CompTIA SecAI+ Study Guide: Everything You Need to Pass the CY0-001 Exam

The CompTIA SecAI+ certification is CompTIA's first Expansion Series cert, launched in February 2026. It validates your ability to secure AI systems, use AI for defensive security, and navigate the rapidly evolving AI governance landscape. Whether you are a security analyst integrating AI tools into your SOC workflow or a GRC professional evaluating AI compliance, this guide covers everything you need to pass the CY0-001 exam.

Exam Overview

• Certification: CompTIA SecAI+
• Exam Code: CY0-001
• Vendor: CompTIA
• Cost: ~$359 USD
• Duration: 60 minutes
• Questions: Up to 60 questions
• Passing Score: 600 out of 900
• Format: Multiple choice + Performance-based questions (PBQs)
• Prerequisites: None required (Security+, CySA+, or PenTest+ and 2+ years cybersecurity experience recommended)

Domain Breakdown

Understanding the exam domains and their weights is critical for efficient study planning. Domain 2 carries the most weight at 40% — nearly half the exam focuses on securing AI systems.

Domain 1: Basic AI Concepts Related to Cybersecurity (17%)

• 1.1 Compare and contrast AI types and techniques relevant to cybersecurity
• 1.2 Explain data security concepts in relation to AI
• 1.3 Explain security considerations throughout the AI lifecycle

Key concepts: Generative AI, Machine Learning, Deep Learning, NLP and LLMs, Transformers, GANs, Supervised/Unsupervised/Reinforcement Learning, Fine-Tuning, Prompt Engineering, RAG, Vector Embeddings, Data Lineage, Data Provenance, Watermarking, Human-in-the-Loop

Domain 2: Securing AI Systems (40%)

• 2.1 Explain AI threat-modeling resources
• 2.2 Explain security controls for AI systems
• 2.3 Explain access controls for AI systems
• 2.4 Explain data security controls for AI systems
• 2.5 Explain monitoring and auditing for AI systems
• 2.6 Given a scenario, analyze attack evidence and suggest compensating controls

Key concepts: OWASP LLM Top 10, OWASP ML Security Top 10, MITRE ATLAS, MIT AI Risk Repository, CVE AI Working Group, Prompt Injection, Model Poisoning, Data Poisoning, Jailbreaking, Model Inversion, Model Theft, Prompt Firewalls, Model Guardrails, Rate Limiting, Token Limits, Hallucination Detection, Bias Auditing, AI Cost Monitoring

Domain 3: AI-assisted Security (24%)

• 3.1 Describe AI-enabled tools used for security tasks
• 3.2 Explain how AI enables or enhances attack vectors
• 3.3 Explain how to use AI to automate security tasks

Key concepts: IDE Security Plug-ins, AI Chatbots for Security, MCP Servers, Anomaly Detection, Automated Pen Testing, Vulnerability Analysis, Deepfakes, AI-Enhanced Social Engineering, Adversarial Networks, Automated Malware Generation, AI Agents, CI/CD Security Scanning, Software Composition Analysis

Domain 4: AI Governance, Risk, and Compliance (19%)

• 4.1 Explain organizational governance structures for AI
• 4.2 Explain risks associated with AI
• 4.3 Explain the impact of compliance on AI

Key concepts: AI Center of Excellence, AI Security Architect Role, Responsible AI Principles, AI Bias, Shadow AI, Accidental Data Leakage, EU AI Act, OECD AI Standards, ISO AI Standards, NIST AI Risk Management Framework (AIRMF), Data Sovereignty, Sanctioned vs Unsanctioned AI

Recommended Study Timeline

Plan for approximately 8-10 weeks of dedicated study. Here is a suggested weekly breakdown:

• Week 1: Domain 1: AI Fundamentals — AI types, ML techniques, transformers, GANs, supervised/unsupervised/reinforcement learning
• Week 2: Domain 1: AI Data & Lifecycle — Data processing, RAG, vector embeddings, prompt engineering, human-in-the-loop
• Week 3: Domain 2: Threat Modeling — OWASP LLM Top 10, MITRE ATLAS, MIT AI Risk Repository, CVE AI Working Group
• Week 4: Domain 2: AI Security Controls — Prompt firewalls, guardrails, rate/token limits, gateway controls, endpoint access
• Week 5: Domain 2: Access & Data Controls — Model/data/agent/API access, encryption (transit/rest/in-use), anonymization, masking
• Week 6: Domain 2: Monitoring & Attack Analysis — Prompt monitoring, log protection, hallucination detection, bias auditing, AI cost monitoring
• Week 7: Domain 3: AI Security Tools — IDE/browser/CLI plug-ins, chatbots, MCP servers, anomaly detection, automated pen testing
• Week 8: Domain 3: AI Threats & Automation — Deepfakes, AI social engineering, AI agents, CI/CD scanning, automated incident response
• Week 9: Domain 4: Governance & Compliance — AI CoE, roles, EU AI Act, NIST AIRMF, OECD/ISO standards, Shadow AI, data sovereignty
• Week 10: Full Review: Practice tests, scenario-based labs, PBQ practice, OWASP/MITRE reference review, exam logistics

Top Study Tips

1. Master the OWASP LLM Top 10 and MITRE ATLAS. Domain 2 is 40% of the exam and heavily references these frameworks. Know each vulnerability category, its impact, and recommended mitigations.
2. Understand both sides: securing AI and using AI for security. The exam tests your ability to protect AI systems from attack AND leverage AI tools for defense. Do not focus on just one side.
3. Know the governance frameworks cold. EU AI Act risk tiers, NIST AI Risk Management Framework phases, and OECD AI principles show up in Domain 4 and overlap into Domain 2 scenario questions.
4. Practice scenario-based thinking. Domain 2.6 presents attack evidence and asks you to recommend compensating controls. Practice mapping attacks (prompt injection, model poisoning, jailbreaking) to specific mitigations (prompt firewalls, guardrails, access controls).
5. Time management is critical. You have only 60 minutes for up to 60 questions — roughly one minute per question. Flag uncertain questions and move on. Come back to PBQs after finishing multiple choice.

Practice Resources

Test your knowledge with our free tools:

Take our free CompTIA SecAI+ practice quiz

View the full CompTIA SecAI+ certification page

• Vulnerability Classifier — Practice classifying security vulnerabilities
• CVSS Calculator — Practice scoring vulnerabilities

Career Impact

The CompTIA SecAI+ certification positions you at the intersection of cybersecurity and AI — one of the fastest-growing specializations in the field. Certified professionals can expect:

• Access to emerging roles like AI Security Architect, AI Governance Engineer, and ML Security Analyst
• Higher earning potential as organizations scramble to secure their AI deployments
• Differentiation from peers with a credential that validates AI-specific security expertise
• Compliance readiness as EU AI Act and NIST AIRMF requirements become mandatory for organizations using AI

What to Study Next

After earning your CompTIA SecAI+ certification, consider these natural next steps:

• Deepen your AI expertise with cloud-specific AI certifications like Azure AI Engineer (AI-102) or AWS Machine Learning (MLS-C01)
• Strengthen your security foundation with CISSP or CySA+ if you have not already
• Explore hands-on offensive AI security through platforms like HackTheBox AI challenges
• Visit our Career Paths page for detailed certification roadmaps

Get Organized with a Study Planner

A structured study plan makes the difference between passing and failing. Our fillable PDF study planners include domain trackers, weekly schedules, and progress tracking designed specifically for CompTIA SecAI+ exam prep.

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This guide is independently created for educational purposes. CompTIA and SecAI+ are trademarks of CompTIA. FixTheVuln is not affiliated with or endorsed by CompTIA.