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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - Adversarial Defense
|
| Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| AI-Assisted Security | 24% | - Operational Use of AI
|
The CompTIA CY0-001 certification exam is a valuable credential that often comes with certain personal and professional benefits. For many CompTIA professionals, the CompTIA SecAI+ Certification Exam (CY0-001) certification exam is not just a valuable way to boost their skills but also CompTIA SecAI+ Certification Exam certification exam gives them an edge in the job market or the corporate ladder. There are other several advantages that successful CompTIA CY0-001 Exam candidates can gain after passing the CompTIA CY0-001 exam.
NEW QUESTION # 138
Which techniques belong to the MITRE ATT&CK Command-and-Control phase? (Choose two.)
Answer: A,E
Explanation:
Beaconing and covert channels maintain communication with the attacker.
NEW QUESTION # 139
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?
Answer: A
Explanation:
Basic Concept: Dynamic Application Security Testing (DAST) tests running applications by sending various inputs to discover vulnerabilities. AI can significantly enhance DAST by intelligently generating diverse, targeted test payloads that traditional tools might miss. CompTIA SecAI+ covers AI augmentation of security testing methodologies.
Why C is Correct: Payload creation is highly suitable for AI automation during DAST. AI can generate diverse, contextually appropriate attack payloads such as SQL injection strings, XSS vectors, command injection attempts, and format string exploits tailored to the specific application ' s behavior observed during testing. AI can learn from the application ' s responses to previous payloads and generate increasingly targeted inputs, discovering vulnerabilities more efficiently than static payload databases.
Why A is Wrong: DDoS attacks are volume-based attacks designed to overwhelm network or application infrastructure. Automating DDoS during DAST is inappropriate as it would disrupt service availability rather than discover application security vulnerabilities, and it is harmful to legitimate operations.
Why B is Wrong: Data poisoning is an attack targeting AI/ML model training data integrity. It is relevant to securing AI systems but is not a DAST technique for testing web or software application security vulnerabilities during dynamic testing.
Why D is Wrong: Threat modeling is a structured analysis process performed before development or testing to identify potential threats and design appropriate countermeasures. It is a planning activity, not an attack technique that can be automated during dynamic application security testing.
NEW QUESTION # 140
A security alert triggers an agentic system. An analyst notices the following payload in the logs"
The alert includes multiple shell commands that are not typically run as part of any hardening.
Which of the following is the most effective control to implement?
Answer: A
Explanation:
The payload in the alert attempts to trick the system into executing unauthorized shell commands.
The most effective control is to implement allow-list validation (approved strings) before execution. This ensures that only predefined, safe commands are executed, blocking prompt injection attempts that introduce malicious code such as the fake patch script.
NEW QUESTION # 141
Which of the following ensures the integrity of data usage in an AI system?
Answer: A
Explanation:
Basic Concept: Data integrity in AI systems requires not only that data is accurate at a point in time, but that its entire history of transformation and usage can be traced and verified. Tracking how data has been used and transformed throughout the AI system lifecycle provides ongoing integrity assurance. CompTIA SecAI+ Study Guide covers data governance controls including lineage for AI integrity.
Why D is Correct: Data lineage tracks and documents the complete journey of data from its origin through every transformation, processing step, and use within an AI system. By recording what happened to the data, when, by whom, and through which processes, data lineage provides the audit trail needed to ensure data integrity throughout the AI system ' s data usage lifecycle. It enables verification that data has been used as intended and has not been improperly modified at any stage.
Why A is Wrong: Data masking replaces sensitive data values with anonymized equivalents to protect privacy. It is a confidentiality control that modifies data values rather than a mechanism for ensuring or tracking data integrity across the system.
Why B is Wrong: Data cleansing removes or corrects errors, inconsistencies, and noise in datasets to improve data quality. It is a data preparation activity that improves data accuracy at a point in time but does not track data usage or provide ongoing integrity assurance throughout the AI system lifecycle.
Why C is Wrong: Data verification confirms that data meets expected quality standards and validates its accuracy at a specific check point. While important for quality assurance, it provides a point-in-time check rather than continuous tracking of data usage and transformations as data lineage does.
NEW QUESTION # 142
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 # 143
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