CompTIA · CY0-001
CompTIA SecAI+ Certification (CY0-001) Practice Tests
CompTIA SecAI+ validates the skills needed to secure AI systems, apply AI responsibly within cybersecurity operations, and manage governance, risk, and compliance for AI-enabled environments. It is designed for experienced cybersecurity professionals with 2+ years of hands-on security experience.
Exam Details
Practice Questions
600
≈ 6 practice exams
Duration
60 minutes
Passing Score
600/900
Difficulty
ProfessionalLast Updated
Apr 2026
Topics Covered
Basic AI Concepts for CybersecuritySecuring AI SystemsAI-Assisted Security OperationsAI Governance, Risk, and ComplianceAdversarial AI and Threat MitigationAI Deployment and Security ControlsResponsible AI Practices
CompTIA SecAI+ Certification (CY0-001) Practice Exam Preparation
Use this CY0-001 practice exam to prepare for CompTIA SecAI+ Certification (CY0-001) with realistic questions, detailed explanations, and focused study modes. The practice bank includes 600 questions for CompTIA CY0-001, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to recurring topics such as Basic AI Concepts for Cybersecurity, Securing AI Systems, AI-Assisted Security Operations, AI Governance, Risk, and Compliance, and Adversarial AI and Threat Mitigation. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Basic AI Concepts Related to Cybersecurity17%
Securing AI Systems40%
AI-Assisted Security24%
AI Governance, Risk, and Compliance19%
Exam Overview
CompTIA SecAI+ (CY0-001) is a vendor-neutral professional certification that validates the knowledge and skills required to secure artificial intelligence systems and responsibly integrate AI into cybersecurity operations. Launched on February 17, 2026, it is the first certification in CompTIA's Expansion Series and the first vendor-neutral credential focused specifically on the intersection of AI and cybersecurity. The certification covers implementing technical security controls for AI models, gateways, and data pipelines—including model guardrails, prompt firewalls, encryption requirements, and data anonymization—alongside using AI-driven tools to automate threat detection, anomaly discovery, and incident response workflows.
The exam also addresses AI governance, risk, and compliance (GRC), requiring candidates to apply global regulatory frameworks such as GDPR and the NIST AI Risk Management Framework across the AI lifecycle. Accredited by ANSI to ISO 17024 standards, SecAI+ is valid for three years and requires continuing education units (CEUs) for renewal, following CompTIA's standard certification maintenance model.
Who Should Take This Exam
SecAI+ is designed for experienced cybersecurity practitioners who are integrating AI technologies into their security programs or are responsible for securing AI-enabled environments. Applicable job roles include security engineers, SOC analysts, blue-team operators, application security engineers, and security governance professionals seeking to validate AI-specific competencies alongside their existing security expertise.
CompTIA positions SecAI+ as a mid-level specialization that builds directly on core certifications such as Security+, CySA+, and PenTest+. It is well-suited for professionals already working in security operations, threat detection, or GRC roles who need to demonstrate competence in protecting AI pipelines, applying AI-driven automation, and navigating the compliance requirements of AI-enabled environments.
Prerequisites
There are no formal prerequisites required to sit for the CY0-001 exam. However, CompTIA strongly recommends candidates have 3–4 years of overall IT experience, including at least 2 years of hands-on cybersecurity experience, before attempting SecAI+. Prior attainment of Security+, CySA+, or PenTest+—or equivalent knowledge—is also recommended, as the exam assumes familiarity with core security concepts such as threat modeling, incident response, and risk management.
Candidates should also have a working understanding of foundational AI concepts—including machine learning terminology, model lifecycle basics, and common AI use cases—before diving into the AI-specific controls and governance frameworks that make up the bulk of the exam content. CompTIA positions SecAI+ as an add-on specialization rather than a standalone entry-level credential.
Exam Format
The CY0-001 exam consists of a maximum of 60 questions, combining multiple-choice and performance-based question (PBQ) formats. Performance-based questions require candidates to demonstrate practical skills through simulated scenarios rather than selecting from predefined answers. The total allotted time is 60 minutes, and the exam is delivered in English only.
Scoring is on a scale of 100 to 900, with a passing score of 600. The exam is available through Pearson VUE, which offers both online proctored and in-person testing center delivery options consistent with other CompTIA exams. No specific number of unscored pretest items has been published for this exam version.
