CompTIA SecAI+ CY0-001 Exam Guide: AI Security Certification Explained

CompTIA SecAI+ CY0-001 is one of the most interesting additions to the cybersecurity certification landscape because it focuses on a problem that security teams can no longer treat as theoretical: how do you secure artificial intelligence systems while also using AI safely to improve cybersecurity operations?

Released in 2026, CompTIA SecAI+ sits at the intersection of traditional cybersecurity, artificial intelligence, machine learning, security automation, and AI governance. Unlike an introductory AI certification, CY0-001 does not simply ask candidates to define machine learning or explain what a large language model is. A significant portion of the exam focuses on protecting AI applications, models, data, APIs, agents, and supporting infrastructure from real-world security threats.

For cybersecurity students and early-career professionals, SecAI+ can also serve as a useful roadmap for understanding how familiar security principles such as least privilege, encryption, access control, monitoring, threat modeling, and incident response are changing as organizations adopt generative AI and AI-enabled applications.

Last updated: August 2026

What Is the CompTIA SecAI+ Certification?

CompTIA SecAI+ is a vendor-neutral cybersecurity certification designed around the security implications of artificial intelligence. The current exam is CompTIA SecAI+ V1, exam code CY0-001.

The certification validates a combination of AI knowledge and cybersecurity skills. Candidates are expected to understand important AI concepts, protect AI systems through technical security controls, use AI to support security operations, and understand the governance, risk, and compliance requirements surrounding enterprise AI.

This distinction is important. SecAI+ is not primarily a certification for becoming a data scientist or machine learning developer. Its perspective is much closer to that of a security engineer, security analyst, AI security architect, risk professional, or cybersecurity practitioner who must work with AI-enabled environments.

If you are preparing specifically for the certification, reviewing a structured CompTIA SecAI+ certification exam preparation resource alongside the official objectives can help you organize the large number of AI security concepts covered by CY0-001.

CompTIA SecAI+ CY0-001 Exam Details

Exam Information	Details
Certification	CompTIA SecAI+
Exam Code	CY0-001
Version	V1
Launch Date	February 17, 2026
Number of Questions	Up to 60 questions
Question Types	Multiple-choice and performance-based questions
Exam Duration	60 minutes
Passing Score	600 on a scale of 100–900
Exam Language	English
Recommended Experience	Approximately 3–4 years of IT experience, including roughly 2 years of hands-on cybersecurity experience

CompTIA recommends previous cybersecurity knowledge and experience before attempting the exam. Experience at the Security+, CySA+, PenTest+, or equivalent level can make the technical material considerably easier to understand.

For complete beginners, this means SecAI+ may be better treated as a specialization rather than a first cybersecurity certification. Understanding networking, identity, access control, encryption, vulnerability management, incident response, and security architecture will make the AI-specific concepts much more approachable.

CompTIA SecAI+ CY0-001 Exam Domains

The CompTIA SecAI+ CY0-001 exam is organized into four major domains:

Domain	Exam 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%

The weighting tells you something important about the philosophy behind the certification. Securing AI Systems accounts for 40% of the examination. In other words, SecAI+ is primarily an AI security certification rather than a general AI fundamentals exam.

Domain 1: Basic AI Concepts Related to Cybersecurity – 17%

The first domain gives candidates the technical vocabulary needed to understand the security problems covered throughout the rest of CY0-001.

You should be comfortable with major AI technologies and techniques, including:

• Generative AI
• Machine learning
• Statistical learning
• Deep learning
• Transformers
• Natural language processing
• Large language models (LLMs)
• Small language models (SLMs)
• Generative adversarial networks (GANs)

You should also understand commonly used model-training techniques such as supervised learning, unsupervised learning, reinforcement learning, model validation, and fine-tuning.

Terms such as epoch, pruning, and quantization are important because security professionals increasingly need to understand how AI models are developed and optimized before they can properly evaluate the risks associated with them.

Prompt Engineering

Prompt engineering is another important part of the domain. Candidates should understand the difference between system prompts and user prompts as well as common approaches such as:

• Zero-shot prompting
• One-shot prompting
• Multi-shot prompting
• Prompt templates
• System roles

This knowledge becomes particularly important later when studying prompt injection, jailbreaking, prompt firewalls, and model guardrails.

