Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
FDA oversight of AI-enabled medical device software keeps widening, yet the audit trail for the LLM calls clinical software makes over HTTP is usually missing. This walks through what FDA expects, where on-device inference sits outside the boundary, and how identity-bound policy plus per-decision records close the gap.
Read post →Enterprise buyers now send SaaS vendors AI-specific security questionnaires about how AI features are governed, logged, and attributed. This walks through what they ask, why promises fail an auditor, and the architecture that lets a vendor answer with evidence.
Read post →GxP pharma workflows are wiring LLM calls into validated systems, but 21 CFR Part 11 audit-trail and ALCOA+ data-integrity expectations do not stop at the application log. This walks through where AI calls sit under Part 11 and how an external per-decision record supplies attributable, tamper-evident evidence.
Read post →Telehealth platforms are wiring AI into visit summaries, triage, and scribing, which pushes PHI into LLM prompts that network DLP cannot read. This walks through the HIPAA obligations at the AI call boundary and the identity-aware policy and per-decision records that satisfy them.
Read post →Broker-dealers and RIAs are wiring AI into research, client communications, and portfolio workflows, but FINRA supervision and books-and-records duties attach to the output. This walks through the obligations and the per-decision records that supply supervisory evidence.
Read post →Responsible AI is usually a list of principles: fairness, accountability, transparency, human oversight. Those principles become governance only when a deterministic policy decision point enforces them and a per-decision record proves it. This walks through how to operationalize responsible AI.
Read post →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.
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.
A CrewAI crew is Python: agents with roles, tasks, tools, and a process that runs in your own process space. Most of that orchestration sits outside a network gateway. The part a gateway governs is the outbound HTTP traffic, when an agent calls an LLM through LiteLLM or reaches an external API through an HTTP tool. This walks the security patterns for that boundary: per-agent identity on model calls, prompt classification, egress control, and an audit record of what each agent asked the model.
Azure OpenAI addresses models by deployment name on a resource-specific host, authenticates with either an api-key header or a Microsoft Entra ID token, and carries an api-version query parameter on every call. This guide walks the setup for a governed gateway in front of those deployments: terminate the connection, validate the Entra ID token, bind a natural-person identity, apply per-deployment policy, constrain egress to the Azure host, and commit an audit record for each decision.
Groq serves an OpenAI-compatible API on its LPU hardware, which returns completions fast enough that a slow, out-of-band control has no time to act before the response lands. That speed is the argument for inline enforcement, not against it. This walks the gateway patterns for Groq traffic: point the OpenAI-compatible base URL at the gateway, bind each call to a natural-person identity, apply per-model policy, constrain egress to the Groq host, and record each decision.
Read post →Mistral is two deployment stories under one brand: the hosted La Plateforme API at api.mistral.ai with a Bearer key, and open-weight models a team runs itself behind vLLM, TGI, or Ollama. A single identity-bound gateway can govern both, because both are HTTP endpoints. This walks the Mistral gateway patterns: bind each call to a natural-person identity, apply per-model policy, constrain egress for the hosted case, cover the self-hosted endpoint the same way, and record every decision.