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CompTIA CY0-001 Exam Syllabus Topics:

Section	Weight	Objectives
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. Organizational AI governance structures 2. Responsible AI principles and ethics 3. Global standards: NIST AI RMF, EU AI Act
AI-assisted Security	24%	- AI for threat detection and response 1. Accelerated threat hunting 2. Anomaly detection and behavioral analysis 3. Automated incident triage and correlation - 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
Securing AI Systems	40%	- Security controls for AI systems 1. Model security: access, integrity, anti-tampering 2. Data protection: integrity, confidentiality, privacy 3. Deployment environment security - 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
Basic AI Concepts Related to Cybersecurity	17%	- AI applications in security 1. Security automation and decision support 2. Threat detection and anomaly analysis - Core AI principles and terminology 1. Machine learning, deep learning, NLP, automation 2. Generative AI concepts and capabilities - AI-driven threats and risks 1. Malicious use of generative AI 2. Adversarial machine learning attacks 3. Automated phishing, polymorphic malware

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CompTIA SecAI+ Certification Exam Sample Questions (Q134-Q139):

NEW QUESTION # 134
A security alert triggers an agentic system. An analyst notices the following payload in the logs. The alert includes multiple shell commands that are not typically run as part of any hardening:

Which of the following is the most effective control to implement?

A. Using only approved libraries when interacting with agentic systems
B. Adding logic that includes approved strings before running the shell commands
C. Modifying the application to ignore the SECURITY_UPDATE tag
D. Deprecating model usage and retaining the model with safer parameters

Answer: B

Explanation:
Basic Concept: Agentic AI systems that execute shell commands based on model-generated output are vulnerable to prompt injection attacks where malicious actors craft inputs that cause the agent to run unauthorized commands. Input validation using allowlists is a critical defense mechanism. CompTIA SecAI+ Study Guide covers agentic AI security controls.
Why A is Correct: Adding logic that validates shell commands against an approved allowlist before execution is the most direct and effective defense. This ensures only pre-approved, safe commands can be executed regardless of what the agentic system ' s model generates, preventing malicious command injection from reaching the operating system. This principle of allowlist-based input validation is a foundational secure agentic AI control.
Why B is Wrong: Deprecating and retraining the model is a lengthy process that addresses root cause training issues but does not provide immediate protection against ongoing injection attacks in the current deployed system.
Why C is Wrong: Modifying the application to ignore a specific tag merely removes one attack surface while leaving the system vulnerable to other injection vectors. It is not a comprehensive defense.
Why D is Wrong: Using only approved libraries controls which code libraries the agentic system can call, but does not validate or restrict the shell commands generated by the model at runtime based on arbitrary user input.

NEW QUESTION # 135
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?

A. Transparency
B. Bias
C. Inclusiveness
D. Consistency

Answer: B

Explanation:
The model's inability to reliably predict illnesses for certain population segments indicates bias in the dataset or training process. This leads to unfair or inaccurate outcomes for specific groups, making bias the most applicable risk in this case.

NEW QUESTION # 136
A security consultant needs to detect attacks across a large language model (LLM) firewall.
Which of the following techniques should the consultant use?

A. Signature matching
B. Translation analysis
C. Distributed denial-of-service
D. Vulnerability enumeration

Answer: A

Explanation:
Signature matching allows the detection of known malicious patterns, inputs, or behaviors targeting an LLM firewall. It is an effective technique for identifying and blocking prompt injection or other recognized attack methods.

NEW QUESTION # 137
A security architect performs threat modeling of an AI system. The architect needs to determine which attacks can be performed against the system.
Which of the following actions should the architect take next?

A. Analyze MITRE Adversarial Threat Landscape for AI Systems (ATLAS) for tactics, techniques, and procedures (TTPs).
B. Identify trust boundaries and perform threat modeling with Open Worldwide Application Security Project (OWASP) Top 10.
C. Quantify the risk of known vulnerabilities identified in the AI system.
D. Leverage a large language model (LLM) to map likely attack paths based on the code base.

Answer: A

Explanation:
Basic Concept: AI-specific threat modeling requires consulting resources that catalogue adversarial attacks specifically developed for AI and ML systems. General cybersecurity frameworks may miss AI-unique attack vectors such as model inversion, data poisoning, and adversarial examples. CompTIA SecAI+ Study Guide identifies MITRE ATLAS as the authoritative source for AI system TTPs.
Why D is Correct: MITRE ATLAS provides a comprehensive, curated knowledge base of adversarial tactics, techniques, and procedures specifically targeting AI and ML systems, derived from real-world attack case studies. Analyzing ATLAS enables the architect to enumerate realistic AI-specific attacks applicable to the system being threat-modeled, which directly answers the question of which attacks can be performed.
Why A is Wrong: Using an LLM to map attack paths introduces uncertainty and potential hallucination risk.
LLMs may generate plausible-sounding but inaccurate attack paths and cannot guarantee comprehensive coverage of AI-specific attack techniques.
Why B is Wrong: Quantifying risk of known vulnerabilities is a risk assessment step that occurs after identifying which attacks are possible. The architect must first identify attack possibilities before quantifying their risk impact.
Why C is Wrong: OWASP Top 10 covers web application vulnerabilities and, in its LLM edition, certain LLM-specific risks. However, MITRE ATLAS provides a more comprehensive and structured catalog of AI and ML-specific adversarial TTPs for systematic threat modeling.

NEW QUESTION # 138
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?

A. Transparency
B. Bias
C. Inclusiveness
D. Consistency

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 # 139
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