257 posts on industry verticals.
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.
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.
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.
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.
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.
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.
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.
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.
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.
HIPAA has required access logging on systems that touch protected health information for two decades. AI put a new door in the wall: clinicians and applications now send PHI to model APIs, often under a shared key and without a business associate agreement. This piece covers what the HIPAA audit and access requirements mean for AI traffic, and the request-layer logging that keeps model calls inside the same evidentiary standard as any other PHI system.
Insurers run models across underwriting, pricing, claims, and fraud, and regulators from the NAIC model bulletin to state algorithm rules now expect governance and records for those decisions. This piece covers why an AI gateway, the enforcement kind that binds identity and writes a per-decision record, fits the insurance control environment, and where its boundary sits against actuarial and model-risk work it does not replace.
Lawyers paste privileged client material into AI tools to draft and summarize, and a firm carrying a duty of confidentiality usually has no record of which matter that data belonged to or where it went. This piece shows how an AI gateway puts model traffic under matter-aware policy and produces the per-decision record a firm needs to show client data stayed inside its obligations.