Insights
Short, decision-oriented resources for teams reviewing AI architecture, RAG systems, agentic workflows, MCP integrations, and AI delivery pipelines.
See how pipeline risk mapping and adversarial prompt testing can validate AI security controls before production, including prompt injection, data exfiltration, and system prompt leakage tests.
Presented by Alejandro Aucestovar at the SANS Cloud Security Exchange Summit on August 18, 2026.
Browse practical topics that help security, platform, and engineering leaders reason about AI risk before deployment decisions become difficult to change.
AI Security Architecture
An AI security architecture review is most useful when the team can show how the system makes decisions, where data flows, what the AI can access, which controls enforce boundaries, and how risky behavior would be detected or stopped.
Start with architecture diagrams, data classifications, identity models, agent permissions, retrieval sources, MCP servers, tool integrations, CI/CD workflows, approval gates, logging, monitoring, and incident response ownership. The goal is not paperwork. The goal is evidence that business risk has been understood before the system is trusted.
Before scheduling a review, collect the highest-level evidence you can share safely, identify unresolved architecture decisions, and avoid sending confidential diagrams or production details through email. Use the checklist page to prepare, then contact Assured-AI Security for an introductory consultation.