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| Section | Weight | Objectives |
|---|---|---|
| Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use data sources to support an investigation |
| Implementation | 25% | - Given a scenario, implement identity and account management controls - Given a scenario, implement secure network architecture concepts - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure host settings - Given a scenario, implement secure systems design - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement public key infrastructure (PKI) |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Explain threat actor types and attributes - Explain penetration testing concepts - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts |
| Architecture and Design | 21% | - Summarize virtualization and cloud security concepts - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Explain the importance of physical security controls - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts - Summarize authentication and authorization design concepts - Explain the importance of security concepts in an enterprise environment |
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NEW QUESTION # 94
A team of data scientists is ready to release a model for enterprise use. The team wants to protect the model from unintentional changes or tampering. Which of the following is the most appropriate action?
Answer: B
Explanation:
Protecting the model from tampering requires controlled and auditable access. By integrating the model with an API secured by IAM roles, only authenticated and authorized users or systems can interact with it, ensuring integrity and preventing unauthorized changes.
NEW QUESTION # 95
Which of the following is the most impactful security risk associated with the use of a generative AI chatbot?
Answer: D
Explanation:
Basic Concept: Generative AI chatbots interact with users in natural language and may access organizational knowledge bases, databases, or prior conversations. The conversational nature of these systems creates unique risks around sensitive information disclosure. CompTIA SecAI+ Study Guide ranks data leakage as the primary security concern for generative AI chatbots.
Why B is Correct: Data leakage occurs when a generative AI chatbot inadvertently reveals sensitive information including PII, confidential business data, intellectual property, training data, or system configurations in its responses. This can happen through prompt injection attacks, insufficient output filtering, or the model memorizing and reproducing sensitive training data. The impact is immediate, potentially irreversible, and can result in regulatory violations, competitive disadvantage, and reputational damage.
Why A is Wrong: Overly permissive access is a contributing factor that can exacerbate data leakage but is an access control design issue rather than the most directly impactful runtime risk of operating a generative AI chatbot.
Why C is Wrong: Weak encryption is a data protection concern for data in transit or at rest. While important, it is a configuration issue separate from the generative AI chatbot ' s core operational risks and is not specific to chatbot technology.
Why D is Wrong: Model validation ensures a model performs as expected before deployment. While important for quality assurance, it is a development lifecycle activity rather than an ongoing operational security risk associated with running a chatbot.
NEW QUESTION # 96
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 # 97
A penetration tester is assessing the controls of a deployed AI system that is designed to search and return the contents of files. The tester runs the following:
Which of the following is the best control to prevent abuse of the system?
Answer: C
Explanation:
The penetration test shows that the system accepts arbitrary commands like deleteBuckets or listPermissions, which could lead to privilege abuse. The most effective control is least privilege, ensuring the service account only has access to what is strictly necessary (e.g., reading files) and not sensitive operations like deleting buckets or altering permissions.
NEW QUESTION # 98
A security engineer needs to monitor an AI-based system for runtime operations. The engineer is mostly concerned about the visibility of internal activity.
Which of the following is the most appropriate monitoring solution?
Answer: B
Explanation:
Basic Concept: Monitoring an AI system ' s internal runtime behavior requires deep observability into what the system is doing at the code and function execution level, not just at the perimeter. CompTIA SecAI+ Study Guide addresses AI system observability and runtime monitoring under securing AI infrastructure.
Why D is Correct: Enabling stack call and debugging level traces at the function level provides the highest granularity of visibility into internal operations. This approach exposes what functions are called, in what order, with what inputs, and what is returned, offering genuine insight into the AI system ' s internal activity at runtime precisely as the engineer requires.
Why A is Wrong: A SIEM aggregates and correlates log and event data from multiple sources. While useful for security alerting, it does not inherently provide visibility into internal function-level operations of an AI model at runtime.
Why B is Wrong: A WAF with header logging monitors and filters HTTP traffic at the application boundary.
It captures external request and response data, not the AI system ' s internal runtime mechanics.
Why C is Wrong: Relying on vendor controls and monitoring prompt inputs is a passive, externally-focused approach. It provides no visibility into intermediate computations or internal operations within the AI model itself.
NEW QUESTION # 99
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