Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
Most AI governance best-practice lists stop at committees and policy documents. A reviewer tests the controls underneath them. This covers six AI governance practices that produce evidence rather than intent: an inventory derived from traffic, identity on every request, inline policy, independent audit records, vendor-AI coverage, and annual regulatory re-verification.
Read post →AI compliance monitoring tells you a policy was violated. At machine speed, that notice arrives after the violation completed. This covers the signals worth monitoring for a high-risk AI system, why monitoring produces forensic value rather than prevention, and where the boundary between watching AI traffic and enforcing policy on it actually falls.
Read post →Most AI compliance automation stops at workflow: reminders, questionnaires, and dashboards that track whether a policy was written. The evidence a regulator wants is generated somewhere else entirely. This explains why real AI compliance automation produces per-decision records at the point AI traffic is enforced, and what that changes about audit readiness.
Read post →An AI compliance audit fails on the evidence you cannot produce, not the policy documents you can. This checklist walks through what a reviewer requests for a high-risk AI system: the inventory, the identity mapping, the per-decision records, the retention proof, and the vendor-AI coverage, with the EU AI Act and Fannie Mae as the reference deadlines.
Read post →The EU AI Act, Fannie Mae LL-2026-04, NIST, HIPAA, and DORA all require an audit trail for AI decisions, using different vocabulary for the same underlying record. This maps the AI audit trail requirements across those regimes, shows what a compliant record contains, and explains why application logs fail the independence test every one of them assumes.
Read post →MCP''s authorization spec builds on OAuth 2.1, which sets the token rules but leaves the per-call decision to you. This walks through the MCP OAuth flow for remote servers, the confused-deputy risk it introduces, and why an enforcement point that evaluates every tool call against the acting identity is the control that OAuth alone does not provide.
A remote MCP server turns tool calls into HTTP traffic between an agent and an endpoint. That traffic is where an MCP gateway enforces identity-bound policy and records each tool invocation. This covers what an MCP gateway checks, why remote Streamable HTTP transport is the enforcement surface, and where local STDIO servers sit outside it.
Read post →An LLM gateway that only routes and rate-limits leaves the security decision to the model. This walks through what a security-grade LLM gateway evaluates on every request: verified identity, model authorization, prompt-level data classification, a fail-closed decision, and a per-decision audit record committed before the response returns.
Read post →A hands-on guide to running Model Context Protocol servers securely on the HTTP transport: stand up the server as an OAuth 2.1 resource server, validate token audience per request, authorize each tools/call against role and arguments, propagate caller identity downstream, constrain egress, and log every decision. Includes policy and validation snippets and a production checklist, plus the line where stdio servers leave the gateway.
Read post →Vercel AI Gateway and DeepInspect both sit between an application and model APIs, and they solve different problems. Vercel AI Gateway gives developers one endpoint for many models with routing, failover, and spend tracking. DeepInspect enforces identity-bound policy and produces per-decision audit records for regulated environments. This compares what each does, where each fits, and how to pick.
Read post →LLM security tools split into categories that solve different problems: guardrail libraries, gateways and firewalls, AI-aware DLP, red-team and testing suites, agent and MCP controls, and audit systems. Buying well means matching a category to your actual gap, not ranking products. This evaluates each category by what it enforces, where it sits, and what it structurally cannot do, then shows how to choose.
Read post →Prompt injection is a family of techniques, not one trick. Direct instructions, payloads hidden in retrieved content, splitting and obfuscation, multi-turn setups, and system-prompt extraction each work differently and need different containment. This walks through the main techniques with concrete examples, then maps them to the request-boundary controls that cap what a successful attack can reach.
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