CompTIA SecAI+
CompTIA SecAI+ covers the CY0-001 exam objectives for professionals with IT and cybersecurity experience who need to secure AI systems and support AI use in security operations. Training focuses on AI concepts, machine learning, LLMs, RAG, data lineage, model guardrails, prompt firewalls, access controls, monitoring, and incident response.
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Description
CompTIA SecAI+ covers the CY0-001 exam objectives for professionals with IT and cybersecurity experience who need to secure AI systems and support AI use in security operations. Training focuses on AI concepts, machine learning, LLMs, RAG, data lineage, model guardrails, prompt firewalls, access controls, monitoring, and incident response. Participants also study adversarial threats, including prompt injection, data poisoning, model theft, and insecure plug-in design. Governance topics include the EU AI Act, OECD standards, ISO AI standards, and NIST AI RMF, with practical labs and scenario-based exercises where offered by the provider.
What You Will Learn
Course Content
Module 1: AI Concepts for Cybersecurity
Generative AI, machine learning, deep learning, transformers, NLP, LLMs, SLMs, and GANs
Supervised, unsupervised, and reinforcement learning
Prompt engineering, including system prompts, user prompts, zero-shot, one-shot, and multi-shot prompting
AI data concepts, including data integrity, provenance, lineage, embeddings, vector storage, and retrieval-augmented generation
Module 2: Securing AI Systems
AI system life cycle security, from data collection and model selection to deployment, validation, monitoring, and maintenance
Threat modeling with resources such as OWASP LLM Top 10, OWASP ML Top 10, MITRE ATLAS, and the MIT AI Risk Repository
Security controls for AI systems, including model guardrails, prompt firewalls, rate limits, token limits, endpoint access controls, and API access controls
Data security controls, including encryption, access control, data minimization, and protection against data leakage
Module 3: AI-Assisted Security Operations
Use of AI-enabled tools to support detection, triage, investigation, reporting, and security task automation
Review of AI-assisted threat analysis, anomaly detection, alert enrichment, and incident response workflows
Hands-on scenarios focused on applying AI responsibly in security operations
Module 4: AI Governance, Risk, and Compliance
AI risk management, policy controls, audit needs, and compliance considerations
Responsible AI use, human oversight, validation, documentation, and monitoring requirements
Training combines lectures, guided labs, case studies, and exam-style practice. After completion, participants should be able to explain key AI security concepts, apply controls to AI systems, use AI tools in security workflows, and prepare for the CompTIA SecAI+ CY0-001 certification exam.
Certification & Exam
This course prepares participants for the CompTIA SecAI+ (CY0-001) certification exam. The certification process is straightforward: build knowledge aligned with the exam objectives, schedule the official CompTIA exam, and pass the required assessment.
The exam uses multiple-choice and performance-based questions to assess practical understanding of AI concepts in cybersecurity, securing AI systems, AI-assisted security work, and AI governance, risk, and compliance.
CompTIA recommends candidates have 3 to 4 years of IT experience and about 2 years of hands-on cybersecurity experience. Participants who pass the exam earn the CompTIA SecAI+ certification.
For current requirements, refer to the official CompTIA SecAI+ certification page.
What You Will Achieve
Analyze core AI concepts used in cybersecurity, including generative AI, machine learning, statistical learning, transformers, deep learning, NLP, LLMs, SLMs, and GANs.
Apply model training and prompt engineering concepts, including supervised, unsupervised, and reinforcement learning, fine-tuning, system prompts, user prompts, zero-shot, one-shot, and multi-shot prompting.
Evaluate data security needs for AI systems, including data cleansing, verification, lineage, integrity, provenance, watermarking, embeddings, vector storage, and retrieval-augmented generation.
Implement security controls for AI systems to reduce risks such as bias, accidental data leakage, model performance issues, intellectual property exposure, and misuse of autonomous systems.
Use AI-assisted security methods to support security operations, improve detection and response workflows, and automate selected cybersecurity tasks in a controlled manner.
Assess responsible AI requirements, including fairness, reliability, safety, transparency, privacy, security, explainability, accountability, and consistency.
Apply AI governance, risk, and compliance concepts using references such as the EU AI Act, OECD standards, ISO AI standards, the NIST AI Risk Management Framework, corporate AI policies, and third-party compliance evaluations.
Training Providers
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