01 What is CompTIA SecAI+ (CY0-001)?

CompTIA SecAI+ is the first vendor-neutral cert focused on AI security. It tests whether you can secure AI systems, assess AI-specific risks, implement governance frameworks, and defend against adversarial ML attacks.

Most security certs bolt on an AI chapter as an afterthought. Most AI certs ignore security entirely. SecAI+ was built for people who work at the intersection. If you're securing AI systems, advising on AI risk, or building security into ML pipelines, this is your cert.

90
Questions
165
Minutes
750
Passing Score (out of 900)
3-4yr
Recommended Experience

Mix of multiple-choice and performance-based questions, proctored through Pearson VUE (testing center or online). CompTIA recommends 3-4 years of hands-on cybersecurity experience with AI exposure, but there are no formal prerequisites. Anyone can register.

Key Takeaway

SecAI+ is not an entry-level cert. If you are new to cybersecurity, start with Security+. SecAI+ assumes you already understand network security, risk management, incident response, and governance fundamentals. What it tests is your ability to apply those concepts in AI-specific contexts.

02 Exam Domains and Weighting

Four weighted domains. The percentages tell you where to spend your study time.

Domain Weight
1.0 Basic AI Concepts Related to Cybersecurity
17%
2.0 Securing AI Systems
40%
3.0 AI-assisted Security
24%
4.0 AI Governance, Risk, and Compliance
19%

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

The basics. AI/ML terminology (supervised vs. unsupervised, neural networks, deep learning), risk assessment frameworks for AI, data classification for training data, and security implications of different deployment models (on-prem, cloud, edge, hybrid).

Domain 2: Securing AI Systems (40%)

40% of the exam. This is where the meat is. Secure data pipelines, model training integrity, supply chain security for ML libraries and pre-trained models, secure API design for model serving, input validation, and testing methodologies. DevSecOps but for ML pipelines.

Domain 3: AI-assisted Security (24%)

How threat actors attack AI systems and how you defend against them. Adversarial ML techniques (evasion, poisoning, model extraction, model inversion), prompt injection, LLM jailbreaking, deepfakes, and threat modeling with MITRE ATLAS.

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

The regulatory domain. AI ethics, bias detection, explainability, EU AI Act risk tiers, NIST AI RMF, ISO/IEC 42001, and organizational AI policies. Technical people tend to underestimate this section. Don't.

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03 Study Strategy: 8-12 Week Plan

Cramming won't work here. The four domains require different types of thinking: conceptual, technical, and regulatory. Spread it over 8-12 weeks. Here's the plan I followed.

Weeks 1-2

Foundations and Domain 1

Read the official exam objectives cover-to-cover. Study AI/ML fundamentals, security basics as they apply to AI, and risk assessment frameworks. Start daily practice questions to baseline your knowledge.

Weeks 3-4

Domain 2: Securing AI Systems

Deep dive into the heaviest domain. Study secure ML pipelines, data integrity, supply chain security, and secure API design. Practice hands-on with ML frameworks if possible. Start using spaced repetition for review.

Weeks 5-6

Domain 3: AI-assisted Security

Study adversarial ML techniques, prompt injection, MITRE ATLAS, and AI-specific attack vectors. Read published research on model poisoning and evasion attacks. This domain pairs well with hands-on labs.

Weeks 7-8

Domain 4: Governance, Risk, and Compliance

AI governance frameworks (NIST AI RMF, EU AI Act, ISO 42001), ethics, bias mitigation. Lighter weight but the regulatory questions are tricky. Don't skip this.

Weeks 9-12

Review, Practice Exams, and Weak Areas

Take full-length timed practice exams. Identify weak domains using analytics. Focus spaced repetition on questions you are getting wrong. Aim for 85%+ on practice tests before scheduling the real exam.

Spaced Repetition Strategy

Instead of re-reading material, you actively recall it at increasing intervals. SM-2 (same algorithm Anki and my app use) schedules questions based on how well you know each one. Get it wrong, it comes back tomorrow. Get it right five times, it shows up next month.

Pro Tip

Do your reviews first thing in the morning. 15-20 minutes on due cards, then move to new material. I found this worked way better than doing reviews at the end of the day when I was already fried.

