CompTIA SecAI+ Certification Training (Instructor-Led Online)

Learn how to secure, govern and responsibly integrate artificial intelligence into your cybersecurity operations. This training provides the most comprehensive approach to earning the CompTIA SecAI+ certification — one of the most relevant new credentials for today’s AI-enabled security landscape.

Prepare for your SecAI+ (V1) certification with access to the SecAI+ Complete Learning Bundle with live online instructor-led sessions, voucher Plus retake assurance. This complete learning solution equips you with the knowledge and exam prep tools needed to master SecAI+ (V1) concepts and pass your certification exam.

What's included:

• Live online instructor-led sessions
  
• Voucher Plus Retake Assurance
  
• CertMaster Perform

Key benefits:

• Comprehensive preparation: Master SecAI+ objectives with lessons and practice tools focused on AI concepts, securing AI systems, AI-assisted security, and AI governance, risk, and compliance.
  
• Interactive learning: Engage with videos, quizzes, and lab activities mapped to real-world AI security and cybersecurity tasks.
  
• Extensive practice: Access hundreds of questions, assessments, and timed practice tests to reinforce your knowledge.
  
• Gradable assessments: Track your progress with actionable feedback from practice tools.
  
• Exam attempt with retake assurance: Take your exam confidently, knowing you’re covered to retake it if needed.
  
• Year-long access: Redeem your product through CompTIA Central within 12 months of your access codes being made available, then enjoy 12 months of training access from the redemption date. 
  
• Easy voucher redemption: Redeem and schedule your exam at CompTIA Central.

Skills you'll learn

• Build skills with CompTIA learning and validate them with SecAI+ certification.
• Apply AI concepts to strengthen your organization’s cybersecurity posture.
• Secure AI systems using advanced controls and protections to safeguard data, models, and infrastructure.
• Leverage AI technologies to automate workflows, accelerate incident response, and scale security operations.
• Navigate global GRC frameworks to ensure ethical and compliant AI adoption across industries.
• Defend against AI-driven threats like adversarial attacks, automated malware, and malicious use of generative AI.
• Integrate AI securely into DevSecOps pipelines and enterprise security strategies.

Training Outline

Learning Objectives

Module 1 — AI and Data Concepts for Cybersecurity

• AI concepts and core AI types
  
• Generative AI and transformers
  
• Machine learning and deep learning
  
• Natural language processing
  
• AI model training approaches
  
• Prompt engineering fundamentals
  
• Model security considerations
  
• AI data types and data security techniques
  
• RAG (Retrieval Augmented Generation) concepts
  
• Data integrity and processing controls

Module 2 — Threat Modeling and Securing AI Systems

• AI threat modeling fundamentals
  
• Threat modeling processes and prerequisites
  
• AI threat modeling frameworks
  
• AI security control types
  
• Model guardrails and prompt templates
  
• Gateway and interface controls
  
• Usage quotas and limitation controls
  
• Security control testing

Module 3 — Access Controls for AI

• AI access control principles and models
  
• Model and agent access controls
  
• API and network access security
  
• AI data security controls
  
• Encryption and data safety measures
  
• Monitoring and logging AI systems
  
• Performance and cost monitoring
  
• AI auditing and compliance monitoring

Module 4 — AI Threats and Compensating Controls

• AI lifecycle security
  
• Ethical AI design considerations
  
• AI attack types and techniques
  
• Backdoor and trojan model attacks
  
• Model poisoning and inversion
  
• Model theft risks
  
• Compensating control strategies
  
• Post-incident AI analysis

Module 5 — Leveraging AI in Security Operations

• AI-enabled security tools
  
• AI use cases in detection and analysis
  
• AI for vulnerability assessment
  
• AI-enhanced attack vectors
  
• AI for social engineering and deception
  
• AI reconnaissance techniques
  
• AI-driven automation
  
• AI in DevSecOps workflows
  
• AI scripting and summarisation

Module 6 — AI Governance, Risk, and Compliance

• AI governance structures
  
• AI organisational roles
  
• Responsible AI principles
  
• AI risk identification and assessment
  
• AI regulatory themes
  
• Compliance frameworks for AI
  
• Organisational AI policy design
  
• Compliance reporting

Exam series code: CY0-001

Exam details

• Exam version: V1
• Exam series code: CY0-001
• Launch date: February 17, 2026
• Number of questions: Maximum of 60, multiple-choice and performance-based
• Duration: 60 minutes
• Passing score: 600 (on a scale of 100–900)
• Languages: English
• Recommended experience: 3–4 years in IT and 2+ years hands-on cybersecurity; Security+, CySA+, PenTest+, or equivalent recommended
• Retirement: Estimated 3 years after launch
  
  

SecAI+ (V1) exam objectives 

Basic AI concepts related to cybersecurity (17%)

• Explain core AI principles and terminology: Machine learning, deep learning, natural language processing, and automation.
• Identify AI applications in security: Use cases for AI in threat detection, defense, and security operations. 
• Recognize AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use of generative AI.

Securing AI systems (40%)

• Implement security controls: Protect AI systems, data, and models using robust technical safeguards. 
• Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures. 
• Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers. 

AI-assisted security (24%)

• Enhance detection and response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation. 
• Automate security workflows: Integrate AI for event triage, alert correlation, and response orchestration. 
• Apply AI techniques in operations: Incorporate AI into threat modeling, behavior analysis, and continuous monitoring. 

AI governance, risk, and compliance (19%)

• Understand regulatory frameworks: Identify global governance requirements and their implications for AI adoption. 
• Integrate GRC into AI projects: Incorporate governance, risk management, and compliance practices throughout the AI lifecycle. 
• Ensure responsible AI use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF