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| Section | Weight | Objectives |
|---|---|---|
| Operations and Incident Response | 16% | - Given a scenario, use data sources to support an investigation - Explain key aspects of digital forensics - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, apply mitigation techniques or controls to secure an environment - Summarize the importance of policies, processes, and procedures for incident response |
| Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure systems design - Given a scenario, implement identity and account management controls |
| Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with network attacks - Explain penetration testing concepts - Given a scenario, analyze potential indicators to determine the type of attack - Given a scenario, analyze potential indicators associated with application attacks - Compare and contrast types of social engineering attacks - Explain vulnerability scanning concepts - Explain threat actor types and attributes |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Summarize regulations, standards, and frameworks that impact organizations - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Given a scenario, follow organizational security policies and procedures |
| Architecture and Design | 21% | - Explain the security implications of embedded and specialized systems - Summarize authentication and authorization design concepts - Summarize virtualization and cloud security concepts - Summarize basics of cryptographic concepts - Explain the importance of security concepts in an enterprise environment - Explain secure application development, deployment, and automation concepts - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience |
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NEW QUESTION # 19
An organization implements a domain-specific AI chatbot. After operating normally for weeks, the model returns contextually incorrect responses - treating ' worm ' as a biological pest rather than a computer worm when answering a cybersecurity question.
Which of the following should the organization do to address the issue?
Answer: A
Explanation:
Basic Concept: Domain-specific AI chatbots can produce contextually inappropriate responses when they lack sufficient domain grounding to disambiguate terms that have different meanings in different contexts.
Guardrails can enforce domain-appropriate interpretation and response constraints. CompTIA SecAI+ Study Guide covers guardrails as a mechanism for maintaining model behavioral boundaries.
Why A is Correct: Configuring guardrails allows the organization to enforce domain-specific behavioral constraints on the chatbot, ensuring it interprets ambiguous terms within the correct technical context.
Guardrails can include context-aware rules that recognize when a query is in a cybersecurity context and constrain the model to provide domain-appropriate responses. This directly addresses the issue of the model providing biologically-framed responses to a technical cybersecurity question.
Why B is Wrong: Encrypting model weights at rest protects the model parameters from unauthorized access or modification. It is a data protection control for model intellectual property and does not influence how the model interprets or responds to domain-specific queries at inference time.
Why C is Wrong: Model access controls restrict who can query and modify the model. They manage authorization at the user and system level but do not enforce domain-appropriate response constraints or prevent contextually incorrect answers from being generated.
Why D is Wrong: Prompt templates provide structured, reusable formats for common queries. While they can help standardize how cybersecurity questions are asked, they require users to use the template and do not provide real-time enforcement of domain-appropriate response generation for all input variations.
NEW QUESTION # 20
For which of the following situations would a human-in-the-loop be most recommended?
Answer: A
NEW QUESTION # 21
An organization is concerned with the exposure of sensitive data. Which of the following is the most relevant security concern?
Answer: B
Explanation:
Model inversion is a security concern where attackers can reconstruct or infer sensitive training data from the AI model's outputs. This directly threatens data confidentiality, making it the most relevant concern for sensitive data exposure.
NEW QUESTION # 22
A cyberthreat intelligence (CTI) analyst conducts research about a threat during a recent attack. The analyst uses an AI threat-modeling resource to find the relevant risks and utilizes causal and domain taxonomies.
Which of the following best describes which resource the analyst is using?
Answer: C
Explanation:
The MIT AI Risk Repository is specifically characterized by its use of both a Causal Taxonomy and a Domain Taxonomy. The repository consolidates AI risks from numerous frameworks and allows practitioners to classify and research risks according to how, when, and why they occur as well as the domain of harm involved. MIT states that its Causal Taxonomy addresses factors such as the responsible entity, intent, and timing, while its Domain Taxonomy categorizes risks into broader domains and subdomains. This terminology directly matches the scenario. CompTIA SecAI+ includes the MIT AI Risk Repository among the threat-modeling resources candidates must understand. MITRE ATLAS instead organizes adversarial AI activity into tactics, techniques, procedures, and case studies. The CVE AI Working Group focuses on vulnerabilities and related vulnerability-management concerns rather than causal/domain risk taxonomies.
OWASP ' s Machine Learning Security Top 10 concentrates on prominent ML security risks and defensive awareness. Because the question explicitly identifies causal and domain taxonomies, the MIT AI Risk Repository is the precise resource being described.
NEW QUESTION # 23
Which of the following technologies is used in deepfake?
Answer: A
Explanation:
Deepfakes are primarily created using GANs, where two neural networks (a generator and a discriminator) compete to produce highly realistic synthetic media, such as manipulated videos or images.
NEW QUESTION # 24
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