Data Security and AI

Artificial intelligence depends heavily on data, which makes data security a fundamental AI security issue.

CY0-001 expects candidates to understand concepts such as data cleansing, verification, integrity, lineage, provenance, augmentation, and balancing. You should also understand the differences between structured, semi-structured, and unstructured data.

Another major concept is Retrieval-Augmented Generation (RAG).

RAG allows an AI application to retrieve information from external knowledge sources before generating a response. This architecture can significantly improve the usefulness of generative AI, but it also introduces new security concerns involving access permissions, sensitive documents, embeddings, vector databases, retrieval authorization, and malicious content entering the retrieval pipeline.

The AI Life Cycle

Security should not begin only after an AI model reaches production.

Candidates should understand how security applies throughout the AI life cycle, including:

• Business use-case definition
• Data collection
• Data preparation
• Model development or selection
• Model evaluation
• Deployment
• Validation
• Monitoring and maintenance
• Feedback and iteration

Human-centric AI concepts are also important. Human-in-the-loop, human oversight, and human validation can provide critical safeguards when AI output influences sensitive business or security decisions.

Domain 2: Securing AI Systems – 40%

This is the largest and arguably most important part of the CY0-001 exam.

If you allocate your study time according to exam weighting, this domain deserves considerably more attention than any other section. Candidates following a structured CY0-001 exam study plan should therefore devote a substantial portion of their preparation to AI attacks, defensive controls, access management, and monitoring.

AI Threat Modeling

Traditional threat modeling remains valuable, but AI introduces new attack surfaces involving training data, inference interfaces, model weights, prompts, agents, plug-ins, APIs, and external data sources.

CompTIA expects candidates to become familiar with AI-specific threat intelligence and security resources, including:

• OWASP guidance for LLM and machine learning security
• MIT AI Risk Repository
• MITRE ATLAS
• CVE AI Working Group
• Threat-modeling frameworks

MITRE ATLAS, or the Adversarial Threat Landscape for Artificial-Intelligence Systems, is particularly useful because it organizes techniques used against AI-enabled systems in a way that cybersecurity professionals familiar with MITRE ATT&CK can understand.

Model and Gateway Security Controls

SecAI+ requires more than simply identifying attacks. Candidates must understand which defensive controls are appropriate for particular scenarios.

Important controls include:

• Model evaluation
• Model guardrails
• Prompt templates
• Prompt firewalls
• Rate limits
• Token limits
• Input quotas
• Modality restrictions
• Endpoint access controls
• Guardrail testing and validation

A useful way to think about this is that AI security requires several layers. The model may have its own safety mechanisms, but organizations should not rely on the model alone. Controls should also exist around APIs, gateways, identities, applications, data sources, and downstream systems.

Access Control for AI Systems

Identity and access management become more complicated when AI systems can interact with enterprise resources or perform actions through agents.

The exam considers access in several areas:

• Model access
• Data access
• Agent access
• Network and API access

The principle of least privilege remains extremely important.

For example, an AI assistant that only needs to search a documentation repository should not automatically receive write access to production databases or infrastructure-management APIs. AI agents should receive only the permissions necessary to complete their defined tasks.

AI Data Security Controls

AI applications can process enormous quantities of confidential information, intellectual property, credentials, personal data, and internal documents.

Candidates should understand encryption requirements for data:

• At rest
• In transit
• In use

Data-protection techniques covered by the objectives include:

• Data anonymization
• Data classification
• Data redaction
• Data masking
• Data minimization

Knowing the difference between these controls is more valuable than simply memorizing the terms. Expect scenario-based questions where you must select the most appropriate control for a particular privacy or security requirement.

Monitoring and Auditing AI Systems

Traditional application monitoring normally focuses on availability, latency, resource utilization, and errors. AI systems require additional visibility.