Skills Measured
- 1.Domain 1 — Basic AI Concepts Related to Cybersecurity (17%): Covers comparing and contrasting AI types and techniques relevant to security, including Generative AI, Machine Learning, Deep Learning, NLP, LLMs, SLMs, GANs, and Transformers. Candidates must understand core AI principles and terminology and identify AI applications in threat detection, defense automation, and security operations.
- 2.Domain 2 — Securing AI Systems (40%): The largest domain, covering implementation of technical controls to protect AI systems, models, and data. Topics include model controls (model evaluation, guardrails, prompt templates), gateway controls (prompt firewalls, rate/token limits, input quotas), guardrail testing and validation, data security controls (encryption in-transit/at-rest/in-use, anonymization, classification labels, redaction, masking, and minimization), and defenses against adversarial attacks on AI infrastructure.
- 3.Domain 3 — AI-Assisted Security (24%): Focuses on leveraging AI-driven tools to enhance security operations. Candidates must demonstrate ability to use AI for threat detection, anomaly discovery, and incident response automation, as well as operationalizing AI-powered workflows within existing security programs.
- 4.Domain 4 — AI Governance, Risk, and Compliance (19%): Addresses applying global GRC frameworks—including GDPR, NIST AI RMF, and relevant industry standards—to AI-enabled environments. Covers ethical AI practices, legal standards, compliance monitoring, and governance across the full AI system lifecycle.
Study Tips
- Start with CompTIA's official CertMaster Study product (ISBN: 978-1-64274-646-4), which is purpose-built for CY0-001 and maps directly to the published exam objectives. It provides 12 months of access and covers all four domains with adaptive question banks.
- Download and study the official CompTIA SecAI+ Exam Objectives document (Version 1.0/2.0) from the CompTIA website. Map every sub-objective to your study notes, paying special attention to Domain 2 (Securing AI Systems, 40%) since it represents nearly half the exam.
- Use CertMaster Labs (ISBN: 978-1-64274-638-9) to build hands-on familiarity with performance-based question scenarios. PBQs simulate real-world tasks such as configuring prompt firewalls and applying data anonymization controls—skills that cannot be memorized from reading alone.
- Review the NIST AI Risk Management Framework (AI RMF 1.0) and GDPR AI-related provisions directly from their official sources. Domain 4 (GRC, 19%) tests practical application of these frameworks, so understanding their actual structure and terminology is essential.
- Study adversarial AI attack categories—including model inversion, data poisoning, prompt injection, and evasion attacks—using resources like MITRE ATLAS, which maps adversarial machine learning tactics to the MITRE ATT&CK framework and is directly applicable to Domain 2 objectives.
- Use CertMaster Perform (ISBN: 978-1-64274-637-2) in the final two weeks before the exam to simulate timed test conditions. The 60-minute limit is tight for up to 60 questions, so practice pacing yourself across both multiple-choice and performance-based items.
- Leverage the recommended certification pathway (Security+ → CySA+ → SecAI+) as a knowledge scaffold: revisit CySA+ threat detection and security operations content to reinforce the AI-assisted security domain, since Domain 3 builds directly on those analyst-level competencies.
Career Benefits
SecAI+ is positioned at the convergence of two of the fastest-growing areas in enterprise technology, and professionals who hold this credential can demonstrate competency for roles such as AI Security Engineer, Security Operations Analyst, Cloud Security Engineer, AI/ML Security Specialist, and GRC Analyst in AI-enabled organizations. CompTIA's recommended pathway places SecAI+ as a specialization after CySA+ or PenTest+, making it a credential that differentiates mid-career cybersecurity professionals in a crowded market.
The demand for professionals who can both secure AI systems and operationalize AI within security teams is expanding rapidly, with organizations across financial services, healthcare, government, and technology sectors facing mounting AI security incidents and growing regulatory pressure around AI governance. SecAI+ provides a vendor-neutral, ANSI/ISO 17024-accredited credential that signals verified competency to employers regardless of specific technology stack, complementing vendor-specific AI and security certifications from providers such as Microsoft, AWS, and Google.
Sample Questions
5 sample questions with answers and explanations. The full bank has 600 questions, enough for 6 full-length practice exams.
Preview — answers shown1. A security engineer at Woodgrove is hardening the hyperparameter configuration for a neural network used in network intrusion detection. Which hyperparameter security consideration is most critical? (Select one!)
