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| Section | Weight | Objectives |
|---|---|---|
| AI Governance, Risk and Compliance | 19% | - Compliance and legal requirements
|
| AI-assisted Security | 24% | - AI in security strategy and operations
|
| Securing AI Systems | 40% | - Security controls for AI systems
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
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NEW QUESTION # 77
Which of the following is a risk addressed by responsible AI?
Answer: D
Explanation:
Basic Concept: Responsible AI is a governance framework addressing risks that arise from AI systems producing outcomes that are unfair, harmful, or contrary to human values. Different risk types fall under different governance domains - some under responsible AI, others under security or operational management. CompTIA SecAI+ Study Guide covers responsible AI risk categories under Domain 4.
Why C is Correct: Response bias occurs when an AI system ' s outputs are systematically skewed against certain groups, topics, or perspectives, reflecting biases embedded in training data or model design. This is a core risk addressed by responsible AI principles including fairness, non-discrimination, and explainability.
Responsible AI frameworks mandate bias detection, assessment, and mitigation to ensure AI responses treat all users and groups equitably.
Why A is Wrong: Model drift describes the degradation of model performance over time as the distribution of real-world data diverges from the training data distribution. While an important operational concern, model drift is primarily a technical performance risk managed through MLOps and monitoring practices, not a core responsible AI governance concern.
Why B is Wrong: Reputational loss is a business risk consequence that may result from various AI failures including biased outputs or privacy violations. It is an outcome or impact rather than a specific risk category that responsible AI frameworks directly address.
Why D is Wrong: Data poisoning is a security attack where adversaries corrupt AI training data to manipulate model behavior. This is a cybersecurity threat managed through security controls and data integrity protections rather than responsible AI ethical governance frameworks focused on fairness and accountability.
NEW QUESTION # 78
A security analyst finds that the AI system is under a denial-of-wallet attack.
Which of the following should the analyst enforce to protect the company? (Choose two.)
Answer: B,D
NEW QUESTION # 79
An organization is developing and implementing AI features into a customer service application.
Which of the following practices should the organization put in place before releasing the application for customer trials?
Answer: A
Explanation:
Basic Concept: Before deploying AI applications that handle customer data in trials, protecting sensitive information through data masking and sanitization is essential. CompTIA SecAI+ Study Guide emphasizes pre-deployment data security controls as a critical step in the AI development lifecycle.
Why A is Correct: Data masking replaces sensitive real customer data with realistic but fictitious equivalents, while sanitization removes harmful or unwanted data elements. Before customer trials, these techniques prevent exposure of real PII or sensitive information, ensure the trial environment cannot leak production data, and protect the organization from privacy regulation violations. This is the most immediately actionable pre-trial security control.
Why B is Wrong: External compliance audits are formal processes typically conducted post-deployment or at planned intervals to verify regulatory compliance. They are not pre-trial security implementations and cannot prevent data exposure in a trial environment.
Why C is Wrong: Approved AI vendor lists are governance artifacts that manage vendor selection risk at the procurement stage. They do not directly protect customer data within an application being prepared for trials.
Why D is Wrong: Third-party risk management addresses risks from external vendors and partners at a strategic level. While important for overall governance, it does not constitute a direct data security control for a pre-trial release.
NEW QUESTION # 80
A developer is selecting authentication controls for an AI system.
Which of the following is the best way to prevent threat actor replay attacks?
Answer: C
Explanation:
Basic Concept: A replay attack occurs when an attacker captures a valid authentication token or credential and reuses it to impersonate a legitimate user. Preventing replay attacks requires ensuring that captured credentials cannot be successfully reused after a defined period or after their intended single use. CompTIA SecAI+ Study Guide covers replay attack prevention under AI system authentication.
Why C is Correct: Expiring session tokens have a limited validity window, typically a few minutes to hours.
If an attacker captures a token, they can only use it until it expires. Short expiration times dramatically reduce the window of opportunity for replay attacks. This is the most direct and effective control specifically targeting replay attack prevention, as expired tokens are rejected even if intercepted.
Why A is Wrong: IdP federation enables single sign-on across multiple systems using federated identity providers. While it standardizes authentication, it does not inherently prevent replay attacks on captured tokens unless combined with short token expiration and proper validation.
Why B is Wrong: SSH certificate authentication uses cryptographic certificates for strong authentication.
While more secure than password-based SSH, certificates alone do not prevent replay attacks unless they include timestamps, nonces, or other anti-replay mechanisms that invalidate captured credentials.
Why D is Wrong: IAM access keys are long-lived credentials that provide programmatic access to services.
They are typically static and do not expire automatically, making them vulnerable to replay attacks if intercepted. They are less suitable for replay attack prevention than expiring session tokens.
NEW QUESTION # 81
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: D
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 # 82
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