SecAI+ expands monitoring to include:

• User queries and prompts
• AI responses
• System logs
• Log sanitization and protection
• Response confidence
• Request rates
• AI processing and storage costs
• Hallucinations
• Accuracy
• Bias and fairness
• Access activity

This is an important shift in security thinking. An AI service can technically remain online and responsive while still producing unsafe, inaccurate, manipulated, or unauthorized results.

AI Attacks You Should Know

The attack list is one of the most important areas for CompTIA SecAI+ CY0-001 preparation.

You should be able to recognize threats such as:

• Prompt injection – malicious instructions manipulate an AI application’s intended behavior.
• Data poisoning – manipulated data influences training, fine-tuning, or retrieval.
• Model poisoning – the behavior or integrity of the model itself is compromised.
• Jailbreaking – attempts are made to bypass safety restrictions or model guardrails.
• Model inversion – an attacker attempts to infer information about data used by a model.
• Model theft – unauthorized extraction or acquisition of proprietary model functionality or assets.
• Membership inference – attempts to determine whether particular data was included in a model’s training set.
• Model denial of service – AI resources are exhausted or abused to disrupt availability or generate excessive costs.
• Sensitive information disclosure – confidential information appears in model responses or related components.
• Insecure output handling – AI-generated output is trusted by another component without adequate validation.
• Excessive agency – an AI system or agent receives more authority than it requires.
• AI supply-chain attacks – compromised models, datasets, libraries, components, or services affect downstream systems.

For the exam, do not study these as isolated definitions. Practice matching each attack to its evidence and then selecting an appropriate compensating control.

Examples of compensating controls include model guardrails, prompt firewalls, access controls, encryption, data-integrity controls, prompt templates, rate limiting, and least privilege.

Domain 3: AI-Assisted Security – 24%

AI introduces new security risks, but it can also improve the efficiency of cybersecurity teams.

Domain 3 examines both sides of that relationship.

Using AI for Security Operations

AI-enabled tools can support cybersecurity tasks through IDE plug-ins, browser extensions, command-line tools, chatbots, assistants, and other AI integrations.

Potential security use cases include:

• Signature matching
• Code quality and linting
• Vulnerability analysis
• Automated penetration testing
• Anomaly detection
• Pattern recognition
• Incident management
• Threat modeling
• Fraud detection
• Translation
• Security-document summarization

AI can dramatically reduce the amount of repetitive analysis required from a security analyst, but the exam also reinforces an important principle: AI output should not automatically be treated as authoritative.

Security professionals still need to verify recommendations, examine evidence, evaluate confidence, and maintain appropriate human oversight.

AI-Enhanced Cyberattacks

Attackers can benefit from AI just as defenders can.

Generative AI may enhance social engineering, reconnaissance, content generation, obfuscation, data correlation, and attack automation.

Deepfake technology introduces additional risks involving impersonation, misinformation, and disinformation.

AI can also accelerate the discovery of potential attack vectors or automate parts of malicious campaigns. From a defensive perspective, the important lesson is not simply that “AI makes attacks faster.” Security teams need to understand which parts of the attack lifecycle can be enhanced by automation and which controls can reduce the resulting risk.

Security Automation and AI Agents

Another major topic is using AI to automate security tasks.

Examples include:

• Low-code and no-code security automation
• Security-document synthesis
• Incident-response ticket management
• AI-assisted change approvals
• AI agents
• Code scanning
• Software Composition Analysis (SCA)
• Unit and regression testing
• Model testing
• Automated deployment and rollback

These capabilities can improve operational efficiency, but automation also increases the consequences of excessive permissions or incorrect AI decisions. Human approval, scoped authorization, logging, and rollback mechanisms remain essential.

Domain 4: AI Governance, Risk, and Compliance – 19%

The final domain moves beyond purely technical controls.

Enterprise AI security also depends on clear ownership, policies, risk management, regulatory compliance, and organizational accountability.

AI Governance Structures

Organizations increasingly establish formal governance structures such as an AI Center of Excellence to coordinate AI strategy, policy, security, compliance, and responsible adoption.

SecAI+ also expects candidates to recognize responsibilities associated with different AI-related roles, including:

• Data scientist
• AI architect
• Machine learning engineer
• Platform engineer
• MLOps engineer
• AI security architect
• AI governance engineer
• AI risk analyst
• AI auditor
• Data engineer

You do not need to become an expert in every role, but you should understand how their responsibilities differ.

