Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
HubSpot Breeze can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
Read post →Grok Enterprise can sit inside a valuable enterprise AI workflow, but DLP requires a decision point that sees the prompt, response, originating identity, and route before content reaches the model. This article defines that enforcement boundary and the evidence security teams need.
Read post →Anthropic says Claude models launched in the EU on or after August 2, 2026 carry machine-readable marks. That provider capability is useful, but a deployer’s evidence needs to show which identity received which model output under which disclosure policy at a specific time.
Read post →Research published on August 10, 2026 found that encrypted reasoning blocks could cross sessions, users, and models within provider families. Providers remediated the demonstrated extraction routes, while publicly shared traces still create a separate exposure that platform teams should govern as request-path data.
Read post →Taiwan disclosed a hybrid, agent-assisted campaign against government agencies in August 2026. The incident belongs to conventional intrusion response, but it also gives enterprise security teams a concrete reason to govern the HTTP model calls made by internal agents with identity-bound policy and decision records.
Read post →ISO 42001 certifies an AI management system, and a certification audit tests whether your controls operate and asks for evidence. Most AIMS documentation describes policy for AI use without a record that a given model call was governed. This piece maps AI gateway records onto the ISO 42001 controls an auditor samples, so operational control over AI use produces artifacts instead of assertions.
Read post →Different regimes impose different retention obligations on AI records, and the safe default is the longest applicable period. This piece walks the retention expectations behind EU AI Act record-keeping, SOC 2 observation windows, and financial-sector rules, then shows what a per-decision model call record has to carry to still be useful years after the request it documents.
Read post →Data residency for AI breaks the moment a prompt leaves your region for a model endpoint in another one, and application code rarely records where the call went. This piece shows how an AI gateway turns residency from a hopeful configuration into an enforced, logged control: identity and data class decide which regional endpoint a call may reach, and every decision is recorded with its destination.
Read post →Indirect prompt injection hides instructions inside content an AI agent reads, a web page, a document, a tool result, and the model acts on them as if they came from you. No request filter reliably stops a model from being fooled. This piece is honest about that limit and shows where the defensible control sits: identity-scoped authorization on what the agent may then do, plus a per-decision record of what it did.
Read post →An AI agent authenticates once and then makes hundreds of tool and model calls, most systems authorize the session and wave the rest through. That is the post-authentication gap. This piece shows what per-call authorization for agents looks like: each tool call checked against the agent identity, its scope, and the parameters it carries, decided inline, and recorded.
Read post →A generic API gateway routes requests, checks a key, and counts calls, which is why teams reach for one in front of their LLM traffic. It stops short of the decisions AI traffic needs: who the caller is at a user grain, what the prompt contains, and a record of the AI decision. This piece compares the two on what each can see and enforce, and where the boundary between them sits.
Read post →Logging an LLM request as if it were a generic HTTP call captures a timestamp, a status code, and a shared key, which answers almost none of the questions an incident or an audit asks. This piece defines the fields a model call record actually needs, why application code is the wrong place to write them, and what a per-decision record looks like in practice, with a concrete schema.
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