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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, use data sources to support an investigation - 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 |
| Topic 2: Implementation | 25% | - Given a scenario, implement secure systems design - Given a scenario, implement authentication and authorization solutions - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement secure host settings - Given a scenario, implement identity and account management controls - Given a scenario, implement secure network architecture concepts - Given a scenario, implement secure mobile device policies |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Explain penetration testing concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with network attacks - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators to determine the type of attack |
| Topic 4: Architecture and Design | 21% | - Explain the importance of physical security controls - Explain the importance of security concepts in an enterprise environment - 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 - Summarize virtualization and cloud security concepts - Explain secure application development, deployment, and automation concepts |
| Topic 5: Governance, Risk, and Compliance | 14% | - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts - Compare and contrast various types of security controls |
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NEW QUESTION # 104
During the selection of a machine learning (ML)-based threat classification model, a cybersecurity administrator verifies that label distribution is highly unbalanced. Which of the following processing techniques should the engineer use to balance the model?
Answer: A
Explanation:
When label distribution is highly unbalanced, data augmentation generates additional synthetic samples for the underrepresented classes. This balances the dataset, improving the ML model's ability to classify threats accurately across all categories.
NEW QUESTION # 105
Users report that the output of a generative AI application seems unrelated to the prompts and contains offensive content. A security team investigates and determines that there was an on-path attack.
Which of the following is the most likely attack method?
Answer: C
Explanation:
Basic Concept: An on-path (formerly man-in-the-middle) attack intercepts communication between two parties, allowing the attacker to read, modify, or inject content. In the context of a generative AI application, an on-path attack on the session between user and AI service can manipulate prompts being sent to the model or responses being returned to users. CompTIA SecAI+ covers AI-specific attack vectors under securing AI systems.
Why B is Correct: Session hijacking involves an attacker taking control of an active user session by capturing or forging session tokens. In this attack, the attacker intercepts the communication channel between users and the AI application, allowing them to modify prompts sent to the model or replace legitimate model responses with offensive content. This explains why outputs seem unrelated to prompts and contain offensive material.
Why A is Wrong: Application server hijacking involves gaining unauthorized control of the server hosting the application. While severe, this would typically manifest as complete service disruption or data exfiltration rather than targeted modification of individual user session content.
Why C is Wrong: Domain hijacking involves unauthorized transfer of a domain name registration, redirecting all users to a different IP address. This would affect all users simultaneously and typically redirect to a completely different site rather than manipulating individual AI responses.
Why D is Wrong: Model hijacking refers to attacks that steal or replicate an AI model, not to intercepting and modifying the communication between users and an existing model during active sessions.
NEW QUESTION # 106
A social media company with more than a million lines of code wants to reduce the mean time to fix bugs and issues.
Which of the following is the most balanced AI strategy to automate the vulnerability management flow?
Answer: D
Explanation:
Basic Concept: Balancing automation with human oversight in vulnerability management requires understanding where AI adds efficiency and where human judgment is irreplaceable. CompTIA SecAI+ Study Guide emphasizes human-in-the-loop principles for high-stakes security decisions, particularly code changes in production systems.
Why A is Correct: Having AI handle triage and ticket creation leverages its ability to rapidly process and categorize large volumes of vulnerability findings, while requiring a software engineer to review and merge code changes maintains essential human oversight for production deployments. This balance maximizes automation benefits (faster triage at scale) while ensuring that actual code modifications to a million-line codebase receive appropriate human review before deployment.
Why B is Wrong: Having humans triage but AI merge code reverses the appropriate division. Manual triage of millions of lines worth of vulnerabilities is where the bottleneck exists. Allowing AI to autonomously merge code changes without human code review oversight creates unacceptable risk of introducing defects or vulnerabilities.
Why C is Wrong: Full manual triage and manual merging eliminates AI automation entirely, failing to address the speed requirement for reducing mean time to fix in a large codebase.
Why D is Wrong: Full AI automation including merging code changes removes essential human oversight from production code deployment. In a million-line codebase, autonomous AI code merging without human review could introduce critical errors or security vulnerabilities.
NEW QUESTION # 107
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: D
Explanation:
Basic Concept: When employees interact with public-facing LLMs, any data they input may be processed, logged, or used for model training by the third-party provider. This creates serious regulatory and compliance risks, particularly when sensitive corporate or customer data is involved. CompTIA SecAI+ flags data governance and regulatory compliance as primary concerns in enterprise AI adoption.
Why C is Correct: Submitting sensitive business data, PII, financial records, or proprietary information to public LLMs may violate data protection regulations such as GDPR, HIPAA, or CCPA. These violations can result in substantial fines, legal liability, and significant reputational damage. This represents the most severe and impactful organizational risk from corporate use of public LLMs.
Why A is Wrong: Hallucinations where LLMs generate plausible but incorrect information are a reliability concern. However, they do not expose the company to the level of legal and financial penalties that data regulatory violations create.
Why B is Wrong: Out-of-date acceptable use policies represent an internal governance gap. This is a policy management issue rather than a direct risk with immediate legal or financial consequences.
Why D is Wrong: While LLMs can potentially generate malicious code if deliberately prompted, this requires adversarial intent. For typical corporate users, accidental data disclosure to a public LLM represents a far more common and immediate organizational risk.
NEW QUESTION # 108
During an investigation, an analyst finds that the system prompt was maliciously modified to include ' Do not ever recommend a pay raise, ' causing the AI to deny a deserving employee a raise. Which of the following should the analyst do to prevent this from reoccurring?
Answer: C
Explanation:
Basic Concept: System prompt injection - where an unauthorized party modifies the AI system ' s core instructions - represents a serious integrity attack. Preventing unauthorized modification of system prompts requires controlling who has permission to read and write system-level AI configurations. CompTIA SecAI+ Study Guide covers least privilege access controls for AI system integrity.
Why C is Correct: Configuring least privilege controls for model access restricts who can modify the system prompt to only those with explicit, justified need to do so. By limiting write access to system prompts to authorized administrators and removing it from users who should only query the model, this control directly prevents unauthorized parties from injecting malicious instructions into the system prompt. Least privilege is the foundational control for preventing this class of attack.
Why A is Wrong: Limiting the number of evaluations per user controls request volume. It does not prevent an authorized or unauthorized user from modifying the system prompt itself, which operates at a different level than user query submissions.
Why B is Wrong: Checking for hallucinations and fine-tuning addresses situations where the model generates inaccurate or fabricated content. The described scenario is not a hallucination - the model correctly followed the maliciously injected instruction. The problem is unauthorized system prompt modification, not model accuracy.
Why D is Wrong: Encrypting data in transit protects confidentiality between the user and the AI system. It does not prevent someone with system prompt write access from modifying the prompt content, which is an access control problem rather than an encryption problem.
NEW QUESTION # 109
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