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Platform & Architecture

145 posts on platform & architecture.

MCP Security Checklist: A Deployment Audit Instrument With Pass Criteria

A checklist you can run against a Model Context Protocol deployment, grouped into transport, authentication, authorization, egress, audit, and supply chain, with an explicit pass criterion for each item. It is written as an audit instrument rather than an essay, and it marks which items a network control can verify on the HTTP transport and which belong to host hardening for stdio servers.

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Azure OpenAI Gateway Patterns: Where the Governed Proxy Sits and What It Reads

Teams front Azure OpenAI with a gateway for four different jobs: token-based rate limiting, cost attribution, load balancing across regional deployments, and content policy on prompts. This article walks the three deployment patterns engineers actually run on Azure API Management, shows working GenAI policy config, and marks the line between what Azure APIM enforces at the request envelope and what governing the prompt payload requires.

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EPSS and KEV Patch Prioritization for the AI Stack: A Working Guide

CVSS severity alone tells you a vulnerability is bad, not whether it is being exploited. This guide shows how to combine the FIRST EPSS exploitation probability score with the CISA Known Exploited Vulnerabilities catalog to rank patch work across your AI inference stack, with a scoring rule, a worked example on real 2026 AI-tool CVEs, and an honest line on what patch prioritization does and does not cover.

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MCP Gateway Setup: Putting a Governed Proxy in Front of Model Context Protocol Traffic

An MCP gateway sits between agents and the MCP servers they call, centralizing authentication, per-tool-call authorization, egress control, and audit. This guide walks the setup on the streamable HTTP transport: terminate the connection, validate the OAuth token audience, authorize each tools/call against role and arguments, propagate identity to downstream servers, and log every decision. It marks where a local stdio server leaves the reach of a network gateway.

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LangChain Security Patterns: Governing Tools, Egress, and Output in a Chain

LangChain hands the model tools, memory, and retrieved context, and each one is a trust boundary the framework does not police for you. This article walks the security patterns that matter in a LangChain app: constrain what tools can do, propagate caller identity through the chain, validate output before it drives an action, and constrain egress. It marks which patterns live in your code and which need an enforcement layer on the HTTP path.

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MCP Security vs API Security: What Changes When Tools Are Discovered at Runtime

MCP looks like an API, so teams reach for their API security playbook and inherit its blind spots. This article walks four places where MCP security departs from classic API security: tools are discovered at runtime rather than fixed at design time, tool descriptions are an input the model trusts, the caller is often a non-human identity, and authorization is per tool call rather than per endpoint. It shows which API controls carry over and which do not.

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RAG Security Architecture: The Four Trust Boundaries in a Retrieval Pipeline

A RAG request crosses four trust boundaries before the answer comes back: what gets indexed, who can retrieve which chunks, what the assembled prompt carries into the model, and what the response returns. This article lays out the reference architecture for each boundary, marks which two are data-plane controls and which two sit on the HTTP path to the model, and shows where identity-bound policy and a per-decision audit record belong.

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Vertex AI Gateway Patterns: Governing Gemini Traffic on Google Cloud

Teams front Vertex AI with a gateway for three jobs: token quotas and cost attribution, regional routing for data residency, and content screening on prompts through Model Armor. This article walks the patterns engineers run on Apigee in front of Gemini, shows where Model Armor sits, and marks the line between screening scoped to Google Cloud and the identity-bound payload governance a mixed model estate needs.

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What Is an AI Control Plane? Control Plane vs Data Plane for AI Traffic

An AI control plane is the layer that decides policy, identity, routing, and audit for AI requests, kept separate from the data plane that carries the actual prompt and completion. This explainer borrows the control-plane and data-plane split from networking, applies it to LLM traffic, names the four components, and shows why separating the two produces deterministic policy and an independent audit record.

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AI Policy Enforcement at the HTTP Layer: Why the Request Boundary Is the Control Point

AI policy can be enforced at four layers: the model, the application, the network, and the HTTP request boundary. This piece walks each layer, shows why the model layer is probabilistic, the application layer self-attesting, and the network layer blind under TLS, and argues that the HTTP request boundary is the only point that sees decrypted prompt content plus identity and can make a deterministic, recordable decision.

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AutoGen Security Patterns for Multi-Agent LLM Traffic

AutoGen makes it quick to stand up multi-agent systems, but each agent opens outbound LLM calls that need identity-aware authorization and an audit trail. This walks through where the AI-traffic boundary sits and the security patterns that hold at machine speed.

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Cohere API Gateway Patterns: Governing Chat, Embed, and Rerank Traffic

Cohere exposes three distinct request shapes: v2 chat generation with the Command models, v2 embed, and v2 rerank, each a separate route with a different data-exposure profile. Routing Cohere traffic through a policy gateway lets a platform bind every call to a natural-person identity, apply per-route policy, constrain egress to the Cohere host, and record each decision. This walks the gateway patterns for the three endpoints, including the private-deployment case.

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