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| Section | Weight | Objectives |
|---|---|---|
| Operations and Incident Response | 16% | - Explain key aspects of digital forensics - Given a scenario, apply mitigation techniques or controls to secure an environment - Given a scenario, use appropriate tool to assess organizational security - Given a scenario, use data sources to support an investigation - Summarize the importance of policies, processes, and procedures for incident response |
| Implementation | 25% | - Given a scenario, implement authentication and authorization solutions - Given a scenario, implement secure systems design - Given a scenario, implement secure network architecture concepts - Given a scenario, apply cybersecurity solutions to the cloud - Given a scenario, implement secure host settings - Given a scenario, implement secure mobile device policies - Given a scenario, implement public key infrastructure (PKI) - Given a scenario, implement identity and account management controls |
| Architecture and Design | 21% | - Given a scenario, implement cybersecurity resilience - Explain secure application development, deployment, and automation concepts - Explain the importance of physical security controls - Summarize basics of cryptographic concepts - Explain the security implications of embedded and specialized systems - Explain the importance of security concepts in an enterprise environment - Summarize virtualization and cloud security concepts - Summarize authentication and authorization design concepts |
| Governance, Risk, and Compliance | 14% | - Explain risk management processes and concepts - Compare and contrast various types of security controls - Summarize regulations, standards, and frameworks that impact organizations - Given a scenario, follow organizational security policies and procedures - Explain privacy and sensitive data concepts in relation to security |
| Attacks, Threats, and Vulnerabilities | 24% | - Explain threat actor types and attributes - Compare and contrast types of social engineering attacks - Given a scenario, analyze potential indicators associated with application attacks - Given a scenario, analyze potential indicators to determine the type of attack - Explain vulnerability scanning concepts - Explain penetration testing concepts - Given a scenario, analyze potential indicators associated with network attacks |
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NEW QUESTION # 128
Which of the following corporate policies would a security architect most likely reference when configuring an AI system to conduct input filtering?
Answer: B
Explanation:
Data governance is the most relevant corporate policy because input filtering depends on knowing what information may enter the AI system, how that information is classified, and which categories of sensitive or regulated data must be blocked, redacted, masked, or otherwise controlled. CompTIA SecAI+ specifically includes sensitive data governance under corporate AI policies and separately identifies data classification labels, redaction, masking, minimization, and related data-safety controls. A security architect can therefore translate the organization ' s data governance rules into technical input-filtering requirements, such as preventing credentials, customer PII, financial information, confidential intellectual property, or restricted datasets from being submitted to a model. A public-model usage policy governs whether and how employees may use externally hosted models, but it is narrower than the underlying data-handling rules. Third-party compliance evaluates external providers and contractual or regulatory obligations. Terms of use describe permitted user behavior but normally do not provide the detailed data-classification requirements required to implement technical filtering. Data governance is therefore the authoritative basis for determining what input the AI system should accept or reject.
NEW QUESTION # 129
Which control BEST prevents attackers from harvesting password hashes during lateral movement?
Answer: C
Explanation:
LSASS-protection solutions prevent hash extraction.
NEW QUESTION # 130
A user interface engineer adds new graphics to the latest release of an AI-integrated application. During the update, the engineer accidentally causes the model to retrain on unverified data. After the update, the model begins to return many errors.
Which of the following is the best way to mitigate future errors?
Answer: A
Explanation:
Basic Concept: When a non-ML engineer can accidentally trigger model retraining during a UI update, this indicates a lack of proper lifecycle management and change controls around the AI model. Uncontrolled retraining on unverified data is a critical vulnerability in the development and deployment process. CompTIA SecAI+ Study Guide identifies the Model Development Life Cycle as the framework for preventing such issues.
Why C is Correct: Implementing a Model Development Life Cycle (MDLC) establishes formal, controlled processes for every stage of model development and updates including data validation requirements before training, change management gates, testing and validation stages, and separation of duties between UI development and model training activities. An MDLC would have prevented the accidental retraining by requiring explicit, controlled authorization before any model training occurs.
Why A is Wrong: A WAF filters HTTP traffic at the application boundary. It does not govern internal development processes or control when and how model retraining occurs within the AI development pipeline.
Why B is Wrong: Role-based access control can restrict who has permission to trigger model retraining, which would help prevent this specific incident. However, it is one component of a broader MDLC governance framework and does not address data validation, testing stages, or the complete change management process.
Why D is Wrong: A GAN is a model architecture for generating synthetic data. It is a training technique unrelated to lifecycle governance or preventing accidental retraining from unverified data during unrelated application updates.
NEW QUESTION # 131
Which techniques belong to the MITRE ATT&CK Command-and-Control phase? (Choose two.)
Answer: A,B
Explanation:
Beaconing and covert channels maintain communication with the attacker.
NEW QUESTION # 132
A security analyst receives an alert about an AI system and is investigating the following output:
Which of the following is the most appropriate control the analyst should recommend?
Answer: A
Explanation:
Basic Concept: Suspicious or unexpected AI system outputs are often caused by malicious or malformed user inputs that exploit the AI ' s input processing. Validating and sanitizing user inputs before they reach the AI model prevents many classes of attacks including prompt injection, data exfiltration attempts, and input manipulation. CompTIA SecAI+ Study Guide emphasizes input validation as a foundational AI security control.
Why B is Correct: Implementing user input validation applies checks and constraints to all user-submitted content before it is processed by the AI system. Input validation can enforce length limits, detect injection patterns, filter disallowed characters or command structures, and ensure inputs conform to expected formats.
By rejecting malicious or malformed inputs at the entry point, this control prevents them from reaching the model and causing the suspicious outputs observed.
Why A is Wrong: Data sanitization processes data to remove harmful elements and is closely related to validation. However, input validation is broader and more proactive, checking conformance to rules before processing, while sanitization typically operates during or after processing. Validation at the input boundary is the more appropriate first-line control.
Why C is Wrong: Monitoring logs for attack keywords is a detective control that identifies attacks after they have already affected the system. It does not prevent suspicious outputs from being generated in the first place.
Why D is Wrong: Hardening the Model Context Protocol server improves the security of the infrastructure hosting the AI components. While important for infrastructure security, it does not directly validate or inspect the content of user inputs that cause suspicious outputs.
NEW QUESTION # 133
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