Responsible AI and Risk

Responsible AI principles covered by the certification include:

• Fairness
• Reliability and safety
• Transparency
• Privacy and security
• Explainability
• Inclusiveness
• Accountability
• Consistency

Real-world AI risks can include biased results, accidental data leakage, reputational damage, intellectual-property issues, model performance problems, autonomous-system risk, and unauthorized or “shadow AI” use inside an organization.

AI Compliance

Cybersecurity professionals working with AI increasingly need to understand major governance frameworks and regulations rather than treating compliance as a separate legal function.

Important references in the SecAI+ objectives include:

• European Union AI Act
• OECD AI standards and principles
• ISO AI standards
• NIST AI Risk Management Framework
• Corporate AI policies
• Third-party compliance evaluations
• Data sovereignty

For most candidates, the best approach is to understand what each framework is trying to achieve and how it affects the design, development, deployment, and use of AI. Memorizing every regulatory clause is far less useful than understanding the practical risk-management principles behind them.

How Valuable Is the CompTIA SecAI+ Certification?

The value of SecAI+ comes from its specialization.

Traditional cybersecurity certifications remain extremely important, but organizations are now introducing AI into development workflows, customer-service platforms, security operations, enterprise search, cloud applications, analytics, and automated business processes.

As a result, security teams are being asked questions that traditional security programs were not originally designed to answer:

• Who is allowed to use public generative AI services?
• Can confidential information be submitted to an external model?
• How should an enterprise RAG application enforce document permissions?
• How can prompt injection be detected or mitigated?
• What permissions should an autonomous AI agent receive?
• How should an organization audit AI-generated decisions?
• What happens if a third-party model or dataset is compromised?
• How should AI risks be incorporated into governance and compliance programs?

These are practical enterprise-security questions, and they explain why AI security knowledge is becoming useful even for professionals who do not intend to build machine learning models themselves.

At the same time, candidates should keep expectations realistic. A certification does not replace cybersecurity experience, hands-on practice, or a strong understanding of security fundamentals. SecAI+ is most valuable when it complements existing security skills.

Who Should Consider CompTIA SecAI+?

The certification can be especially relevant for:

• Cybersecurity analysts
• SOC analysts
• Security engineers
• Incident responders
• Security architects
• Cloud security professionals
• Application security engineers
• AI security professionals
• Governance, risk, and compliance specialists
• Cybersecurity consultants
• IT professionals responsible for enterprise AI adoption

Students can certainly study the material as well, but someone completely new to cybersecurity may find it easier to build a foundation in networking and general security before attempting CY0-001.

Is CompTIA SecAI+ Difficult?

SecAI+ can be challenging because candidates must understand two technical disciplines at the same time.

You need enough AI knowledge to understand models, training, prompts, RAG, embeddings, agents, and AI workflows. At the same time, you need enough cybersecurity knowledge to apply access control, encryption, monitoring, threat modeling, data protection, vulnerability management, and risk-management principles to those technologies.

The exam also includes performance-based questions, so memorizing vocabulary alone is unlikely to provide sufficient preparation.

The strongest preparation method is to continually ask:

“Given this AI security problem, which control should I implement and why?”

That mindset is much closer to the style of knowledge CY0-001 is designed to validate.

How to Prepare for the CY0-001 Exam

1. Start With the Official Exam Objectives

Download the latest CompTIA SecAI+ exam objectives and use them as your master checklist.

AI security changes extremely quickly. Training courses, books, and online resources can become outdated much faster than traditional networking material, so always compare your study resources against the current CY0-001 objectives.

2. Spend More Time on Domain 2

Because Securing AI Systems represents 40% of the exam, your study schedule should reflect that weighting.

Spend extra time on:

• Prompt injection
• Jailbreaking
• Data and model poisoning
• Model inversion
• Membership inference
• Model theft
• AI supply-chain attacks
• Prompt firewalls
• Guardrails
• Access control
• AI data protection
• Monitoring and auditing

3. Understand Attack-to-Control Relationships

Instead of studying a list of attacks and then a separate list of controls, connect the two.

