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CompTIA SecAI+ 

Exam Code : CY0-001 v1

CompTIA SecAI+ is a foundational, vendor-neutral certification designed to validate knowledge of artificial intelligence security and the secure use of AI systems. The course covers core concepts such as AI and machine learning fundamentals, data integrity, model security, adversarial AI threats, risk management, governance, and ethical considerations. It focuses on how AI can both strengthen cybersecurity operations and introduce new risks, preparing learners to assess, deploy, and protect AI-enabled systems responsibly. SecAI+ is ideal for cybersecurity professionals, IT practitioners, and AI-adjacent roles who want to understand how to secure AI technologies and align them with organizational security and compliance requirements.

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Why Join this Program

  • High-Demand Skills – Learn how to secure AI systems and manage emerging AI-driven cyber threats.

  • Career Advancement – Earn a globally recognized, vendor-neutral certification that boosts your professional credibility.

  • Practical Knowledge – Gain hands-on understanding of AI risks, governance, ethics, and secure deployment practices.

  • Future-Ready Advantage – Stay ahead in cybersecurity by mastering skills essential for AI-enabled organizations.

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Program Overview

The CompTIA SecAI+ program provides a comprehensive introduction to securing artificial intelligence systems in modern IT and cybersecurity environments. It covers essential AI and machine learning concepts, data security, model protection, and common AI-specific threats such as data poisoning and adversarial attacks. The program also emphasizes risk management, governance, compliance, and ethical use of AI, ensuring learners understand both the technical and policy aspects of AI security.

Designed for cybersecurity professionals, IT practitioners, and technology leaders, the program bridges the gap between AI innovation and security best practices. Through real-world scenarios and practical insights, SecAI+ prepares learners to assess, deploy, and manage AI-enabled systems securely, helping organizations adopt AI technologies with confidence and responsibility.

Key Features

1. Strong Foundation in AI Concepts for Cybersecurity

SecAI+ emphasizes understanding core AI technologies—such as machine learning, generative AI, transformers, GANs, NLP, LLMs/SLMs, and model training techniques—specifically in a cybersecurity context 

2. End-to-End AI Security Lifecycle Coverage

The certification addresses security across the entire AI lifecycle, including data collection, preparation, model development, deployment, monitoring, feedback loops, and human-in-the-loop validation 

3. Heavy Focus on Securing AI Systems (40% of Exam)

The largest exam domain concentrates on protecting AI systems, highlighting how critical AI security is in modern enterprise environments.

4. AI-Specific Threat Modeling and Attack Analysis

Candidates must understand AI-focused threat frameworks such as OWASP LLM Top 10, MITRE ATLAS, MIT AI Risk Repository, and CVE AI Working Group, along with real-world AI attack vectors

5. Practical AI Security Controls and Guardrails

SecAI+ covers hands-on security controls including prompt firewalls, guardrails, rate/token limits, access controls, encryption, and secure gateways, making it practical and implementation-oriented

6. Advanced AI Attack Detection and Mitigation

The exam trains candidates to analyze and respond to prompt injection, data/model poisoning, jailbreaking, model theft, inference attacks, AI DoS, and supply-chain attacks, along with applying compensating controls

7. AI-Assisted Security Operations

SecAI+ highlights how AI can enhance defensive security, including vulnerability analysis, anomaly detection, threat modeling, incident management, fraud detection, and automated penetration testing

8. Automation Using AI and AI Agents

The certification emphasizes security automation, covering AI-driven scripting, low-code/no-code tools, CI/CD security, automated testing, change management, and AI agents for security workflows

9. Governance, Risk, and Compliance (GRC) for AI

A dedicated domain addresses AI governance structures, responsible AI principles, risk management, and compliance with EU AI Act, NIST AI RMF, ISO standards, OECD guidelines, and corporate AI policies

10. Industry-Ready, Role-Oriented Certification

SecAI+ aligns with real-world roles such as AI security architect, MLOps engineer, AI risk analyst, AI auditor, and governance engineer, requiring hands-on cybersecurity experience and scenario-based problem solving

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Learning Path

Domain 1.0 – Basic AI Concepts Related to Cybersecurity.

  • 1.1 Compare and contrast various AI types and techniques used in cybersecurity

  • 1.2 Explain the importance of data security in relation to AI

  • 1.3 Explain the importance of security throughout the life cycle of AI

Domain 2.0 – Securing AI Systems

  • 2.1 Use AI threat-modeling resources

  • 2.2 Implement security controls for AI systems

  • 2.3 Implement appropriate access controls for AI systems

  • 2.4 Implement data security controls for AI systems

  • 2.5 Implement monitoring and auditing for AI systems

  • 2.6 Analyze AI attacks and suggest compensating controls

Domain 3.0 – AI-Assisted Security 

  • 3.1 Use AI-enabled tools to facilitate security tasks

  • 3.2 Explain how AI enables or enhances attack vectors

  • 3.3 Use AI to automate security tasks

Domain 4.0 – AI Governance, Risk, and Compliance.

