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CompTIA CY0-001 Exam Syllabus Topics:

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

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CompTIA SecAI+ Certification Exam Sample Questions (Q85-Q90):

NEW QUESTION # 85
Which of the following strengthens the performance of a large language model (LLM) for malicious reconnaissance?

Answer: C

Explanation:
RAG augments an LLM with external, up-to-date information retrieval, improving its factual accuracy and contextual scope-capabilities that directly strengthen the model's effectiveness for reconnaissance.


NEW QUESTION # 86
Which of the following is a key principle of responsible AI systems?

Answer: A

Explanation:
Basic Concept: Responsible AI encompasses a set of principles designed to ensure AI systems operate ethically, fairly, and accountably. These principles guide AI development and deployment to minimize harm and maximize trustworthiness. CompTIA SecAI+ Exam Objectives list transparency and explainability as foundational responsible AI principles under Domain 4.
Why B is Correct: Transparency and explainability are cornerstone principles of responsible AI. Transparency means AI systems are open about their nature, capabilities, limitations, and how they make decisions.
Explainability means the system can articulate the reasons behind its decisions in human-understandable terms. Together, they enable accountability, support regulatory compliance, allow bias detection, and build user trust. The CompTIA SecAI+ Study Guide and responsible AI frameworks including OECD and NIST AI RMF consistently identify this as a key principle.
Why A is Wrong: Using protected data for training would violate privacy and intellectual property rights.
This is not a responsible AI principle - responsible AI actually requires ensuring that training data respects privacy, consent, and legal protections.
Why C is Wrong: Human-in-the-loop is an important operational practice for high-stakes AI decisions, but it is one design pattern rather than the key overarching principle of responsible AI. Not all responsible AI systems require human-in-the-loop operation for every decision.
Why D is Wrong: Maximizing model security is a cybersecurity objective for AI systems. While important, it is an operational security concern rather than a responsible AI governance principle focused on fairness, accountability, and trustworthiness in AI decision-making.


NEW QUESTION # 87
A company develops an AI model to diagnose patients. Hospitals access the model through an integrated application programming interface (API). The security team performs a denial-of- service (DoS) attack via brute force on the model. Which of the following controls would have prevented this issue?

Answer: B

Explanation:
Rate limiting restricts the number of API requests within a specific timeframe, preventing brute- force attempts that can overwhelm the AI model and cause denial-of-service conditions.


NEW QUESTION # 88
A data set containing medical information is put into a machine learning (ML) model that is designed to predict specific illnesses for a population. In the process of verifying the reliability of the system, the compliance officer realizes that the system cannot reliably predict illnesses for certain segments of the population.
Which of the following types of risk is most applicable to this case?

Answer: B

Explanation:
Basic Concept: AI models trained on unrepresentative data can produce systematically inaccurate results for certain population groups. This is a form of algorithmic bias where the model ' s performance varies significantly across demographic segments, creating disparate outcomes. CompTIA SecAI+ Exam Objectives cover bias as a core AI governance and risk concept.
Why A is Correct: Bias in AI occurs when a model produces systematically skewed results for certain groups due to biased training data, flawed data collection, or model design choices. In this healthcare scenario, the inability to reliably predict illnesses for specific population segments indicates the training data likely underrepresented those segments, causing the model to learn inadequate patterns for them. This is a critical bias risk with serious health equity implications.
Why B is Wrong: Consistency refers to the model producing the same output given the same input across different runs or time periods. The problem described is not about inconsistent outputs for the same input but about systematically poor performance for specific population groups.
Why C is Wrong: Transparency refers to openness about how the AI model operates, what data it uses, and how it makes decisions. The compliance officer has already assessed the system, suggesting sufficient transparency exists to identify the performance gap.
Why D is Wrong: Inclusiveness is a design principle ensuring AI systems are designed to serve all users regardless of background. While related to the outcome, the specific risk type described - differential predictive accuracy across population segments - is most precisely categorized as bias.


NEW QUESTION # 89
Which of the following attacks would be the best to automate with AI during dynamic application software testing (DAST)?

Answer: C

Explanation:
Basic Concept: Dynamic Application Security Testing (DAST) tests running applications by sending various inputs to discover vulnerabilities. AI can significantly enhance DAST by intelligently generating diverse, targeted test payloads that traditional tools might miss. CompTIA SecAI+ covers AI augmentation of security testing methodologies.
Why C is Correct: Payload creation is highly suitable for AI automation during DAST. AI can generate diverse, contextually appropriate attack payloads such as SQL injection strings, XSS vectors, command injection attempts, and format string exploits tailored to the specific application ' s behavior observed during testing. AI can learn from the application ' s responses to previous payloads and generate increasingly targeted inputs, discovering vulnerabilities more efficiently than static payload databases.
Why A is Wrong: DDoS attacks are volume-based attacks designed to overwhelm network or application infrastructure. Automating DDoS during DAST is inappropriate as it would disrupt service availability rather than discover application security vulnerabilities, and it is harmful to legitimate operations.
Why B is Wrong: Data poisoning is an attack targeting AI/ML model training data integrity. It is relevant to securing AI systems but is not a DAST technique for testing web or software application security vulnerabilities during dynamic testing.
Why D is Wrong: Threat modeling is a structured analysis process performed before development or testing to identify potential threats and design appropriate countermeasures. It is a planning activity, not an attack technique that can be automated during dynamic application security testing.


NEW QUESTION # 90
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