For every threat, ask four questions:

1. What does the attack attempt to accomplish?
2. What evidence might indicate the attack?
3. Which component of the AI architecture is affected?
4. Which preventative or compensating control is most appropriate?

This technique is particularly effective for scenario-based exam questions.

4. Learn RAG Security

Retrieval-Augmented Generation appears simple at first: retrieve information and send it to an LLM.

From a security perspective, however, a RAG architecture may involve document permissions, ingestion pipelines, embeddings, vector databases, APIs, external data, sensitive information, model prompts, and generated responses.

Understand how traditional authorization and data-security principles apply to every stage of this architecture.

5. Study OWASP and MITRE ATLAS

Do not treat OWASP and MITRE ATLAS as names that only need to be memorized.

Explore the actual frameworks.

Looking at real AI attack categories and defensive techniques will make the exam terminology much easier to remember and will also provide knowledge that remains useful after the certification exam.

6. Learn the NIST AI RMF at a Practical Level

The NIST AI Risk Management Framework is important for understanding how organizations approach AI risk systematically.

Focus on the relationship between governance, risk identification, measurement, and risk management rather than attempting to memorize an entire document word for word.

7. Practice Scenario-Based Questions

When reviewing practice questions, do not stop after checking whether your answer was correct.

Explain why every other option is less appropriate.

This is one of the best ways to identify situations where you recognize terminology but do not yet understand how the technology should actually be applied. Candidates who want additional preparation can also review CY0-001 CompTIA SecAI+ exam resources to reinforce important concepts and identify weaker knowledge areas.

A Practical Eight-Week SecAI+ Study Plan

Week	Main Focus
Week 1	AI, ML, deep learning, transformers, LLMs, SLMs, GANs, and training fundamentals
Week 2	Prompt engineering, AI data security, RAG, embeddings, and AI life-cycle security
Week 3	AI threat modeling, OWASP, MITRE ATLAS, model controls, and prompt security
Week 4	AI access control, encryption, data protection, monitoring, auditing, and attack analysis
Week 5	AI-assisted security operations, AI-enhanced threats, and security automation
Week 6	AI governance, responsible AI, NIST AI RMF, EU AI Act, ISO, and corporate policy
Week 7	Hands-on scenarios, PBQ-style exercises, and attack-to-control mapping
Week 8	Full review, practice exams, weak-domain remediation, and final objective checklist

The exact timeline depends on your background. Someone already working in cybersecurity may move quickly through traditional security concepts but need more time for machine learning and AI architecture. A student familiar with AI development may have exactly the opposite experience.

Hands-On Skills Worth Practicing

Even if you are primarily preparing for the certification exam, practical exercises can make the concepts far easier to understand.

Useful exercises include:

• Designing access controls for an enterprise RAG application
• Identifying where sensitive data could leak from an AI workflow
• Creating an AI threat model
• Mapping AI attacks to MITRE ATLAS techniques
• Comparing data masking, anonymization, redaction, and minimization
• Designing prompt and response logging requirements
• Creating a least-privilege permission model for an AI agent
• Building an incident-response workflow for suspected prompt injection
• Evaluating when a human approval step should be required
• Developing a basic organizational policy for approved and unapproved AI tools

These exercises teach the reasoning behind the exam objectives instead of reducing preparation to memorization.

CompTIA SecAI+ vs. Security+

SecAI+ should not be viewed simply as “Security+ with AI questions.”

Security+ provides broad cybersecurity knowledge covering threats, architecture, identity, security operations, risk, and security program management.

SecAI+ takes many of those principles and applies them specifically to AI-enabled systems.

A useful way to think about the relationship is:

• Security+ asks whether you understand the foundations of modern cybersecurity.
• SecAI+ asks whether you can apply security principles to AI systems and use AI responsibly within cybersecurity operations.

For that reason, candidates with a strong Security+ foundation will recognize many familiar principles, but the attack surface and implementation scenarios are substantially different.

Career Relevance of CompTIA SecAI+

AI security is unlikely to remain isolated as a niche responsibility handled by only one specialized team.