Practice Test Strategy

Don't save practice tests for the end. Start taking short quizzes from week one to find your gaps. Longer exams (30-50 questions) by week five. Full timed exams by week nine. Track your scores. If they plateau, change your approach for those domains.

04 Key Frameworks and Standards

You will get tested on these. Not trivia questions about who published what, but scenario-based questions about when to use each one and how they differ.

Government Framework

NIST AI Risk Management Framework (AI RMF)

The big one. Four core functions: Govern (accountability and culture), Map (contextualize risks), Measure (analyze and assess), and Manage (prioritize and act). This is probably the most heavily tested framework on the exam. Know the four functions and their sub-categories cold.

Industry Standard

OWASP Top 10 for Large Language Models (LLMs)

The ten most critical security risks for LLM apps. Prompt injection, insecure output handling, training data poisoning, model DoS, supply chain issues, sensitive data disclosure, insecure plugins, excessive agency, overreliance, model theft. Maps straight to Domain 3. Read it.

Threat Intelligence

MITRE ATLAS (Adversarial Threat Landscape for AI Systems)

ATT&CK but for AI/ML systems. Catalogs adversarial techniques, tactics, and real-world case studies. Covers recon through impact: ML model access, adversarial attacks, data manipulation. If you know ATT&CK, the structure is familiar. The techniques are AI-specific.

Regulation

EU AI Act

EU's AI regulation. Four risk tiers: Unacceptable (banned), High risk (heavy compliance), Limited risk (transparency obligations), Minimal risk (no requirements). Know what falls into each tier and the compliance obligations. Yes, this is on the exam even if you're U.S.-based.

Management System

ISO/IEC 42001: AI Management System

First international standard for AI management systems. Same Plan-Do-Check-Act structure as ISO 27001. If you already know 27001, this maps pretty cleanly. Know how it connects to existing ISO frameworks and where it fits in an organization's AI governance strategy.

Study Approach

For each framework, create a one-page cheat sheet that answers: (1) Who published it? (2) What is its purpose? (3) What are its core components or functions? (4) When would you use it vs. the others? Being able to compare and contrast frameworks is a common exam question pattern.

05 Study Resources and Tools

No single resource covers everything. You'll need a mix. Here's what's actually worth your time.

Official CompTIA Materials

Practice Question Banks

Supplementary Reading

Online Courses

Communities

The only SecAI+ practice test app

670+ questions across every objective. 50 free to try. Premium from $2.99/week or $25 lifetime.

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06 Sample Questions with Explanations

Three questions pulled from the app. These reflect the style and difficulty you'll see on the real exam.

Question 01 | Domain 1: Basic AI Concepts Related to Cybersecurity
An organization is implementing its first AI system for automated fraud detection. Which framework should they use FIRST to systematically identify and prioritize AI-specific risks before deployment?
  • A. OWASP Top 10 for LLMs
  • B. NIST AI Risk Management Framework (AI RMF)
  • C. MITRE ATLAS
  • D. ISO/IEC 42001
Correct Answer: B

NIST AI RMF is the right call because the question asks about systematically identifying risks before deployment. That's exactly what AI RMF's Govern-Map-Measure-Manage structure is built for. OWASP Top 10 for LLMs (A) is LLM-specific, not general fraud detection. MITRE ATLAS (C) catalogs attack techniques but isn't a risk management framework. ISO/IEC 42001 (D) is about organizational AI management processes, not initial risk identification.

Question 02 | Domain 3: AI-assisted Security
A security analyst discovers that an ML model's classification accuracy has dropped from 96% to 71% over two weeks, but the input data distribution appears unchanged. Which attack technique is MOST likely responsible?
  • A. Model inversion attack
  • B. Evasion attack
  • C. Data poisoning attack
  • D. Model extraction attack
Correct Answer: C

Data poisoning corrupts training data to degrade model performance over time, which matches the gradual accuracy drop. Model inversion (A) reconstructs training data from outputs but doesn't hurt accuracy. Evasion attacks (B) fool individual predictions, not overall performance. Model extraction (D) copies the model through queries but doesn't affect the original.

