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| Section | Weight | Objectives |
|---|---|---|
| Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures |
| Architecture and Design | 21% | - Summarize virtualization and cloud security concepts - 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 authentication and authorization design concepts - Summarize basics of cryptographic concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment |
| Operations and Incident Response | 16% | - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics |
| Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure systems design - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Compare and contrast types of social engineering 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 - Given a scenario, analyze potential indicators associated with network attacks |
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NEW QUESTION # 134
Which of the following is the most concerning risk for a company that allows corporate end users to use public-facing large language models (LLMs)?
Answer: C
Explanation:
The greatest concern with employees using public-facing LLMs is the potential exposure of sensitive or regulated corporate data. Submitting such information to external systems may violate data protection laws (e.g., GDPR, HIPAA), creating legal and compliance risks that outweigh issues like hallucinations or malicious outputs.
NEW QUESTION # 135
Which control BEST prevents attackers from harvesting password hashes during lateral movement?
Answer: C
Explanation:
LSASS-protection solutions prevent hash extraction.
NEW QUESTION # 136
Which of the following would most likely be used to prove that an image is AI generated?
Answer: D
Explanation:
Watermarking embeds hidden, verifiable markers into AI-generated images. These markers can later be detected to prove the image originated from an AI system, making it the most reliable method for verification.
NEW QUESTION # 137
Which of the following helps in managing potential security issues related to model training?
Answer: A
Explanation:
Basic Concept: Managing security risks in AI model training requires a comprehensive framework specifically designed for AI risk identification, assessment, and mitigation across the entire AI lifecycle including data collection, training, and deployment. CompTIA SecAI+ Study Guide identifies NIST AI RMF as the primary resource for AI-specific risk management.
Why A is Correct: The NIST AI Risk Management Framework is purpose-built for managing risks throughout the AI lifecycle. It provides structured guidance for identifying, assessing, and mitigating risks specific to AI systems including training data quality, model bias, data poisoning, and training pipeline vulnerabilities. Its AI-specific scope makes it the most appropriate framework for managing model training security issues.
Why B is Wrong: ISO 27001 is an information security management system standard focused on general IT security controls and risk management. It does not specifically address AI model training risks, data pipeline integrity, or ML-specific vulnerabilities.
Why C is Wrong: The OECD provides high-level AI governance principles and policy recommendations at an international level. It offers ethical and policy guidance but does not provide operational risk management guidance for securing AI model training processes.
Why D is Wrong: GDPR is a European data protection regulation focused on personal data privacy, consent, and individual rights. While relevant to training data governance, it does not address the technical security risks of model training pipelines or ML system vulnerabilities.
NEW QUESTION # 138
An internal user enters a client credit card number into an internal generative machine learning (ML) model:
#User prompt: Customer Jane Doe has a new credit card that she wants to add to her account. The number is
5555-5555-5555-5555
Which of the following is the most effective way to prevent prompt injection attacks against a large language model (LLM)?
Answer: A
Explanation:
Basic Concept: Prompt injection occurs when malicious content embedded in user input manipulates an LLM
' s behavior, causing it to leak sensitive data, bypass restrictions, or execute unintended actions. Preventing such attacks requires mechanisms that inspect and filter content at the prompt level. CompTIA SecAI+ covers LLM-specific security controls extensively.
Why A is Correct: Guardrails are purpose-built controls that inspect, filter, and constrain both input prompts and output responses in LLM systems. They can detect sensitive data patterns such as credit card numbers, block prompt injection payloads, enforce content policies, and prevent the model from processing or outputting restricted information. Guardrails are the primary LLM-native defense against prompt injection as cited in the CompTIA SecAI+ Study Guide.
Why B is Wrong: Antivirus software detects known malware signatures in files and executables. It does not inspect or understand the semantic content of LLM prompts and cannot detect or block prompt injection attacks.
Why C is Wrong: A WAF operates at the HTTP layer inspecting web requests and responses against rule sets.
While it can block some patterns, it lacks the contextual intelligence to understand LLM prompt semantics and cannot prevent sophisticated injection attacks.
Why D is Wrong: Role-based access control manages who can access which resources. It controls authorization but does not inspect the content of prompts to prevent injection attacks once a user has legitimate access.
NEW QUESTION # 139
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