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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI-Assisted Security | 24% | - Operational Use of AI
|
| Topic 2: Securing AI Systems | 40% | - Adversarial Defense
|
| Topic 3: Basic AI Concepts Related to Cybersecurity | 17% | - AI Threat Landscape
|
| Topic 4: AI Governance, Risk, and Compliance | 19% | - Risk and Compliance
|
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NEW QUESTION # 81
A cybersecurity analyst wants to choose a machine learning (ML) model to classify log entries while providing the best explainability.
Which of the following models should the analyst use?
Answer: B
Explanation:
Basic Concept: Different ML model architectures offer varying degrees of explainability. In cybersecurity, understanding why a model classified a log entry as malicious or benign is critical for analyst trust, investigation, and regulatory compliance. CompTIA SecAI+ covers model explainability under responsible AI and basic AI concepts.
Why C is Correct: Decision trees are inherently interpretable models that classify data through a series of transparent if-then rules. Every classification decision can be traced through the exact path of conditions that led to it, showing precisely which log entry features triggered the classification. Analysts can read and understand the decision path, making decision trees the gold standard for explainable ML classification in security applications where understanding the reason for a classification is as important as the classification itself.
Why A is Wrong: Large language models are complex transformer architectures with hundreds of billions of parameters. They function as black boxes - their internal decision-making processes are not human- interpretable, making them poor choices when explainability is the primary requirement.
Why B is Wrong: Neural networks are non-linear black box models. While they can achieve high classification accuracy, their multi-layer architecture makes it extremely difficult to explain why specific decisions were made in human-understandable terms.
Why D is Wrong: Generative adversarial networks are designed for generating synthetic data, not for classification tasks. They consist of competing generator and discriminator networks and are fundamentally unsuitable for log entry classification with explainability requirements.
NEW QUESTION # 82
Instructions: Click the (+) to assign each threat category into its appropriate framework.
An architect is modeling an agentic system to meet security standards.
Answer:
Explanation:
See Explanation below for complete solution for this PBQ.
Explanation:
Basic Concept: This is a Performance-Based Question (PBQ) - a simulation item requiring interactive drag- and-drop assignment of threat categories to appropriate frameworks in the actual exam. It tests knowledge of how different AI threat frameworks categorize and address specific threat types for agentic systems.
Key Concept - Framework-to-Threat Mapping: MITRE ATLAS covers ML-specific adversarial tactics such as model evasion, data poisoning, model extraction, and prompt injection for agentic systems. OWASP LLM Top 10 addresses application-level LLM vulnerabilities such as insecure output handling, excessive agency, and supply chain risks. NIST AI RMF addresses governance-level risks across the AI lifecycle. STRIDE addresses architectural threats including spoofing, tampering, repudiation, information disclosure, DoS, and elevation of privilege.
Why This Matters: Agentic AI systems have a unique threat landscape combining traditional software vulnerabilities with AI-specific attacks. Correctly mapping threat categories to frameworks is essential for comprehensive threat modeling of systems that autonomously execute multi-step tasks with tool access and real-world consequences.
Reference: CompTIA SecAI+ Study Guide Domain 4 covers AI governance frameworks and their specific threat categories. Candidates should understand the scope and focus areas of MITRE ATLAS, OWASP LLM Top 10, NIST AI RMF, and traditional security frameworks as they apply to agentic AI system security modeling.
NEW QUESTION # 83
A team of engineers builds an application using a large language model (LLM). The application is built on Linux and is hosted on a virtual server. Users must create an account in order to access and use the platform.
Which of the following should the team do to protect the account credentials?
Answer: B
Explanation:
Basic Concept: User account credentials stored in a database must be protected against unauthorized disclosure. The security of credentials at rest requires cryptographic controls that prevent even database administrators or attackers with database access from reading plaintext passwords. CompTIA SecAI+ Study Guide covers credential security controls as part of AI application security.
Why C is Correct: Implementing hashing and encryption for credential protection is the industry-standard approach. Passwords should be hashed using strong, slow algorithms such as bcrypt, Argon2, or scrypt with unique salts, making them computationally infeasible to reverse even if the database is compromised.
Additional sensitive credential data can be encrypted. Together, hashing and encryption ensure that account credentials remain protected even if the underlying storage is accessed by unauthorized parties.
Why A is Wrong: Patching the model with new datasets updates the AI model ' s training data and knowledge. It does not address the security of user account credentials stored in the application ' s authentication database.
Why B is Wrong: Updating Linux and virtual server software patches system vulnerabilities and is important for overall security hygiene. However, it does not implement specific protections for the account credentials themselves stored in the application database.
Why D is Wrong: Deploying an authenticated API requires users to authenticate to use the API, improving access control. While this complements credential security, it does not protect the storage of credentials at rest and does not replace hashing and encryption of the credential values themselves.
NEW QUESTION # 84
An analyst finds failed login attempts across multiple systems using different usernames but from the same IP. Which attack is MOST likely?
Answer: B
Explanation:
Password spraying uses common passwords across many usernames.
NEW QUESTION # 85
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: A
Explanation:
Protecting the model from tampering requires controlled and auditable access. By integrating the model with an API secured by IAM roles, only authenticated and authorized users or systems can interact with it, ensuring integrity and preventing unauthorized changes.
NEW QUESTION # 86
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