Understand models, agents, MCP servers, and the AI attack surface.
This course gives technical and governance teams a grounded view of AI systems in production. It explains how models, agents, MCP servers, tools, data, and human oversight fit together, and where the real security and control boundaries live.
Learn the building blocks behind enterprise AI systems and the decisions that shape their risk profile.
The baseline architecture patterns behind LLM-powered systems.
What makes agentic systems powerful and brittle at the same time.
Test understanding of common deployment shapes.
Understand why MCP servers and tool boundaries are major control points.
How MCP expands capability while increasing blast radius if unmanaged.
Where policy, identity, and validation need to sit.
Inspect an AI estate and identify control gaps.
Move from architecture to concrete attack paths and security responses.
A practical breakdown of the most relevant attack classes.
Input, output, policy, and oversight controls that matter first.
Create an initial defense plan for a sample AI deployment.
Check readiness for runtime-focused learning.
A strong onboarding program for AI governance, application security, and platform teams adopting Quanterios AI or reviewing high-risk AI systems.
Recommended next steps after this course include ai-runtime, ai-compliance.