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CompTIA CY0-001 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: AI-assisted Security24%- AI in security strategy and operations
  • 1. Threat modeling and risk assessment
  • 2. Compliance monitoring and auditing
- Security automation and orchestration
  • 1. Workflow automation and response playbooks
  • 2. Vulnerability management and assessment
- AI for threat detection and response
  • 1. Automated incident triage and correlation
  • 2. Accelerated threat hunting
  • 3. Anomaly detection and behavioral analysis
Topic 2: AI Governance, Risk and Compliance19%- Risk management for AI
  • 1. AI risk identification and assessment
  • 2. Risk mitigation and control strategies
- Governance frameworks and policies
  • 1. Responsible AI principles and ethics
  • 2. Global standards: NIST AI RMF, EU AI Act
  • 3. Organizational AI governance structures
- Compliance and legal requirements
  • 1. Transparency, accountability and auditability
  • 2. Data protection and privacy laws
Topic 3: Basic AI Concepts Related to Cybersecurity17%- AI-driven threats and risks
  • 1. Malicious use of generative AI
  • 2. Automated phishing, polymorphic malware
  • 3. Adversarial machine learning attacks
- AI applications in security
  • 1. Threat detection and anomaly analysis
  • 2. Security automation and decision support
- Core AI principles and terminology
  • 1. Generative AI concepts and capabilities
  • 2. Machine learning, deep learning, NLP, automation
Topic 4: Securing AI Systems40%- Defending against AI-specific attacks
  • 1. Threat modeling for AI lifecycles
  • 2. Prompt injection, data poisoning, model inversion
  • 3. Adversarial example defense
- Security controls for AI systems
  • 1. Data protection: integrity, confidentiality, privacy
  • 2. Deployment environment security
  • 3. Model security: access, integrity, anti-tampering
- Secure AI development and operations
  • 1. DevSecOps integration for AI
  • 2. Secure MLOps and AI pipeline design

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CompTIA SecAI+ Certification Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
Which of the following is used to train an AI model with unstructured data?

Answer: B

Explanation:
Fine-tuning allows an AI model to adapt to unstructured data (such as text, audio, or images) by retraining it on domain-specific datasets. This process improves the model's ability to handle and generate outputs aligned with the unstructured data context.


NEW QUESTION # 40
An employee wants a consulting company to procure a data set that contains age, ethnicity, and diabetes status. During development, the employer wants to ensure the integrity of the data.
Which of the following is the best strategy to accomplish this task?

Answer: D

Explanation:
Basic Concept: Data integrity ensures that data has not been tampered with, corrupted, or modified during storage or transmission. For AI training data that is procured from external sources, cryptographic integrity verification is essential to confirm the data arrived unmodified. CompTIA SecAI+ Study Guide covers data integrity controls for AI data pipelines.
Why A is Correct: Implementing checksums provides cryptographic verification of data integrity. A checksum or hash value such as SHA-256 is computed from the dataset at the source. The receiver computes the same hash and compares it to the provided value. Any modification to the data during transit or storage will produce a different hash, immediately detecting tampering or corruption. This is the most reliable, automated, and scalable strategy for ensuring the integrity of procured training data.
Why B is Wrong: Human evaluation can verify data quality and relevance but is impractical for verifying integrity across large datasets of medical records. Human reviewers cannot detect subtle bit-level corruption or intentional small modifications, and the process is not scalable.
Why C is Wrong: Querying the model tests model performance rather than verifying the integrity of the underlying training data. The model cannot tell you whether its training data was modified after collection or during procurement.
Why D is Wrong: Log monitoring tracks system activities and events over time. While useful for auditing access to data, it cannot retroactively confirm that data content has not been modified and does not provide cryptographic integrity guarantees.


NEW QUESTION # 41
For which of the following situations would a human-in-the-loop be most recommended?

Answer: C


NEW QUESTION # 42
Developers introduce new features to their generative AI product in an effort to stand out from the competition and offer more value to customers.
Which of the following most accurately explains the risks when enabling more functionality?

Answer: C

Explanation:
Basic Concept: The relationship between AI system capabilities and security risk is a fundamental concept in AI governance. As AI models gain more functionality and capabilities, their potential for misuse, unintended consequences, and attack surface expansion grows proportionally. CompTIA SecAI+ Study Guide addresses capability-risk proportionality under AI governance.
Why D is Correct: The risks of a generative AI product are proportional to its capabilities. Each new feature expands what the model can do, which simultaneously expands what adversaries can manipulate it to do, what sensitive operations it can be directed to perform, and what unintended harm it can cause. A model that can generate text, images, execute code, and call external APIs has dramatically greater risk potential than one that can only generate text. Risk grows with capability scope.
Why A is Wrong: Risks do not remain constant when new features are added. New features introduce new attack vectors, expand the model ' s action space, and create new opportunities for misuse. Each addition fundamentally changes the system ' s risk profile.
Why B is Wrong: While risks do increase with new features, saying they simply increase does not capture the precise relationship. The increase is proportional to the nature and scope of the capabilities added, not a uniform increment for any feature addition.
Why C is Wrong: While risks can be measured qualitatively, stating that risks are measured qualitatively is a statement about measurement methodology rather than an explanation of how risks change when functionality is enabled. It does not accurately describe the relationship between capability and risk.


NEW QUESTION # 43
A security administrator must provide access controls for AI systems to list tables.
Which of the following should the administrator implement?

Answer: D

Explanation:
Basic Concept: AI systems interact with different resource layers including models, data stores, and infrastructure. Controlling what data an AI system can access requires implementing access controls at the data layer. CompTIA SecAI+ Study Guide differentiates between model access, data access, and network access controls for AI systems.
Why D is Correct: Data access controls govern what data resources an AI system can interact with, including which databases, tables, and records it can read or modify. To control an AI system ' s ability to list database tables, the administrator must implement data access controls that define precisely which tables the AI can enumerate and query, following the principle of least privilege for data interactions.
Why A is Wrong: Agentic AI access refers to permissions granted to autonomous AI agents to perform actions and use tools. It is a broader concept about what an AI agent can do operationally rather than a specific data-layer access control mechanism.
Why B is Wrong: A Network Access Control List controls network traffic at the IP and port level, determining which hosts can communicate with which network resources. It operates at the network layer and cannot enforce fine-grained control over which database tables an AI system is allowed to list.
Why C is Wrong: Model access controls govern who and what can interact with the AI model itself - who can query it, update it, or access its parameters. This is distinct from data access, which controls what the model can read from data stores during operation.


NEW QUESTION # 44
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