AHyperparameters should be logged and version-controlled to prevent unauthorized modifications that could degrade model performance
BBatch size must be set to prime numbers to prevent cryptographic attacks
CLearning rate values should be encrypted during model training
DDropout rates should be randomized for each training epoch
Explanation
Hyperparameter security focuses on maintaining integrity and auditability. Unauthorized modifications to hyperparameters (learning rate, architecture choices, regularization) can intentionally degrade model performance, introduce backdoors, or cause the model to ignore certain attack patterns. Version control and logging provide traceability and prevent tampering. Encrypting learning rate values during training provides no security benefit since they are not sensitive data, batch size has no cryptographic significance, and randomizing dropout rates defeats the purpose of reproducible training and doesn't enhance security.
2. Trey Research uses AI-powered static and dynamic malware analysis tools in their SOC. An analyst receives alerts from both systems about a suspicious executable. The static analysis tool (analyzing code structure) flags it as benign, while the dynamic analysis tool (analyzing runtime behavior in a sandbox) identifies malicious activity. What is the BEST next step? (Select one!)
AAverage the confidence scores from both tools to make a final determination
BResubmit only to the static analysis tool with different parameters
CTreat as malicious and investigate further, as dynamic analysis detected harmful runtime behavior that static analysis missed
DTrust the static analysis result since it examines the actual code rather than behavior
Explanation
Dynamic analysis often catches malicious behaviors that static analysis misses, such as polymorphic code, delayed execution, or environment-specific triggers. When tools disagree, the detection of malicious behavior during runtime should trigger investigation, as static analysis can be evaded through obfuscation. This is a defense-in-depth principle where different analysis methods complement each other. Averaging scores or trusting only one method ignores the complementary nature of these techniques. Further investigation is warranted when any credible detection method identifies threats.
3. Tailspin's customer behavior prediction model is vulnerable to model inversion attacks where attackers reconstruct training data from model outputs. Which defenses directly mitigate this risk? (Select two!)
Multiple correct answersARounding prediction confidence scores to reduce output granularity
BRounding numerical outputs to limit precision of returned values
CImplementing rate limiting on model API endpoints
DUsing ensemble methods to combine multiple model predictions
EApplying input validation to reject malformed queries
Explanation
Model inversion attacks exploit high-precision outputs to reconstruct training data. Rounding confidence scores and rounding numerical outputs both reduce the information leakage in each response, making reconstruction significantly harder. Rate limiting helps with model extraction attacks but does not reduce per-query information leakage. Ensemble methods improve adversarial robustness but don't inherently limit output precision. Input validation prevents malformed queries but doesn't limit the granularity of returned outputs.
4. Wingtip Toys deployed an AI-powered recommendation engine that autonomously approves and executes product discounts, inventory transfers, and vendor orders without human review. A security assessment found the LLM could be manipulated through crafted user inputs to approve unauthorized bulk orders. Which OWASP LLM Top 10 risk does this autonomous decision-making architecture represent? (Select one!)
ALLM01: Prompt Injection
BLLM08: Excessive Agency
CLLM02: Insecure Output Handling
DLLM09: Overreliance
Explanation
LLM08: Excessive Agency occurs when an LLM-based system is granted too much autonomy, permissions, or capability to take consequential actions without appropriate human oversight. The system has the ability to execute real business transactions—discounts, inventory transfers, vendor orders—without any approval workflow. This excessive grant of authority means that any manipulation of the LLM (through prompt injection, social engineering, or model limitations) can have immediate financial and operational impact. Mitigation requires implementing least-privilege access, approval workflows for high-impact actions, and human-in-the-loop controls. Insecure Output Handling (LLM02) would involve passing unsanitized LLM text output to downstream interpreters like SQL or system commands. Overreliance (LLM09) refers to human operators trusting LLM outputs without verification, not the architectural grant of autonomous system authority. Prompt Injection (LLM01) describes a technique that could be used to exploit the excessive agency, but the root vulnerability is the excessive authority granted.
5. Northwind's legal team is reviewing their customer service chatbot that makes product recommendations without human intervention. Under GDPR Article 22, what is required for this system? (Select one!)
AAnnual third-party audit of the AI model and decision logic
BComplete prohibition since it performs automated decision-making
CHuman oversight is required only if decisions have legal or similarly significant effects on individuals
DExplicit user consent is required for all AI interactions regardless of impact
Explanation
GDPR Article 22 grants individuals the right not to be subject to solely automated decisions that produce legal effects or similarly significantly affect them. Product recommendations from a chatbot typically do not meet this significance threshold, so mandatory human oversight under Article 22 is not triggered. The system is not completely prohibited since it does not make legally significant decisions. User consent and annual audits are not specifically mandated by Article 22 for this type of low-impact recommendation system.
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