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 is implementing federated learning to train a medical diagnosis model across 15 hospitals without centralizing patient data. The security team must address vulnerabilities specific to this architecture. Which threats are unique to federated learning deployments? (Select two!)
Multiple correct answersAGradient leakage exposing training samples
BSQL injection attacks
CMalicious participants poisoning the global model
DCross-site scripting
EBuffer overflow exploits
Explanation
Federated learning faces specific threats including gradient leakage where transmitted gradients can be reverse-engineered to reconstruct training data, and model poisoning where malicious participants submit corrupted model updates to compromise the global model. SQL injection, cross-site scripting, and buffer overflows are general application security issues not specific to federated learning architectures.
2. Litware Inc. implements differential privacy with epsilon=0.1 for salary analytics. Users complain that aggregate statistics are too noisy for business decisions. The privacy team suggests epsilon=5.0. What is the PRIMARY tradeoff? (Select one!)
AHigher epsilon increases computational cost exponentially
BHigher epsilon provides better utility but weaker privacy guarantees
CHigher epsilon strengthens privacy but reduces model performance
DHigher epsilon requires more training data to maintain accuracy
Explanation
In differential privacy, epsilon controls the privacy-utility tradeoff. Lower epsilon values (like 0.1) add more noise for stronger privacy but reduce utility. Higher epsilon values (like 5.0) add less noise, improving utility but providing weaker privacy guarantees. Computational cost and training data requirements are not directly tied to epsilon. The fourth option reverses the relationship.
3. Woodgrove Bank detected deepfake audio used in a social engineering attack where a fraudster impersonated the CFO's voice to authorize a wire transfer. The security architect is designing defenses against future deepfake attacks. Which three controls represent a defense-in-depth approach? (Select three!)
Multiple correct answersAImplement AI-based deepfake detection tools to analyze audio and video for manipulation artifacts
BEstablish procedural controls requiring multi-channel verification for high-value financial transactions
CDeploy Content Credentials based on the C2PA standard to verify media authenticity from trusted sources
DEliminate all voice-based communication throughout the organization
ERely solely on employee training to detect deepfakes without any technical controls
FAssume deepfake detection AI is sufficiently accurate and requires no additional procedural safeguards
Explanation
Defense-in-depth against deepfakes requires layering technical and procedural controls. AI-based deepfake detection analyzes media for manipulation artifacts but is not foolproof as detection tools lag behind generation quality. Procedural controls like multi-channel verification (confirming requests via separate channels) create friction that defeats many social engineering attempts even when detection fails. C2PA Content Credentials provide cryptographic provenance for media authenticity. Eliminating voice communication is impractical. Training alone is insufficient as deepfakes become more sophisticated. Over-relying on AI detection is dangerous because detection accuracy decreases as generation improves. Effective defense requires multiple overlapping layers.
4. Adatum's security team is implementing an AI Software Bill of Materials for their threat intelligence analysis model. Which three components must be included in a comprehensive AI SBOM? (Select three!)
Multiple correct answersATraining data sources, including origin, version, and data collection methodology
BThird-party libraries and dependencies used in model training and inference
CModel architecture details including layer types and key structural choices
DIndividual employee names who contributed to model development
EPhysical server hostnames where the model is deployed
FMarketing materials describing the model's business value
Explanation
An AI SBOM documents the components and dependencies of an AI system for security, compliance, and supply chain risk management. Critical components include training data provenance to identify data poisoning risks and licensing issues, third-party libraries and dependencies to track vulnerabilities and supply chain attacks, and model architecture details for security analysis and reproducibility. Employee names, deployment infrastructure hostnames, and marketing materials are not part of the technical SBOM focused on security and reproducibility concerns.
5. Contoso's research team suspects their customer recommendation model is vulnerable to model inversion attacks where adversaries could reconstruct personal customer data from model outputs. Which defenses are most effective against this threat? (Select two!)
Multiple correct answersAAdding differential privacy noise to model outputs during inference
BRounding or truncating prediction confidence scores to reduce information precision
CImplementing two-factor authentication for all user accounts
DEnabling SQL injection protection on the database layer
EInstalling endpoint antivirus on model servers
Explanation
Model inversion attacks exploit the precision of model outputs to reconstruct training data. Differential privacy adds mathematically calibrated noise to outputs, bounding the information that can be inferred about training data. Rounding or truncating confidence scores reduces the precision of information available to adversaries while maintaining model utility. Two-factor authentication protects API access but does not prevent inversion by authorized users. SQL injection protection and antivirus address different threat categories unrelated to ML model inversion.
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