  • 4.1 Explain organizational governance structures that support AI

  • 4.2 Explain risks associated with AI

  • 4.3 Summarize the impact of compliance on business use and development of AI

What Skills Will You Learn?

1. AI Fundamentals for Cybersecurity

  • Understand how AI, machine learning, and generative AI are used in security environments

  • Interpret AI models, prompts, and outputs from a security perspective

2. Securing AI Systems

  • Apply security controls to protect AI models, data, agents, and APIs

  • Design and enforce AI guardrails, prompt firewalls, and access controls

3. AI Threat Modeling & Attack Analysis

  • Identify and analyze AI-specific attacks such as prompt injection, poisoning, jailbreaking, and model theft

  • Use industry frameworks like OWASP, MITRE ATLAS, and MIT AI Risk resources

4. Data Security for AI

  • Protect sensitive data used in AI systems

  • Implement encryption, anonymization, masking, minimization, and classification strategies

5. Monitoring, Auditing, and Incident Detection

  • Monitor AI prompts, responses, logs, usage, and costs

  • Detect hallucinations, bias, accuracy issues, and misuse in AI systems

6. AI-Assisted Security Operations

  • Use AI tools to enhance threat detection, vulnerability analysis, incident response, and fraud detection

  • Improve SOC efficiency using AI-driven insights

7. Security Automation Using AI

  • Automate security tasks with AI agents, scripting tools, and low-code/no-code platforms

  • Integrate AI into CI/CD pipelines for testing, scanning, and deployment security

8. Understanding AI-Enabled Attacks

  • Recognize how attackers use AI for deepfakes, social engineering, reconnaissance, malware, and DDoS

  • Assess emerging AI-driven threat landscapes

9. AI Governance, Risk, and Compliance (GRC)

  • Apply Responsible AI principles (fairness, transparency, privacy, accountability)

  • Understand global AI regulations and standards such as EU AI Act, NIST AI RMF, ISO, and OECD

10. Enterprise & Role-Based AI Security Skills

  • Support roles like AI Security Architect, SOC Analyst, MLOps Engineer, GRC Analyst, and AI Risk Analyst

  • Align AI security practices with business objectives and compliance requirements

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Jobs You Can Land with the CompTIA SecAI+ Certification:

  • AI Security Analyst – Monitor, analyze, and secure AI systems by detecting threats such as prompt injection, model poisoning, and data leakage while ensuring safe AI operations.
  • AI Security Engineer – Implement and manage security controls for AI models, data pipelines, APIs, and AI agents, including guardrails, encryption, and access controls.
  • SOC Analyst (AI-Enabled) – Use AI-driven tools to enhance security monitoring, threat detection, incident analysis, and response across enterprise environments.
  • Machine Learning Security Engineer – Secure machine learning pipelines, training data, and models by preventing adversarial attacks and ensuring model integrity throughout the lifecycle.
  • MLOps Engineer (Security-Focused) – Manage secure deployment, monitoring, and maintenance of AI models, integrating security controls into MLOps and CI/CD workflows.
  • DevSecOps Engineer (AI Systems) – Automate security testing, scanning, and deployment for AI applications within CI/CD pipelines while enforcing secure development practices.
  • AI Risk Analyst – Identify, assess, and mitigate risks related to AI usage, including bias, data privacy, IP exposure, and model reliability.
  • AI Governance Engineer – Develop and enforce AI policies, standards, and oversight mechanisms to ensure responsible, ethical, and compliant AI use within organizations.
  • AI Compliance Analyst / Auditor – Evaluate AI systems and processes to ensure compliance with regulations such as the EU AI Act, NIST AI RMF, ISO standards, and corporate policies.
  • Cybersecurity Consultant (AI & Emerging Technologies) – Advise organizations on securely adopting AI technologies, improving AI security posture, and aligning AI initiatives with business and regulatory requirements.
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Exam Details

Course NameCompTIA SecAI+ 
Course Number:CY0-001  
Required examCY0-001 v1 
Number of QuestionsMaximum of 90 questions 
Type of QuestionsMultiple-choice and performance-based 
Length of Test90 Minutes 
Passing Score750 (on a scale of 100-900) 
RetirementUsually three years after launch 
LanguagesEnglish

Exam Preparation

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