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| Section | Weight | Objectives |
|---|---|---|
| Architecture and Design | 21% | - Summarize authentication and authorization design concepts - Explain secure application development, deployment, and automation concepts - Summarize virtualization and cloud security concepts - Explain the importance of security concepts in an enterprise environment - Explain the security implications of embedded and specialized systems - Summarize basics of cryptographic concepts - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience |
| Implementation | 25% | - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure host settings - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure systems design - Given a scenario, implement authentication and authorization solutions - 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 |
| Operations and Incident Response | 16% | - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain penetration testing concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks - Given a scenario, analyze potential indicators associated with application attacks |
| Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures - Compare and contrast various types of security controls - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations |
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NEW QUESTION # 110
Which of the following describe the practice of providing examples in a prompt? (Choose two.)
Answer: C,D
Explanation:
Providing examples in a prompt is referred to as one-shot prompting when a single example is given and multi-shot prompting when multiple examples are provided.
NEW QUESTION # 111
A security analyst needs to conduct a security assessment of the output from an AI-enabled development tool.
Which of the following should the analyst do first?
Answer: D
Explanation:
Basic Concept: Security assessment of AI-generated code requires a systematic review of the code itself to understand what has been generated and identify potential vulnerabilities before remediation steps are taken.
Security assessments follow a structured methodology beginning with understanding the current state.
CompTIA SecAI+ Study Guide covers AI-generated code security assessment under AI-assisted security.
Why D is Correct: Performing a source code review is the first and most fundamental step in assessing AI- generated code security. Before removing secrets, enforcing access controls, or scanning for sensitive data, the analyst must understand what the AI tool has generated by reviewing the code for security vulnerabilities, insecure patterns, logic flaws, and policy violations. The review provides the baseline knowledge needed to prioritize and direct all subsequent remediation actions.
Why A is Wrong: Removing hard-coded secrets is a specific remediation action for a specific finding. This step should come after the source code review has identified the presence and location of hard-coded secrets, not before the initial assessment reveals whether they exist.
Why B is Wrong: Enforcing access controls for code repositories is a security hardening measure for the repository infrastructure. It protects access to existing code but does not constitute an assessment of what the AI tool has generated from a security standpoint.
Why C is Wrong: Enabling sensitive data discovery scans repositories for PII and sensitive information patterns. While valuable as part of the assessment, it is a specific automated scanning tool best used after or alongside a manual code review that provides contextual understanding of the codebase.
NEW QUESTION # 112
A company launches an AI application to monitor cloud misconfiguration and compliance. The AI application is shutting down development servers and opening ports during a client demonstration. Which of the following actions should the company take to return to normal operations and prevent future issues?
Answer: C
Explanation:
Option D is correct because the AI application is taking high-impact remediation actions without adequate human authorization. Human-in-the-loop control requires a qualified person to review and approve consequential actions, such as stopping servers or changing firewall exposure, before execution. The system may still detect misconfigurations, rank risks, and recommend fixes, but it should not autonomously disrupt development or alter network access during a demonstration. Option A restores service temporarily but leaves the unsafe decision path unchanged. Option B removes useful monitoring and does not correct the excessive automation. Option C might close improperly opened ports, yet it addresses only one symptom and does not prevent the AI from repeating other damaging actions. The stronger design is to roll back the unauthorized changes, restrict the agent's permissions, require approval for disruptive operations, and maintain an auditable record of recommendations and approvals. The NIST AI Risk Management Framework calls for appropriate human-oversight processes to be defined, assessed, and documented, supporting this governance pattern.
NEW QUESTION # 113
A company is adopting AI and wants to create policies and procedures that include a structure for evaluating, publishing, and approving patterns for AI usage.
Which of the following should the company establish to meet this goal?
Answer: B
Explanation:
Basic Concept: Successful AI adoption at an organizational level requires a centralized governance body that standardizes AI practices, promotes best practices, and ensures consistent, safe, and effective AI deployment across the organization. CompTIA SecAI+ Study Guide covers AI organizational governance structures under Domain 4.
Why A is Correct: An AI Center of Excellence (CoE) is an organizational unit specifically designed to govern, standardize, and advance AI adoption. It develops and publishes policies, creates approved patterns for AI usage, evaluates new AI use cases, provides expert guidance, and maintains governance oversight. The CoE exactly matches the described need for a structure to evaluate, publish, and approve AI usage patterns across the organization.
Why B is Wrong: An AI legal affairs office focuses on legal compliance, intellectual property, and regulatory matters related to AI. While important for legal risk management, it does not fulfill the broader governance mandate of establishing and approving AI usage patterns and best practices across the organization.
Why C is Wrong: An AI audit department conducts post-implementation reviews and compliance assessments of existing AI systems. It is a retrospective and oversight function rather than a proactive body for developing and approving AI usage patterns and policies.
Why D is Wrong: An AI data science division is a technical team focused on building AI models and solutions. It is a development function rather than a governance structure designed to create policies, evaluate AI patterns, and provide cross-organizational oversight of AI adoption.
NEW QUESTION # 114
Which of the following helps in managing potential security issues related to model training?
Answer: C
Explanation:
The NIST AI RMF provides structured guidance for identifying, assessing, and managing risks specific to AI systems, including those arising during model training. It addresses issues like bias, security, and data integrity, making it the best framework for managing training-related security concerns.
NEW QUESTION # 115
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