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Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.

Vertex AI Gateway Setup: A Governed Proxy in Front of generateContent

Vertex AI addresses models by a long resource path on a regional host, authenticates with a short-lived Google OAuth bearer token rather than a static key, and offers both the native generateContent shape and an OpenAI-compatible endpoint. This guide walks the setup for a governed gateway in front of Vertex: terminate the request, validate the Google identity, bind a natural-person identity, apply per-model policy, constrain egress to the regional host, and commit an audit record for each decision.

Platform & Architectureai-securityllm-securityidentity-and-authorizationpolicy-enforcementcloud-security
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Self-Hosted Llama Gateway Patterns: Governing an Inference Endpoint You Own

Running Llama on your own hardware with vLLM, TGI, or Ollama removes the third-party provider from the picture. It does not remove the identity, authorization, and audit problem, because a self-hosted inference server still speaks HTTP and still accepts a prompt from whoever can reach the port. This walks the gateway patterns for a self-hosted Llama endpoint: per-identity authentication on model calls, prompt classification, egress control that forces every call through one path, and a per-decision audit record of what each caller sent to the model you host.

Platform & Architectureai-securityllm-securityidentity-and-authorizationpolicy-enforcementarchitecture
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Semantic Kernel Security Patterns: Governing What Your Kernel Sends to the Model

Microsoft Semantic Kernel orchestrates plugins, planners, and memory in your own process. When the kernel needs a model, it calls an AI service connector that reaches an LLM endpoint over HTTP. That connector call is where network security patterns attach. This walks the gateway patterns for a Semantic Kernel deployment: per-identity on the connector, classification of the prompt the kernel assembles from functions and memory, egress control, and a per-decision audit record of what the kernel asked the model.

Platform & Architectureai-securityllm-securityagentic-aiidentity-and-authorizationpolicy-enforcement
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AI Security for Clinical Documentation: Governing the PHI That Reaches the Model

Ambient scribes and note-generation tools turn a clinical encounter into text by sending the transcript to an LLM over HTTP. That request carries protected health information out of the environment, and HIPAA holds the covered entity responsible for what happens to it. This walks the security patterns for clinical documentation AI: binding each call to a clinician identity, classifying PHI in the prompt, minimum-necessary enforcement at the request layer, and the per-decision audit record HIPAA audit controls expect.

Industry Verticalshipaaai-securitycomplianceidentity-and-authorizationauditshadow-ai
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AI Security for Data Teams: Governing What Analysts Send to the Model

Data analysts and scientists use AI to write SQL, explain query results, and draft analysis, which means schemas, sample rows, and sometimes regulated records travel to an LLM over HTTP. Legacy DLP runs underneath TLS and cannot read the prompt. This walks the security patterns for data teams: binding each call to an analyst identity, classifying regulated data in the prompt, egress control on the notebook and BI tiers, and a per-decision audit record of what left for the model.

Industry Verticalsai-securitydata-loss-preventiondlpidentity-and-authorizationshadow-aicompliance
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AI Security for Code Review Bots: Governing the Source Code That Leaves for the Model

An AI code review bot reads a pull request and sends the diff, and often surrounding files, to an LLM over HTTP. That request carries proprietary source and whatever secrets the diff contains out of the environment. This walks the security patterns for code review bots: binding each call to a bot and repository identity, classifying source and secrets in the prompt, egress control that keeps the model call on one path, and a per-decision audit record of what the bot sent to which model.

Industry Verticalsai-securityllm-securityidentity-and-authorizationpolicy-enforcementdevsecops
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AI Security for Financial Analysts: Governing What Reaches the Model

Financial analysts use AI to summarize research, check models, and draft memos, which sends holdings, client data, and sometimes material non-public information to an LLM over HTTP. Those calls sit inside a recordkeeping regime that expects the firm to know what was communicated and to retain it. This walks the security patterns for analyst AI use: binding each call to an analyst identity, classifying MNPI and client data in the prompt, egress control, and a per-decision audit record that serves the firm supervision obligation.

Industry Verticalsai-securitycomplianceregulationidentity-and-authorizationauditshadow-ai
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AI Security for DevOps: Governing What Copilots Send to the Model

DevOps and SRE teams use AI to write infrastructure code, triage incidents, and read logs, which sends configs, log lines, and sometimes secrets and production data to an LLM over HTTP. This walks the security patterns for DevOps AI use: binding each call to an engineer identity, classifying secrets and production data in the prompt, egress control from the tooling tier, and a per-decision audit record of what an engineer sent to the model during a change or an incident.

Industry Verticalsai-securitydevsecopsidentity-and-authorizationpolicy-enforcementcloud-securityshadow-ai
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Retail AI Pricing Compliance: Governing the Model Calls Behind a Price

Retailers now route parts of the pricing workflow through LLMs: generating pricing rules, analyzing competitor and demand signals, and powering merchandiser copilots. When those steps call a model over HTTP, regulators and plaintiffs will ask what data drove the decision and whether prohibited attributes were involved. This walks the compliance patterns for AI-assisted pricing: binding each model call to an identity, classifying the inputs in the prompt, and a per-decision audit record of what reached the model, with an honest boundary on what a gateway does not cover.

Industry Verticalsai-securityai-governancecomplianceregulationauditidentity-and-authorization
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AI Security for Legal Research: Governing What Reaches the Model

Lawyers use AI to research case law, draft, and review documents, which sends client facts, privileged analysis, and confidential material to an LLM over HTTP. The duty of confidentiality and the risk of a privilege waiver both attach to that request. This walks the security patterns for legal research AI: binding each call to a user and matter identity, classifying privileged and confidential content in the prompt, egress control, and a per-decision audit record of what was disclosed to the model.

Industry Verticalsai-securitycomplianceidentity-and-authorizationauditshadow-airegulation
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AI Security for SOC Teams: Governing What Analysts Send to the Model

SOC analysts use AI copilots to triage alerts, enrich indicators, and summarize incidents, which sends logs, IOCs, and internal telemetry to an LLM over HTTP. The tools the SOC runs to watch everything else, the SIEM and EDR, cannot read that prompt traffic. This walks the security patterns for a SOC using AI: binding each call to an analyst identity, classifying internal telemetry in the prompt, egress control, and a per-decision audit record of what left for the model during triage.

Industry Verticalsai-securityshadow-aiidentity-and-authorizationpolicy-enforcementcybersecurityaudit
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AI Agent Guardrails: Constraining a Loop That Chooses Its Own Next Call

Guardrails written for a single chat completion assume one request, one response, one human reading the output. An agent runs a loop: it calls a model, reads the result, picks a tool, calls the model again, and repeats without a human between the steps. That changes what a guardrail has to constrain. This covers the four controls an agent loop needs, why per-call classification is not enough, and how identity-bound authorization limits blast radius when an injected instruction succeeds.

AI Security Solutionsai-agent-securityai-guardrailsagentic-aiidentity-and-authorizationpolicy-enforcementai-security
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