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| Section | Weight | Objectives |
|---|---|---|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| Securing AI Systems | 40% | - Adversarial Defense
|
| AI-Assisted Security | 24% | - Operational Use of AI
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
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NEW QUESTION # 41
Hotspot Question
Instructions
Use the drop-down menus to define two appropriate security controls for each component of the AI system. Each control may be used only once.
If at any time you would like to bring back the initial state of the simulation please click the Reset All button.
An engineer is deploying a new AI system and wants to integrate it into the core system through an API.
Answer:
Explanation:
Explanation:
Cloud control plane: IAM policies, Resource policies
Prompt firewall: Injection policies, Output monitoring
WAF: Connection rate limits, Input token validation
Front-end API + Model + Vector database: Guardrails, Input quota
API gateway: Load balancing, Authentication token validation
Cloud control plane relies on IAM and resource policies to enforce centralized identity and access management as well as usage restrictions.
Prompt firewall protects against malicious input via injection policies and ensures outputs are monitored for compliance and safety.
WAF controls connection rate to mitigate DoS and validates input tokens to stop unauthorized access attempts.
Front-end API, Model, and Vector database share guardrails to restrict unsafe behaviors and input quotas to prevent overuse, making them consistent and efficient security points.
API gateway enforces load balancing for availability and validates authentication tokens at the entry point, providing secure and scalable access.
NEW QUESTION # 42
SIMULATION
Instructions
Click the (+) to assign each threat category into its appropriate framework.
If at any time you would like to bring back the initial state of the simulation, please click the Reset All button.
An architect is modeling an agentic system to meet security standards.
Answer:
Explanation:
Explanation:
MAESTRO: Overreliance, Insecure plug-in design
OWASP Top 10: Broken access control, Identification and authentication failures OWASP Top 10 LLM: Prompt injection, Model denial of service STRIDE: Elevation of privilege, Repudiation, Supply chain vulnerabilities, Insecure design MAESTRO addresses AI-specific misuse such as excessive trust in outputs (overreliance) and unsafe plug-in design.
OWASP Top 10 maps to classic web threats: broken access control and authentication failures are key risks.
OWASP Top 10 LLM focuses on LLM-related threats like prompt injection and denial of service targeting the model.
STRIDE categories cover privilege escalation, repudiation (denying actions), supply chain risks, and insecure design flaws.
NEW QUESTION # 43
Before submitting code to the upstream code repository, a security administrator wants to implement the following:
* Comments describing the inputs, outputs, and purpose of code blocks
* Recommendations for code security
Which of the following should the administrator implement to address both requirements?
Answer: D
Explanation:
An AI-enabled IDE plug-in is the best choice because it operates directly in the developer ' s coding environment before code is submitted to the upstream repository. It can analyze source code while it is being created, generate or improve comments describing functions, inputs, outputs, and code-block purpose, and simultaneously provide security recommendations for potentially vulnerable or unsafe code. This directly addresses both requirements at the appropriate stage of the development workflow. CompTIA SecAI+ explicitly includes IDE plug-ins among AI-enabled tools and identifies code quality, linting, and vulnerability analysis as AI-assisted security use cases. A machine-learning code checker may identify defects but does not necessarily generate useful documentation. A CLI-based add-on could perform some analysis, but it is less naturally integrated into the developer ' s authoring workflow. A repository runner generally operates after code has reached a repository or pipeline. The IDE plug-in therefore provides the most immediate combination of documentation assistance and secure-coding guidance before submission.
NEW QUESTION # 44
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 # 45
Global IPs attempt logins against thousands of accounts with known breached credentials. What attack is occurring?
Answer: A
Explanation:
Credential stuffing uses leaked username-password pairs.
NEW QUESTION # 46
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