES-128, AES-192, and AES-256 .
AI model storage # HMAC-SHA512
The scenario separately emphasizes protection against loss or modification of intellectual property. HMAC- SHA512 supplies message authentication and integrity verification, allowing unauthorized modification of the AI model to be detected. HMAC is not confidentiality encryption, but it is the most appropriate listed additional control for protecting model integrity. OWASP specifically identifies HMACs as cryptographic controls for stored-data integrity.
Why the other options are not selected
* SHA-2 512: Hashing only; no secret key and no encryption.
* Elliptical curve: A family of cryptographic techniques, not a specific storage or transport protocol.
* ECDH: Used for key agreement, not direct bulk-data encryption.
* AES-512: Invalid AES key size.
* RSA-2048: Primarily used for asymmetric encryption, signatures, or key transport; inefficient for bulk database or model encryption.
* CAMELLIA: Appropriate symmetric encryption for stored data.
* HMAC-SHA512: Integrity and authenticity, not confidentiality.
Final Part 2 order
* De-identification
* Anonymization
* Tokenization
* Classification
* Masking
Explanation:
De-identification removes an explicitly identifying field-in this case, the person's name-while retaining other fields needed for processing. The remaining patient ID and date of birth could potentially permit re- identification when combined with other information, so this is de-identification rather than complete anonymization. OWASP describes de-identification as deleting, scrambling, or pseudonymizing direct and indirect identifiers.
Anonymization removes identifying attributes so that the displayed record is not directly tied to the named individual. Here, both the name and IP address are removed.
Tokenization substitutes a sensitive value with a non-sensitive representative token. The original payment- card number is replaced with the hexadecimal-style value 0x0193828829. OWASP recognizes tokenization as a method for reducing exposure of stored sensitive data.
Classification labels information according to sensitivity. Adding sensitivity: " SECRET " does not conceal or transform the PIN; it categorizes the record so appropriate controls can be applied. OWASP recommends classifying processed, stored, and transmitted data and applying controls based on that classification.
Masking obscures only part of a sensitive value while preserving its general format. Changing 999-99-9999 to 999-99-XXXX is partial masking.
NEW QUESTION # 36
An IT company implements an adaptable chatbot that learns from user prompts. Based on the conversation shown - where User 2 injected false information about a company acquisition that caused the chatbot to give incorrect responses to User 3 - which of the following compensating controls should an administrator implement to mitigate the issue?
Answer: C
NEW QUESTION # 37
A group of security engineers is developing a SIEM system that will be able to ingest data from multiple structured and unstructured sources, have a chatbot integrated with an LLM that the security analyst can interact with, and provide insights from the SIEM alert data.
Which of the following techniques should the security engineers consider before collecting the data from the respective sources?
Answer: C
Explanation:
Basic Concept: Before ingesting data from multiple sources - both structured and unstructured - into an AI- powered SIEM system, the data must be prepared to ensure quality, consistency, and usability. Data from diverse sources often contains noise, errors, duplicates, and formatting inconsistencies that will degrade AI performance if not addressed. CompTIA SecAI+ covers data preparation as a prerequisite for AI system effectiveness.
Why C is Correct: Data cleansing is the process of detecting and correcting or removing corrupt, inaccurate, incomplete, and duplicate data. Before collecting data from multiple structured and unstructured SIEM sources, engineers must cleanse the data to standardize formats, remove duplicates, fill missing values, and eliminate noise. Clean input data is fundamental to producing accurate AI-generated insights and reliable LLM interactions in the SIEM context.
Why A is Wrong: Balancing addresses class distribution imbalance in labeled training data for classification models. While relevant when training ML detection models, it is not the primary consideration before initial data collection from diverse SIEM sources.
Why B is Wrong: Verification confirms that data meets expected quality standards and validates its accuracy against trusted sources. It is a post-collection quality check performed after cleansing, not the first step before data collection.
Why D is Wrong: Vector storage refers to databases that store embeddings for semantic search, relevant for RAG systems. It is a storage architecture decision made after data is collected, processed, and prepared, not a pre-collection technique.
NEW QUESTION # 38
Which of the following types of prompts best describes a developer's input that informs AI interactions?
Answer: B
Explanation:
Option A is correct because a system prompt contains the developer- or platform-defined instructions that establish how the AI should behave throughout an interaction. It can define the model's role, objectives, prohibited actions, response style, tool-use boundaries, and security constraints before the user submits a request. That description matches an input supplied by a developer to govern later AI interactions. Option B is the end user's prompt or request; it expresses what the user wants in a particular turn and normally has lower authority than system-level instructions. Option C, zero-shot prompting, describes asking the model to perform a task without providing an example. Option D, multi-shot prompting, supplies multiple examples to demonstrate the desired input or output pattern. Neither zero-shot nor multi-shot identifies the authority or source of the instruction. In a secure AI application, system instructions should be protected from unauthorized modification, tested for conflicts, and reinforced with technical controls rather than treated as the sole security boundary. The essential distinction is prompt hierarchy: system instructions configure persistent behavior, while user prompts request individual tasks.
NEW QUESTION # 39
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