This CompTIA SecAI+ course enables a safer digital future by empowering IT and cybersecurity talent worldwide to meet the emerging challenges and opportunities at the intersection of AI and security.
CompTIA SecAI+ is the global IT industry’s first comprehensive “expansion” certification focused on the security of artificial intelligence systems and the secure application of AI in cybersecurity operations. This certification equips professionals with critical, vendor-neutral skills to understand, defend, and ethically deploy AI technologies within any organization.
Course Objectives
- Apply foundational and advanced AI concepts to strengthen organizational cybersecurity.
- Implement robust security controls and best practices for protecting AI systems and data.
- Leverage AI-driven tools to enhance threat detection, response, and automation of security operations.
- Navigate global governance, risk, and compliance frameworks to ensure responsible AI adoption.
Who Should Attend?
Ideal for those who currently hold a CompTIA cybersecurity certification (such as Security+, CySA+, PenTest+, etc.) or equivalent experience, and are looking to expand their skill set for evolving job roles in the context of AI technologies
Job roles that benefit from SecAI+ skills
- Security Analyst – Turn AI into an ally for threat detection and incident response, using it to surface patterns, model attacker behavior and act on threats more quickly.
- Cloud Engineer – Confidently design and maintain cloud environments that host AI workloads and data, with controls that support both security and compliance.
- SOC Analyst – Strengthen day‑to‑day operations in the SOC by using AI to flag anomalies, reduce alert fatigue and streamline how security events are handled.
- Penetration Tester / Security Consultant – Add AI systems to your testing playbook, evaluating models and data pipelines for unique vulnerabilities and providing clearer risk guidance to stakeholders.
- Senior Systems Administrator – Support AI projects running on the infrastructure you manage by understanding their governance, risk and compliance needs.
- Data Scientist / ML Engineer – Build and deploy models with stronger protections around training data, pipelines and outputs, while collaborating more smoothly with security teams.
- DevSecOps / CI/CD Engineer – Extend existing DevOps practices with AI‑aware security checks and automated tests that run before code or models reach production.
- Risk & Compliance Officer – Apply frameworks like GDPR and NIST AI RMF to real AI use cases, helping your organization meet legal and regulatory expectations.
- Military Cyber Operations Specialist – Defend mission‑critical AI and ML systems and better understand how adversaries might use AI in contested environments.
Course Prerequisites
3–4 years of IT experience with approximately 2 years of hands-on cybersecurity experience.
Course Length: 3 days
Course Outline
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 summarization
Module 6 — 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
SecAI+ (V1) exam objectives summary
– Basic AI concepts related to cybersecurity (17%)
– Securing AI systems (40%)
– AI-assisted security (24%)
– AI governance, risk, and compliance (19%)