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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implementation | 25% | - Given a scenario, implement secure mobile device policies - 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 authentication and authorization solutions - Given a scenario, implement secure network architecture concepts - Given a scenario, implement identity and account management controls - Given a scenario, implement secure host settings |
| Topic 2: Architecture and Design | 21% | - Summarize virtualization and cloud security concepts - Explain the security implications of embedded and specialized systems - Explain the importance of physical security controls - Summarize basics of cryptographic concepts - Given a scenario, implement cybersecurity resilience - Explain the importance of security concepts in an enterprise environment - Summarize authentication and authorization design concepts - Explain secure application development, deployment, and automation concepts |
| Topic 3: Attacks, Threats, and Vulnerabilities | 24% | - Given a scenario, analyze potential indicators to determine the type of attack - Explain penetration testing concepts - Explain vulnerability scanning concepts - Given a scenario, analyze potential indicators associated with application attacks - Explain threat actor types and attributes - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with network attacks |
| Topic 4: 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 - Explain key aspects of digital forensics - Given a scenario, use data sources to support an investigation - Given a scenario, use appropriate tool to assess organizational security |
| Topic 5: Governance, Risk, and Compliance | 14% | - Given a scenario, follow organizational security policies and procedures - Explain privacy and sensitive data concepts in relation to security - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Explain risk management processes and concepts |
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NEW QUESTION # 98
A machine learning (ML) engineer is working with a security engineer to identify the best practices for securing a system with various AI models.
Which of the following actions should the engineers suggest?
Answer: B
Explanation:
Basic Concept: Securing AI systems requires a structured, end-to-end approach that addresses security at every phase of the AI model ' s lifecycle from data collection through training, testing, deployment, and ongoing monitoring. CompTIA SecAI+ Study Guide identifies the Model Development Life Cycle as the foundational framework for AI system security.
Why B is Correct: A secure Model Development Life Cycle (MDLC) integrates security practices at every stage of AI model development specifically tailored to ML workflows. It encompasses secure data handling, training data validation, model testing for adversarial robustness, secure deployment practices, and ongoing monitoring. Unlike generic software development lifecycles, the MDLC addresses ML-specific risks such as data poisoning, model drift, and adversarial attacks.
Why A is Wrong: Guardrail testing and security validation are important components of the MDLC but represent only the testing phase. They do not encompass the full lifecycle of security practices needed from data acquisition through production monitoring.
Why C is Wrong: Implementing comprehensive security architecture is a broad statement that describes an outcome rather than a specific actionable practice. It does not provide the structured, ML-specific guidance of an MDLC.
Why D is Wrong: A secure SDLC is designed for traditional software development and covers code security, testing, and deployment. While relevant to AI application development, it does not specifically address ML model-specific risks such as training data security, model integrity, and inference-time attacks.
NEW QUESTION # 99
Which of the following ensures the integrity of data usage in an AI system?
Answer: D
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 # 100
A global security operations center (SOC) wants to adapt and leverage the strength of AI in order to enhance its security operations. Which of the following is the best way to enhance the global SOC functions?
Answer: A
Explanation:
AI can significantly enhance SOC operations by summarizing and correlating high volumes of alerts, enabling analysts to quickly identify patterns, prioritize threats, and gain actionable insights. This reduces analyst fatigue and improves response times without introducing unsafe automation risks.
NEW QUESTION # 101
A security administrator needs to improve an AI model. During an initial investigation, the administrator notices that two successive login failures are recorded every day, and then a successful login occurs after a specific time interval. All the successful login attempts have been during office hours.
Which of the following techniques should the administrator use to improve the AI model ' s security?
Answer: D
Explanation:
Basic Concept: Pattern recognition is an AI technique that enables a system to identify recurring sequences or structures within data. In cybersecurity, detecting behavioral patterns such as consistent pre-login failure sequences followed by successful access is critical for threat detection. CompTIA SecAI+ Exam Objectives cover this under AI-assisted security.
Why B is Correct: The scenario describes a highly regular, repeating behavioral pattern - two failures followed by success at a specific time interval, consistently during office hours. Pattern recognition enables the AI model to learn this sequence and flag it as indicative of credential stuffing or an automated brute-force attack with timing controls. ML-driven pattern recognition is specifically designed for such behavioral anomaly detection.
Why A is Wrong: Access management controls who can log in and under what conditions. It enforces authorization policies but does not analyze or detect suspicious behavioral sequences in authentication logs.
Why C is Wrong: Signature matching compares known attack signatures against observed data. The described pattern is behavioral and time-based rather than a known malware or exploit signature, making this technique unsuitable.
Why D is Wrong: Vulnerability analysis identifies weaknesses in systems and code. It does not analyze authentication log sequences or detect behavioral patterns in user activity data.
NEW QUESTION # 102
A security administrator sees suspicious queries on AI logs.
Which of the following should the administrator implement to address this issue?
Answer: C
Explanation:
Basic Concept: Suspicious queries in AI system logs indicate that potentially malicious or policy-violating prompts are reaching the AI model. Proactively intercepting and filtering suspicious prompts before they are processed requires a prompt-level security control. CompTIA SecAI+ Study Guide identifies prompt firewalls as the appropriate control for blocking suspicious AI queries.
Why A is Correct: A prompt firewall analyzes incoming queries using a combination of pattern matching, semantic analysis, and policy rules to identify and block suspicious prompts before they reach the AI model.
It can detect prompt injection attempts, jailbreaking patterns, sensitive data extraction queries, and other suspicious prompt characteristics. By intercepting malicious prompts at the perimeter, it prevents them from influencing model behavior or extracting sensitive information.
Why B is Wrong: Data size controls limit the volume or size of data in requests. While controlling input size can prevent some attacks, it does not analyze the content or semantics of queries to detect suspicious patterns.
A small suspicious prompt can be just as harmful as a large one.
Why C is Wrong: Rate limiting controls the frequency of requests from a source. While it can slow down automated attack campaigns, it does not inspect query content for suspicious patterns and allows suspicious queries through as long as they are submitted below the rate threshold.
Why D is Wrong: Agentic AI is an AI architecture for autonomous multi-step task execution. It is a type of AI system, not a security control for filtering suspicious queries from an existing AI system ' s logs.
NEW QUESTION # 103
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