As AI becomes integrated into enterprise software, cloud platforms, SOC workflows, developer tools, automation systems, and business applications, cybersecurity professionals will increasingly encounter AI-related security requirements as part of normal security operations.

SecAI+ knowledge may therefore be useful in areas such as:

• AI application security
• Cloud security
• Security architecture
• Security operations
• AI governance
• Risk management
• Application security
• DevSecOps and MLOps security
• AI red teaming
• Security consulting

The certification alone does not guarantee a particular position, but it can provide a structured way to demonstrate knowledge in an area that is becoming increasingly relevant to enterprise security.

Frequently Asked Questions About CompTIA SecAI+ CY0-001

What is the CompTIA SecAI+ exam code?

The current CompTIA SecAI+ V1 certification exam uses the exam code CY0-001.

How many questions are on the CY0-001 exam?

The exam contains up to 60 questions and includes both multiple-choice and performance-based questions.

How long is the CompTIA SecAI+ exam?

Candidates receive 60 minutes to complete the CY0-001 exam.

What is the passing score for CompTIA SecAI+?

The passing score is 600 on CompTIA’s 100–900 scoring scale.

What is the most important SecAI+ exam domain?

Domain 2, Securing AI Systems, represents 40% of CY0-001 and is therefore the largest individual exam domain.

Do I need to know how to program AI models?

SecAI+ is focused primarily on securing and governing AI rather than turning candidates into machine learning developers. However, understanding AI architecture, model training concepts, prompting, data pipelines, RAG, APIs, automation, and AI development workflows is important.

Is SecAI+ suitable for beginners?

Students can learn a great deal from the SecAI+ curriculum, but CompTIA recommends several years of IT experience and hands-on cybersecurity knowledge. Beginners may benefit from first developing networking and cybersecurity fundamentals.

Does SecAI+ replace Security+?

No. The certifications address different scopes. Security+ focuses on broad cybersecurity fundamentals, while SecAI+ specializes in securing AI systems, AI-assisted security, and AI governance.

Should I study actual AI tools for the exam?

Yes, but concentrate on concepts rather than memorizing a particular vendor interface. SecAI+ is vendor-neutral. Understanding how AI tools, agents, APIs, RAG systems, security automation, and model controls operate is more valuable than specializing in a single commercial AI platform.

Final Thoughts

CompTIA SecAI+ CY0-001 reflects an important change in cybersecurity. Security professionals are no longer responsible only for protecting traditional servers, endpoints, networks, cloud services, and applications. They increasingly need to understand AI models, training data, prompts, agents, vector stores, AI APIs, model gateways, automated workflows, and the governance requirements surrounding those systems.

That makes SecAI+ particularly interesting for professionals who already understand cybersecurity fundamentals and want to extend those skills into AI security.

The best preparation strategy is not to memorize hundreds of AI terms. Build a clear mental model of how an AI system works, identify where it can be attacked, determine which data and identities require protection, select the appropriate technical controls, monitor the system after deployment, and understand how governance affects the entire AI life cycle.

If you are planning to take the certification, combine the official CompTIA objectives with practical labs, framework documentation, and a focused CompTIA SecAI+ CY0-001 exam preparation course. This approach can help you identify the concepts that need additional review while keeping your preparation aligned with the actual exam domains.

If you can consistently work through AI security decisions in realistic scenarios, you will not only be better prepared for the CY0-001 exam—you will also have developed skills that are increasingly relevant to real-world cybersecurity work.

External References and Official Resources

• CompTIA SecAI+ Official Certification Page – Official certification information and current SecAI+ resources.
• NIST AI Risk Management Framework – Official NIST guidance for managing artificial intelligence risks.
• OWASP GenAI Security Project – Security guidance covering generative AI, LLMs, and emerging AI application risks.
• OWASP GenAI LLM Top 10 2026 – Current OWASP guidance covering major security risks affecting LLM-powered applications.
• MITRE ATLAS – Adversarial Threat Landscape for Artificial-Intelligence Systems.
• European Commission – AI Act – Official information regarding the European Union’s regulatory framework for artificial intelligence.