Question 03 | Domain 4: AI Governance, Risk, and Compliance
Under the EU AI Act, a company develops an AI system used for real-time biometric identification in public spaces by law enforcement. How would this system be classified?
  • A. Unacceptable risk (prohibited)
  • B. High risk (subject to compliance requirements)
  • C. Limited risk (transparency obligations only)
  • D. Minimal risk (no specific obligations)
Correct Answer: A

Unacceptable risk (prohibited). The EU AI Act explicitly bans real-time remote biometric identification in public spaces for law enforcement, with very narrow exceptions. This is a specific prohibited use case. High risk (B) is for things like credit scoring or hiring tools. Limited risk (C) is chatbots with transparency requirements. Minimal risk (D) is spam filters and video game AI.

Notice the pattern: every question is scenario-based. You need to know when to apply each framework, how to diagnose attacks from symptoms, and what regulations actually mean in practice. Memorizing definitions won't cut it.

07 Exam Day Tips

No hack replaces preparation. But these tips help you avoid throwing away points you actually know.

Pacing

Budget Your Time

Budget roughly 1 minute per question. Flag difficult questions and come back to them. Do not spend more than 2 minutes on any single question during your first pass. Use remaining time to review flagged items.

Strategy

Read Every Word

CompTIA loves qualifiers like "MOST likely," "BEST," and "FIRST." Two answers may both be technically correct, but only one is the BEST answer for the specific scenario described. The scenario context matters.

Elimination

Process of Elimination

If you are not sure, eliminate obviously wrong answers first. Getting from four options to two gives you a 50% chance even if you need to guess. Never leave a question blank.

PBQs

Handle Performance-Based Questions

Performance-based questions (PBQs) often appear at the beginning. Many candidates skip them, do the multiple-choice questions first, then return to PBQs with remaining time. This is a valid strategy.

Preparation

The Night Before

Do a light review of your cheat sheets the evening before. Do NOT cram. Get a full night of sleep. Eat a solid breakfast. Bring your two forms of ID. Arrive 30 minutes early.

Mindset

Trust Your Preparation

If you have been scoring 85%+ on full practice exams consistently, you are ready. Anxiety during the exam is normal. Trust the work you have put in and move methodically through each question.

08 Frequently Asked Questions

How hard is the CompTIA SecAI+ exam? +
Advanced-level. CompTIA recommends 3-4 years of hands-on security experience with AI exposure. Four domains covering AI fundamentals, securing AI systems, AI-assisted security, and governance. If Security+ was moderately hard for you, expect a real step up. 8-12 weeks of structured study with relevant experience should get you there.
What is the passing score for CompTIA SecAI+? +
The CompTIA SecAI+ (CY0-001) uses a 100-900 scaled score. Based on CompTIA's precedent with other security certifications (Security+, CySA+, PenTest+, CASP+), the passing score is expected to be 750. CompTIA uses scaled scoring methodology, so the number of raw correct answers needed can vary slightly between exam forms. Check CompTIA's official page for the confirmed cut score closer to your exam date.
How long should I study for the SecAI+ exam? +
8-12 weeks at 1-2 hours per day. Strong in both security and AI/ML already? Maybe 6 weeks. New to either? Plan for 12-16. Daily spaced repetition beats weekend cram sessions every time.
What prerequisites do I need for CompTIA SecAI+? +
None. Anyone can register. That said, CompTIA recommends 3-4 years in cybersecurity with AI exposure. Security+ or equivalent knowledge is a solid baseline. Knowing AI/ML concepts and regulatory frameworks beforehand will save you a lot of study time.
Is CompTIA SecAI+ worth it in 2026? +
Yes. Every company is deploying AI and most security teams have no idea how to assess AI-specific risks. This cert says you do. It's vendor-neutral, it's the first of its kind, and it counts toward CompTIA's continuing education if you hold other CompTIA certs.
What study materials are available for CompTIA SecAI+? +
Official exam objectives PDF (free from CompTIA), CertMaster suite, SecAI+ Prep app (670+ questions, free tier on Google Play), NIST AI RMF docs, OWASP Top 10 for LLMs, MITRE ATLAS, Pluralsight/LinkedIn Learning courses, and r/CompTIA on Reddit. See the Study Resources section above for the full breakdown.

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About the Author

Moises Santiago

CISSP | 12 Security Certifications | AI Automations

12 security certs including CISSP. Built SecAI+ Prep because nothing existed for this exam and he needed to study for it himself. Taking SecAI+ on launch day using this app.