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CompTIA CY0-001 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Securing AI Systems	40%	- Secure AI development and operations 1. DevSecOps integration for AI 2. Secure MLOps and AI pipeline design - Defending against AI-specific attacks 1. Adversarial example defense 2. Prompt injection, data poisoning, model inversion 3. Threat modeling for AI lifecycles - Security controls for AI systems 1. Model security: access, integrity, anti-tampering 2. Data protection: integrity, confidentiality, privacy 3. Deployment environment security
Topic 2: AI Governance, Risk and Compliance	19%	- Compliance and legal requirements 1. Transparency, accountability and auditability 2. Data protection and privacy laws - Risk management for AI 1. AI risk identification and assessment 2. Risk mitigation and control strategies - Governance frameworks and policies 1. Global standards: NIST AI RMF, EU AI Act 2. Organizational AI governance structures 3. Responsible AI principles and ethics
Topic 3: Basic AI Concepts Related to Cybersecurity	17%	- Core AI principles and terminology 1. Generative AI concepts and capabilities 2. Machine learning, deep learning, NLP, automation - AI applications in security 1. Threat detection and anomaly analysis 2. Security automation and decision support - AI-driven threats and risks 1. Adversarial machine learning attacks 2. Automated phishing, polymorphic malware 3. Malicious use of generative AI
Topic 4: AI-assisted Security	24%	- Security automation and orchestration 1. Vulnerability management and assessment 2. Workflow automation and response playbooks - AI in security strategy and operations 1. Threat modeling and risk assessment 2. Compliance monitoring and auditing - AI for threat detection and response 1. Automated incident triage and correlation 2. Anomaly detection and behavioral analysis 3. Accelerated threat hunting

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CompTIA SecAI+ Certification Exam CY0-001 Prüfungsfragen mit Lösungen (Q133-Q138):

133. Frage
Which of the following risks most often occurs when developing an internal chatbot?

A. Model theft
B. Unauthorized access
C. Data leakage
D. Prompt injection

Antwort: C

Begründung:
Data leakage is the most applicable risk because an internal chatbot commonly interacts with organizational information that may include confidential documents, customer information, employee records, credentials, intellectual property, and other sensitive content. CompTIA SecAI+ specifically identifies accidental data leakage as an AI risk and sensitive information disclosure as an attack or failure condition affecting AI systems. Internal chatbots can expose information when retrieval permissions are too broad, conversation context crosses security boundaries, prompts contain confidential material, or model responses reproduce information that the requesting user should not receive. Prompt injection is an important chatbot attack vector, but it describes deliberate manipulation of model instructions rather than the broader organizational risk emphasized here. Model theft concerns unauthorized acquisition or reproduction of a model. Unauthorized access concerns gaining access to resources without proper authorization and is generally addressed through model, data, agent, network, and API access controls. In contrast, information leakage can occur even when legitimate employees are using the chatbot normally. Protecting sensitive data through access controls, minimization, masking, classification, and monitoring is therefore fundamental to internal chatbot security.

134. Frage
A short AI-generated video shows a celebrity ' s likeness talking about a fake public security event.
Which of the following was used to create this video?

A. Machine learning (ML) classifier
B. Random forest
C. Convolutional neural network
D. Statistical analysis

Antwort: C

Begründung:
Basic Concept: Creating realistic deepfake videos that convincingly replicate a real person ' s facial expressions, movements, and voice requires deep learning models capable of learning and synthesizing complex spatial and temporal features from existing video data. CompTIA SecAI+ covers deepfake technologies under basic AI concepts.
Why B is Correct: Convolutional Neural Networks are foundational to deepfake video generation. CNNs excel at learning spatial features from visual data and are used within deepfake architectures to analyze source and target faces, extract facial features, and synthesize realistic face swaps or face animations. Modern deepfake systems typically combine CNNs with autoencoders and GANs to generate convincing video content showing a person saying or doing things they never did.
Why A is Wrong: Statistical analysis involves mathematical methods for analyzing data distributions and relationships. It does not have the capability to generate synthetic video content or replicate a person ' s visual likeness in motion.
Why C is Wrong: An ML classifier assigns input data to predefined categories. Classification models detect and label content rather than generating new synthetic video content of a person ' s likeness. They are detection tools, not generation tools.
Why D is Wrong: Random forest is an ensemble ML method using multiple decision trees for classification and regression tasks. It works on structured, tabular data and cannot process or generate visual, spatial data needed for realistic deepfake video synthesis.

135. Frage
Which of the following technologies is used in deepfake?

A. Generative adversarial network (GAN)
B. Prompt engineering
C. Multi-shot prompting
D. Transfer learning

Antwort: A

Begründung:
Deepfakes are primarily created using GANs, where two neural networks (a generator and a discriminator) compete to produce highly realistic synthetic media, such as manipulated videos or images.

136. Frage
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?

A. Integrate an application programming interface (API) with identity and access management (IAM) roles to interact with the model.
B. Change the model to a large language model (LLM) for interactive features with guardrails.
C. Provide secure copies of the model for local runtime usage.
D. Restrict access to only IT professionals in the organization.

Antwort: A

Begründung:
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.

137. Frage
Which of the following resources most accurately covers the most widely recognized threat vectors and common vulnerabilities related to large language model (LLM) applications?

A. National Institute of Standards and Technology (NIST) AI Risk Management Framework (RMF)
B. MITRE Adversarial Threat Landscape for AI Systems (ATLAS)
C. Massachusetts Institute of Technology Risk Repository
D. Open Worldwide Application Security Project (OWASP)

Antwort: D

Begründung:
OWASP is the best answer because it publishes the Top 10 for Large Language Model Applications, a resource specifically structured around significant security vulnerabilities and attack patterns affecting LLM- based applications. CompTIA SecAI+ Objective 2.1 directly lists the OWASP Top 10 and specifically the LLM Top 10 as an AI threat-modeling resource. MITRE ATLAS is also highly relevant to AI security, but its purpose is broader: it catalogs adversarial tactics, techniques, and procedures associated with attacks against AI and machine learning systems. The MIT AI Risk Repository aggregates a broad range of AI risks rather than functioning as the recognized LLM application vulnerability Top 10. NIST AI RMF provides a risk- management framework for governing and managing AI risks but is not primarily a vulnerability catalog for LLM applications. Since the question asks for the resource that most directly captures widely recognized threat vectors and common vulnerabilities associated specifically with LLM applications, OWASP ' s LLM- focused Top 10 is the most precise match.

138. Frage
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