CompTIA SecAI+ (CTIA_SECAI+)
Learn to secure AI systems and use AI in cybersecurity operations. Cover AI threats, access controls, governance, risk, and compliance in 3 days.
Register or Request Training
- Private class for your team
- Live expert instructor
- Online or on‑location
- Customizable agenda
- Proposal turnaround within 1–2 business days
Course Overview
CompTIA SecAI+ helps cybersecurity professionals address emerging challenges at the intersection of artificial intelligence and security.
This vendor-neutral course focuses on securing AI systems and applying AI safely in cybersecurity operations. You will explore core AI and data concepts, threat modeling and controls for AI, access control and monitoring approaches, common AI attack techniques and compensating controls, and governance, risk, and compliance considerations for responsible AI adoption.
Course Benefits
- Apply foundational and advanced AI concepts to strengthen organizational cybersecurity
- Implement security controls and best practices to protect AI systems and data
- Use AI-driven tools and techniques to improve threat detection, response, and security operations automation
- Navigate governance, risk, and compliance considerations to support responsible AI adoption
Delivery Methods
Delivered for your team at your site or online.
Course Outline
- 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
- 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
- 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
- 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
- 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 summarization
- AI Governance, Risk, and Compliance
- AI governance structures
- AI organizational roles
- Responsible AI principles
- AI risk identification and assessment
- AI regulatory themes
- Compliance frameworks for AI
- Organizational AI policy design
- Compliance reporting
Class Materials
Each student receives a comprehensive set of materials, including course notes and all class examples.
Class Prerequisites
Experience in the following is required for this Cybersecurity class:
34 years of IT experience, including approximately 2 years of hands-on cybersecurity experience.
Experience in the following would be useful for this Cybersecurity class:
Holding a CompTIA cybersecurity certification (for example, Security+, CySA+, or PenTest+) or equivalent knowledge.
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