CompTIA SecAI+ (CY0-001) Practice Exams 2026

About the CompTIA SecAI+ CY0-001 Exam

CompTIA SecAI+ is a new certification launched in 2025 that validates skills in securing AI systems and using AI to enhance cybersecurity operations. It bridges the gap between traditional cybersecurity knowledge and emerging AI/ML security challenges, making it uniquely positioned for the modern threat landscape where AI is both an attack vector and a defensive tool. This is the first vendor-neutral certification focused specifically on the intersection of AI and cybersecurity, covering everything from adversarial machine learning and prompt injection attacks to AI-driven threat detection and automated incident response.

The CY0-001 exam consists of up to 90 questions (multiple-choice and performance-based) to be completed in 90 minutes, with a passing score of 750 on a 100-900 scale. Performance-based questions (PBQs) simulate real-world AI security scenarios—evaluating AI model vulnerabilities, configuring AI-enhanced security tools, and assessing AI governance frameworks. The exam costs $392 USD and is delivered at Pearson VUE testing centers worldwide or via online proctored exam. SecAI+ is valid for 3 years and is designed for cybersecurity professionals who want to specialize in the rapidly growing field of AI security.

SecAI+ CY0-001 Domains and Weighting:

• Domain 1: AI Concepts and Applications (20%) - AI/ML fundamentals, neural networks, NLP, generative AI, AI in cybersecurity applications
• Domain 2: AI Threats and Vulnerabilities (25%) - Adversarial attacks, prompt injection, data poisoning, model theft, AI-specific attack surfaces
• Domain 3: AI Security Architecture (20%) - Secure AI pipelines, model deployment security, data governance, responsible AI frameworks
• Domain 4: AI-Enhanced Security Operations (20%) - AI-driven threat detection, automated incident response, behavioral analytics, AI in SOC operations
• Domain 5: AI Governance and Ethics (15%) - AI regulations, bias mitigation, explainability, compliance frameworks, ethical AI deployment

SecAI+ targets cybersecurity professionals with 2-3 years of experience who want to expand into AI security. CompTIA recommends holding Security+ certification and having a basic understanding of AI/ML concepts before attempting the exam. Unlike vendor-specific AI certifications, SecAI+ validates knowledge applicable across all AI platforms and frameworks—from cloud-hosted LLMs and on-premises ML models to edge AI deployments. Candidates typically need 2-3 months of focused study combining AI fundamentals, AI-specific threat research, and hands-on practice with AI security tools.

Why Take CompTIA SecAI+?

• First-Mover Advantage in AI Security: As AI adoption accelerates across every industry, professionals who can secure AI systems are in critical demand. Organizations are deploying large language models, computer vision systems, and AI-powered automation at unprecedented scale—but few security teams have the expertise to identify AI-specific vulnerabilities like adversarial attacks, prompt injection, or training data poisoning. Earning SecAI+ now positions you at the forefront of this emerging discipline before the market becomes saturated, giving you a significant competitive advantage in the job market.
• Dual-Skill Validation in Cybersecurity and AI/ML: SecAI+ proves both cybersecurity expertise and AI/ML knowledge—a rare and valuable combination that few professionals currently possess. Traditional security certifications don't cover AI-specific threats, and AI certifications don't address security implications. SecAI+ bridges this gap by validating that you understand how AI systems work, how they can be attacked, and how to defend them. This dual-skill profile is exactly what organizations need as they integrate AI into critical business operations and security infrastructure.
• Vendor-Neutral AI Security Credential: Unlike vendor-specific AI certifications tied to a single platform (AWS, Azure, or Google Cloud), SecAI+ validates AI security knowledge applicable across all AI platforms and frameworks. Whether your organization uses OpenAI, Anthropic, open-source models, or custom-built ML systems, the security principles and threat models covered by SecAI+ apply universally. This vendor neutrality provides career flexibility and ensures your skills remain relevant regardless of which AI platforms your current or future employers adopt.
• Career Growth in an Emerging High-Paying Field: AI security specialists are among the highest-paid cybersecurity professionals, earning $100,000-$150,000 USD as organizations invest heavily in securing their AI systems. The demand for AI security expertise is growing faster than the talent pool can supply, creating significant salary premiums for certified professionals. Roles like AI Security Engineer, ML Security Analyst, and AI Red Team Lead are emerging across technology companies, financial institutions, healthcare organizations, and government agencies—all seeking professionals with validated AI security skills.

What You'll Learn in the SecAI+ CY0-001 Exam

The SecAI+ CY0-001 exam covers the intersection of artificial intelligence and cybersecurity, spanning AI fundamentals, AI-specific threats, secure AI architecture, AI-enhanced security operations, and AI governance. Unlike traditional security certifications, SecAI+ requires understanding both how AI systems work internally and how they can be exploited or leveraged for defense. The exam tests practical competency in identifying AI vulnerabilities, implementing AI security controls, and using AI tools to enhance cybersecurity operations.

AI/ML Security Fundamentals

• How AI Systems Work: Understanding machine learning pipelines (data collection, preprocessing, training, validation, deployment), neural network architectures (CNNs, RNNs, transformers), natural language processing fundamentals, generative AI models (LLMs, diffusion models), and the AI development lifecycle—knowledge essential for identifying where security controls must be applied
• Attack Surfaces Specific to AI: Identifying unique vulnerabilities in AI systems including training data integrity, model parameter exposure, inference API abuse, embedding extraction, and supply chain risks in ML libraries and pre-trained models; understanding how traditional cybersecurity threats manifest differently in AI contexts
• Data Pipeline Security: Securing the end-to-end data pipeline from collection through preprocessing to model training; implementing data validation, provenance tracking, and integrity checks; protecting training datasets from poisoning, exfiltration, and unauthorized modification; and ensuring data governance compliance throughout the ML lifecycle

AI Threat Landscape

• Adversarial ML and Prompt Injection: Understanding adversarial examples (evasion attacks, perturbation techniques), prompt injection attacks (direct and indirect injection, jailbreaking), model inversion attacks, membership inference attacks, and training data extraction; implementing defensive measures including input validation, output filtering, adversarial training, and prompt engineering best practices
• Model Poisoning and Deepfakes: Detecting and preventing data poisoning attacks (backdoor attacks, label flipping, clean-label poisoning), model supply chain attacks (trojaned pre-trained models, compromised ML libraries), deepfake generation and detection techniques, and synthetic media threats to organizational security
• AI-Powered Social Engineering: Recognizing AI-enhanced phishing attacks (LLM-generated spear phishing, voice cloning, video deepfakes), AI-driven reconnaissance and OSINT automation, automated vulnerability exploitation using AI tools, and defensive strategies against AI-augmented threat actors

AI-Enhanced Security Operations

• AI-Driven SIEM/SOAR: Leveraging AI and machine learning in security information and event management (SIEM) systems for anomaly detection, alert correlation, and false positive reduction; implementing AI-powered security orchestration, automation, and response (SOAR) playbooks; and understanding the capabilities and limitations of AI-driven security tools
• Behavioral Analytics and Threat Hunting: Applying user and entity behavior analytics (UEBA) powered by machine learning for insider threat detection, account compromise identification, and lateral movement detection; using AI-assisted threat hunting to identify advanced persistent threats (APTs) and novel attack patterns that evade signature-based detection
• AI in Vulnerability Management: Using AI for automated vulnerability prioritization based on exploitability, asset criticality, and threat intelligence; implementing ML-powered code analysis for security flaw detection; and leveraging AI for attack surface management, penetration testing automation, and security posture assessment

How to Prepare for the SecAI+ CY0-001 Exam

SecAI+ preparation typically takes 2-3 months for candidates with Security+ certification and basic AI/ML knowledge. The exam emphasizes practical understanding of AI security concepts—how AI systems are attacked, how to defend them, and how to use AI to enhance security operations. Candidates without cybersecurity background should first obtain Security+, while those without AI/ML knowledge should invest extra time building foundational understanding of machine learning concepts.

1. Build AI/ML Fundamentals (3-4 weeks): Start by developing a solid understanding of how AI and machine learning systems work. Study machine learning pipelines (data collection, preprocessing, feature engineering, model training, validation, deployment), neural network architectures (CNNs, RNNs, transformers), natural language processing, and generative AI models. You don't need to become a data scientist, but you must understand the technical components well enough to identify security implications at each stage. Free resources include Andrew Ng's Machine Learning Specialization (Coursera), fast.ai practical deep learning courses, and Hugging Face documentation for LLM fundamentals. Focus on understanding model architectures, training processes, and inference pipelines—these form the attack surface you'll need to secure.
2. Study AI-Specific Security Threats and Defenses (3-4 weeks): Deep dive into AI-specific attack vectors: adversarial examples, prompt injection (direct and indirect), data poisoning (backdoor attacks, label flipping), model theft and extraction, membership inference attacks, and model inversion. Study the OWASP Top 10 for LLM Applications and MITRE ATLAS (Adversarial Threat Landscape for AI Systems) framework for comprehensive threat taxonomy. Learn defensive techniques including adversarial training, input/output filtering, model monitoring, and secure model deployment practices. Review real-world AI security incidents—studying actual breaches and vulnerabilities provides context that theoretical knowledge alone cannot.
3. Hands-On Practice with AI Security Tools (2-3 weeks): Practical experience is essential for performance-based questions. Work with AI security tools and frameworks: use Adversarial Robustness Toolbox (ART) for adversarial attack simulation and defense, experiment with prompt injection techniques on local LLM deployments, practice with AI-powered SIEM features (Elastic Security ML, Splunk MLTK), and explore AI model security scanning tools. Set up a lab environment with open-source AI models to practice identifying vulnerabilities and implementing security controls. Understanding how AI-enhanced SOC tools work—behavioral analytics, automated threat detection, and AI-driven incident response—is critical for the AI-Enhanced Security Operations domain.
4. Complete Practice Exams and Review (2 weeks): In the final two weeks, take full-length timed practice exams to assess readiness and identify weak areas. Review the CompTIA SecAI+ Exam Objectives document to ensure complete coverage of all objectives. Focus additional study time on your weakest domains—AI Threats and Vulnerabilities (25%) and AI Concepts and Applications (20%) together represent 45% of the exam. On exam day, manage your time carefully across 90 questions in 90 minutes. For performance-based questions, if you're stuck, flag the question and return after completing multiple-choice sections. Review the official CompTIA SecAI+ page for current exam objectives and format details.

SecAI+ test-taking strategy: questions often present AI security scenarios requiring you to identify the correct threat, defense mechanism, or governance approach. Eliminate answers that confuse traditional IT threats with AI-specific threats—SecAI+ specifically tests your ability to distinguish between conventional cybersecurity and AI-unique security challenges. Budget 250-350 total study hours for candidates with Security+ and basic AI knowledge, or 400-500 hours for those building both skill sets simultaneously.

Frequently Asked Questions