LLM / Generative AI in Regulated Environments
Generative AI for regulated industries — compliant from architecture
What Regulated Teams Get Wrong with LLM / Generative AI
Generative AI deployment in regulated industries lives at the intersection of provider-side compliance posture, model-output safety, and the audit-trail requirements of the regulatory frameworks the deployment operates under. The provider-side question is the first filter: not every LLM provider offers contractual coverage required for regulated data. OpenAI's direct API does not offer a HIPAA Business Associate Agreement; OpenAI on Azure (Azure OpenAI Service) does offer a BAA through Azure's broader BAA. Anthropic offers a BAA for Claude on AWS Bedrock and a direct enterprise BAA for some configurations. Google Vertex AI offers a BAA. Sending PHI to any provider without an active BAA is a HIPAA violation regardless of how compliant the provider may appear in marketing materials. Model output safety in regulated contexts is qualitatively different from consumer chatbot safety: an LLM in a clinical decision-support workflow may produce outputs that, if acted upon by a clinician, become medical advice and trigger FDA SaMD jurisdiction; an LLM in financial customer service may produce outputs that constitute investment advice and trigger FINRA or FCA jurisdiction. The compliance design must constrain what outputs the deployment is willing to surface, with output validation before user presentation. Audit trail requirements for generative AI are emerging rapidly: EU AI Act Article 13 (transparency) and Article 14 (human oversight) for high-risk systems require structured logs of the model invocations, inputs, outputs, and human review events. FDA Predetermined Change Control Plans for AI-enabled medical devices require defined boundaries on what changes the model can undergo without triggering a new submission.
We build LLM / Generative AI systems for regulated industries. Compliance-native from architecture. Fixed price.
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We deploy generative AI for regulated clients with provider-side BAA confirmation as the first checkpoint, output validation as the second, and structured audit trails as the third. Provider selection: Azure OpenAI Service or AWS Bedrock with the appropriate model partner agreement for HIPAA workloads; direct Anthropic enterprise contracts for non-PHI but compliance-scoped workloads; Vertex AI for clients standardized on GCP. Provider configuration disables data retention for training purposes — every regulated deployment opts out of any training-data use of customer prompts and completions. Prompt and response logging captures structured events with sanitized inputs (PHI tokenized or redacted in the audit stream, but presence and field-class noted) and outputs categorized by safety classification. Output validation runs every model response through a structured guardrail layer before presentation: PHI leakage detection on responses generated from contexts containing PHI, refusal detection for queries the model should not have answered, and topic-out-of-bounds detection for deployments with narrow scope. Human-in-the-loop workflows are designed for regulatory compliance with named approver capture rather than implicit operator presence. For agentic deployments, the LangChain or LangGraph patterns we cover elsewhere apply; for non-agentic deployments, the structured event log is simpler but the audit-trail requirements are identical.
Compliance Enforcement at the Code Level
Generative AI governance in our regulated engagements is enforced through provider configuration, prompt and output validation, and continuous evaluation. Provider configuration is validated by ALICE — any code path that sends data to a provider without a BAA-aware wrapper fails the validation. Prompt-and-output validation runs as a separate inference pass against a guardrail model or rule-based filter; the validation produces a structured classification that joins the audit event. Continuous evaluation runs a regression suite of representative prompts (including red-team prompts known to elicit unsafe responses) on a defined cadence to detect model behavior drift. For deployments under EU AI Act high-risk classification, conformity assessment evidence is generated continuously: training data lineage (where applicable for fine-tuned models), validation statistics, human oversight event records, and incident logs. For FDA SaMD deployments, the Predetermined Change Control Plan defines which model changes are within the authorized envelope and which require new submission; deviation from the plan is detected automatically and escalated.
ALICE validates every commit against the applicable regulatory framework before it merges. Compliance violations are caught at the commit level — not in production, not in an audit finding.
In Production
A health insurance payer engaged us to build a member-services LLM deployment that would answer benefits-coverage questions in natural language while remaining within HIPAA and state insurance regulator boundaries. We selected Azure OpenAI Service under the payer's existing Azure HIPAA BAA, configured prompt-and-response audit logging with PHI redaction in the log stream, implemented output validation that detected and blocked responses straying outside the trained benefits-information scope, and designed a human-escalation path for queries the deployment classified as outside its authorized scope. The deployment serves 1.4 million members with 78% containment rate (queries answered without human escalation) and zero PHI-exposure or scope-out-of-bounds findings in the first 14 months of operation. The audit log was accepted by the state insurance regulator during a routine market conduct examination.
Ready When You Are
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We build LLM / Generative AI systems for healthcare, financial services, energy, and government. Compliance-native from architecture. Fixed-price delivery.
Related Services
HIPAA-Compliant ML & AI Implementation Guide
LLM provider BAA selection, prompt and output audit logging, guardrail validation, EU AI Act conformity evidence, and FDA Predetermined Change Control Plans for generative AI in regulated industries.