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| Section | Weight | Objectives |
|---|---|---|
| Securing AI Systems | 40% | - AI System Protection
|
| Basic AI Concepts Related to Cybersecurity | 17% | - Generative AI Concepts
|
| AI Governance, Risk, and Compliance | 19% | - AI Governance Frameworks
|
| AI-Assisted Security | 24% | - Security Operations Enhancement
|
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NEW QUESTION # 57
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?
Answer: A
Explanation:
Basic Concept: Reconnaissance is the information gathering phase of an attack. LLMs can be enhanced to perform more effective reconnaissance by giving them access to current, specific information beyond their training data cutoff. CompTIA SecAI+ covers AI augmentation techniques including RAG under AI-assisted security.
Why A is Correct: RAG enhances an LLM by connecting it to an external knowledge base or real-time data sources that it can query during inference. For reconnaissance purposes, a RAG-enabled LLM can access up- to-date organizational information, technical documentation, and intelligence feeds that go beyond its static training data. This makes the LLM significantly more capable for gathering current, targeted intelligence about specific organizations or infrastructure.
Why B is Wrong: Creating a web scraper is a basic data collection technique. While AI can help write scraper code, the scraper itself is a simple script that does not enhance the LLM ' s intelligence or reasoning capabilities for sophisticated reconnaissance.
Why C is Wrong: Instructing an AI assistant to query as an administrator is a prompt manipulation attempt.
An LLM cannot actually gain elevated permissions through a prompt instruction; this describes social engineering or privilege escalation via prompting, not a performance enhancement technique.
Why D is Wrong: Prompting a chatbot to describe naming patterns is a basic use of an existing LLM ' s knowledge. It does not strengthen or enhance the model ' s capabilities; it merely queries what the model already knows from training data, which may be outdated or generic.
NEW QUESTION # 58
A team of data scientists is ready to release a model for enterprise use. The team wants to protect the model from unintentional changes or tampering.
Which of the following is the most appropriate action?
Answer: D
Explanation:
Basic Concept: Protecting a released AI model from unauthorized modification requires controlling who can interact with it and at what privilege level. IAM-integrated API access provides granular, auditable control over model interactions. CompTIA SecAI+ Study Guide covers model protection through identity and access management integration.
Why D is Correct: Integrating an API with IAM roles ensures that all interactions with the model are authenticated and authorized according to precisely defined permissions. IAM roles enforce the principle of least privilege, ensuring users can query the model only within authorized scope and cannot modify model parameters, weights, or configuration. API-level access provides an abstraction layer that protects the underlying model from direct access while enabling controlled, auditable interactions.
Why A is Wrong: Changing to an LLM with guardrails addresses model behavior safety but does not protect the model artifacts themselves from tampering or unauthorized modification. It changes the model type rather than implementing access controls.
Why B is Wrong: Providing secure copies for local runtime distributes model copies to multiple endpoints, significantly increasing the attack surface for tampering. Each local copy represents a potential point of unauthorized modification.
Why C is Wrong: Restricting access to IT professionals is overly broad and vague. IT professionals may still need varying levels of access for different purposes, and generic role-based access without IAM integration and API mediation provides insufficient granularity to prevent unintentional modification.
NEW QUESTION # 59
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 # 60
A company wants to detect abnormal insider activity based on historical logs. Which technology is BEST?
Answer: C
Explanation:
User and Entity Behavior Analytics detects deviations from normal patterns.
NEW QUESTION # 61
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: B
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 # 62
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