Secure Agentic AI | MCP, A2A, IAM and RAG Masterclass

About this course

This course contains the use of artificial intelligence.

Cyvitrix Learning creates and reviews the curriculum, scripts, explanations, examples, and learning design. Human subject matter experts remain responsible for accuracy, quality, and updates.

Secure agentic AI as a system of models, identities, tools, data, memory, permissions, decisions, and human accountability rather than as a single model endpoint.

What this course helps you doModel agent loops, tools, memory, retrieval, identities, permissions, and trust boundaries in an enterprise architecture.

Threat-model prompt injection, goal manipulation, poisoned tools, unsafe memory, excessive agency, and multi-agent abuse.

Design least-privilege identities and bounded delegation for people, agents, workloads, tools, and external services.

Evaluate MCP components and authorization flows without assuming that protocol support makes an integration secure.

Protect retrieval, vector stores, agent memory, tenant boundaries, secrets, tokens, and sensitive context.

Define approval gates, monitoring, audit evidence, incident response, rollback, and containment for agent actions.

Create a risk-based deployment roadmap that connects use cases, controls, owners, tests, residual risk, and review.

How the learning path develops

Start by mapping the agent loop and every data, control, identity, and authority flow. Progress through threat modeling, prompt and tool risks, MCP and A2A interactions, authorization, retrieval, memory, monitoring, incident response, and governance. Finish with a deployment decision that states the use case, safeguards, evidence, residual risk, and conditions for approval.

How to study effectively

Choose one agentic use case and draw its actors, models, tools, data stores, trust boundaries, and external dependencies.

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