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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 - Summarize the importance of policies, processes, and procedures for incident response - Given a scenario, apply mitigation techniques or controls to secure an environment |
| Topic 2: Architecture and Design | 21% | - Summarize virtualization and cloud security concepts - Summarize basics of cryptographic concepts - Explain secure application development, deployment, and automation concepts - Summarize authentication and authorization design concepts - Explain the security implications of embedded and specialized systems - Explain the importance of security concepts in an enterprise environment - Explain the importance of physical security controls - Given a scenario, implement cybersecurity resilience |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack - Compare and contrast types of social engineering attacks - Explain vulnerability scanning concepts - Explain penetration testing concepts - Explain threat actor types and attributes - Given a scenario, analyze potential indicators associated with network attacks |
| Topic 4: Implementation | 25% | - Given a scenario, implement secure host settings - Given a scenario, implement identity and account management controls - Given a scenario, implement secure systems design - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure mobile device policies - Given a scenario, implement secure network architecture concepts - Given a scenario, implement public key infrastructure (PKI) |
| Topic 5: Governance, Risk, and Compliance | 14% | - Summarize regulations, standards, and frameworks that impact organizations - Compare and contrast various types of security controls - Explain risk management processes and concepts - Explain privacy and sensitive data concepts in relation to security - Given a scenario, follow organizational security policies and procedures |
Our CompTIA SecAI+ Certification Exam (CY0-001) practice exam can be modified in terms of length of time and number of questions to help you prepare for the CompTIA real test. We're certain that our CY0-001 Questions are quite similar to those on CY0-001 real exam since we regularly update and refine the product based on the latest exam content.
NEW QUESTION # 154
A security consultant needs to detect attacks across a large language model (LLM) firewall.
Which of the following techniques should the consultant use?
Answer: A
Explanation:
Signature matching allows the detection of known malicious patterns, inputs, or behaviors targeting an LLM firewall. It is an effective technique for identifying and blocking prompt injection or other recognized attack methods.
NEW QUESTION # 155
An AI security team must assess the probability of an attack on its new system and the impact associated with such an attack.
Which of the following threat-modeling resources best addresses the threat landscape for machine learning (ML)?
Answer: C
Explanation:
Basic Concept: Assessing attack probability and impact for ML systems requires a resource specifically built to catalog real-world adversarial attacks against AI and ML systems, including documented techniques with associated impact information. CompTIA SecAI+ Exam Objectives identify MITRE ATLAS as the authoritative ML threat landscape resource.
Why B is Correct: MITRE ATLAS is specifically designed as a comprehensive knowledge base of adversarial tactics, techniques, and case studies targeting AI and ML systems. It catalogs real-world attacks with associated probability factors derived from actual incidents and provides impact assessments for various attack types including data poisoning, model evasion, model extraction, and inference attacks. This directly enables the probability and impact assessment the team requires.
Why A is Wrong: The CVE AI working group focuses on identifying and cataloging specific vulnerability instances in AI software components. While useful for vulnerability management, it does not provide the comprehensive threat landscape coverage with probability and impact assessments for ML-specific attack tactics that ATLAS provides.
Why C is Wrong: The MIT risk repository is an academic resource cataloging general AI-related risks. It is research-oriented and does not provide the practitioner-focused, operational attack taxonomy and case study library that MITRE ATLAS offers for ML threat modeling.
Why D is Wrong: OWASP provides application security guidance including the OWASP LLM Top 10. While valuable for LLM-specific risks, OWASP does not provide the comprehensive ML threat landscape coverage or the probability and impact data that MITRE ATLAS offers for assessing the full spectrum of ML attack scenarios.
NEW QUESTION # 156
An organization wants to reduce vulnerabilities after deployment. The organization decides to incorporate an AI-assisted early detection and vulnerability identification process in its development workflow.
Which of the following AI-assisted functions is the best option?
Answer: D
Explanation:
Basic Concept: Reducing post-deployment vulnerabilities requires catching security issues as early as possible in the development workflow. AI-assisted tools that analyze code during development provide the earliest possible intervention point. CompTIA SecAI+ Study Guide covers AI integration in secure development under AI-assisted security.
Why A is Correct: AI-assisted code linting analyzes source code in real time during development to identify security vulnerabilities, insecure coding patterns, policy violations, and quality issues before code is compiled or committed. By catching vulnerabilities at the coding stage - the earliest possible point in the development workflow - AI code linting prevents vulnerable code from progressing to testing, staging, or production, directly reducing post-deployment vulnerabilities at their source.
Why B is Wrong: Incident management handles security events and incidents after they have occurred in production. It is a reactive capability focused on response and recovery rather than early-stage vulnerability identification in the development workflow.
Why C is Wrong: Automated deployment/rollback automates the process of pushing code to production and reverting to previous versions when issues are detected post-deployment. It is a deployment safety mechanism rather than an early detection tool during the development phase.
Why D is Wrong: System auditing reviews and records system activities and configurations for compliance verification. It is primarily a detective and compliance control for systems that are already deployed, not an early development-phase vulnerability identification tool.
NEW QUESTION # 157
A line of business wants to onboard an application that uses a custom AI model for employee assessments. The Chief Information Officer (CIO) agrees to allow the engagement to proceed but first wants a threat model. Which of the following is the most appropriate to use for an AI threat model?
Answer: D
Explanation:
ATLAS is specifically designed for creating AI threat models. It catalogs adversarial techniques, tactics, and use cases that target AI systems, making it the most appropriate framework for assessing risks to a custom AI model in employee assessments.
NEW QUESTION # 158
A threat intelligence team wants to automate the analysis and summary of its in-depth reports for multiple target audiences. Which of the following is the best solution?
Answer: D
Explanation:
Using an LLM with retrieval-augmented generation and current data is the best solution because the requirement is to analyze substantial threat-intelligence material and generate tailored summaries for different audiences. CompTIA SecAI+ explicitly identifies document synthesis and summarization as an AI-assisted security automation use case and also includes retrieval-augmented generation as a core AI concept.
RAG enables the LLM to retrieve relevant information from an organization ' s current threat-intelligence repositories before generating its response. This improves factual grounding and allows reports to incorporate recent indicators, adversary information, vulnerabilities, campaigns, and internal findings that may not exist in the model ' s original training data. The LLM can then adapt the same underlying intelligence into executive summaries, analyst-level reports, or technical remediation guidance.
Sentiment analysis determines emotional or opinion-related characteristics and does not meet the summarization requirement. K-means clustering is useful for grouping similar observations but does not naturally generate audience-specific narrative reports. Regression predicts numerical values rather than synthesizing complex textual intelligence. Consequently, an LLM combined with RAG provides the strongest fit for automated, current, audience-aware threat-report analysis and summarization.
NEW QUESTION # 159
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