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. Litware needs to ensure their training data hasn't been tampered with between collection and model training. Which two security controls should they implement? (Select two!)
Multiple correct answersAData lineage tracking
BCryptographic hash verification
CData augmentation
DFeature scaling
EOne-hot encoding
Explanation
Data lineage tracking documents the complete chain of custody and transformations applied to data from source to consumption, enabling detection of unauthorized modifications. Cryptographic hash verification (using SHA-256 or similar) allows validation that data files have not been altered by comparing current hashes to stored baseline hashes. Together, these controls ensure training data integrity and enable auditing. Data augmentation creates training variations. Feature scaling normalizes ranges. One-hot encoding converts categorical variables. These are preprocessing techniques, not security controls.
2. Fabrikam's security team is mapping adversarial attacks against their ML-based threat detection system using MITRE ATLAS. Which tactic is specific to ATLAS and NOT found in the traditional ATT&CK framework? (Select one!)
AML Model Access
BInitial Access
CCommand and Control
DPersistence
Explanation
ML Model Access is a tactic unique to MITRE ATLAS that covers techniques for gaining access to machine learning models through APIs, repositories, or inference endpoints. Traditional ATT&CK focuses on enterprise IT systems and does not include ML-specific tactics. The other options are standard ATT&CK tactics that also appear in ATLAS but are not ML-specific.
3. Proseware discovered an attacker poisoned their malware classification model by flipping labels in 5% of training samples (marking malware as benign). What type of poisoning attack is this? (Select one!)
ALabel flipping attack
BBackdoor attack
CModel inversion attack
DClean-label poisoning
Explanation
Label flipping attacks directly change the labels of training data (e.g., malware to benign) to degrade model performance. This is detectable if training data is monitored for inconsistencies. Clean-label poisoning is more sophisticated, modifying input features while keeping labels correct to introduce targeted misclassification. Backdoor attacks insert trigger patterns that cause specific misclassifications. Model inversion attempts to extract training data from a trained model and is not a poisoning technique.
4. Fabrikam implements a model serialization format for their neural network checkpoints. They want to avoid arbitrary code execution vulnerabilities present in Python pickle files. Which safer alternative should they use? (Select one!)
ASafeTensors format
BHDF5 format
CJSON format
DProtocol Buffers
Explanation
SafeTensors is a serialization format specifically designed for safely storing model weights and tensors. Unlike pickle, it does not allow arbitrary code execution during deserialization, making it the secure choice for ML model storage. Pickle can execute arbitrary Python code when loading, creating serious security risks. JSON lacks support for efficient tensor storage. HDF5 is better than pickle but has had security vulnerabilities. Protocol Buffers are general-purpose but not optimized for ML model weights like SafeTensors.
5. A security analyst is reviewing an attack taxonomy and needs to distinguish between MITRE ATT&CK and MITRE ATLAS frameworks. Which tactics are specific to MITRE ATLAS and have no direct parallel in ATT&CK? (Select two!)
Multiple correct answersAML Model Access
BInitial Access
CReconnaissance
DML Attack Staging
EPersistence
Explanation
MITRE ATLAS is the adversarial threat landscape framework specifically for AI/ML systems. While it shares many tactics with ATT&CK (such as Reconnaissance, Initial Access, and Persistence), it includes ML-specific tactics that have no direct ATT&CK counterpart. ML Model Access focuses on techniques to gain access to the ML model itself, and ML Attack Staging involves preparing ML-specific attacks. Organizations must study ATLAS independently, as familiarity with ATT&CK alone is insufficient for understanding AI-specific attack patterns.
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