AWS AI Governance Consulting for Financial Services and Healthcare

Kriv AI runs this practice across two verticals that ask the same underlying question in different regulatory language: financial services (banks, insurers, lenders) and healthcare and life sciences (payers, providers, pharma). Both are asking the same thing: which AI systems are running in the AWS environment, who owns each one, what happens when a model or prompt template changes, and what evidence exists if a regulator or auditor asks.

AWS AI governance consulting is not the same discipline as generic AI governance advisory. The controls live inside the AWS account itself: Bedrock model access policies, SageMaker model registry entries, IAM roles that scope which teams can invoke which foundation models, and CloudTrail logs that prove it happened. A governance program that ignores the AWS control plane and only produces a policy document will not hold up in an examination or an internal audit walkthrough.

Most AML/KYC AI work on AWS combines a few recurring patterns: a graph database such as Amazon Neptune for entity resolution across counterparties, a SageMaker or Bedrock-hosted model for alert scoring and adverse media summarization, and a service like Amazon Comprehend or a fine-tuned model for extracting structured fields out of unstructured KYC documents. None of this replaces the underlying Bank Secrecy Act (BSA) and AML program; it changes how alerts get generated and triaged.

The governance question specific to AML/KYC is documentation of the scoring logic and how it drifts over time. FinCEN guidance and BSA examiners do not mandate a specific AI architecture, but they do expect a firm to be able to explain why a transaction was or was not escalated, and an AI-assisted alert-scoring model needs the same kind of change log and validation record a rules-based system already carries under most BSA/AML programs.

Kriv AI's AML/KYC accelerator on AWS packages the entity resolution, screening, and alert-triage pattern as a starting architecture. The governance engagement on top of it produces the model inventory, guardrail configuration, and evidence trail the deployment needs before it goes live in production, not retrofitted after the fact.

AWS publishes a list of services covered under its Business Associate Addendum, and both Bedrock and SageMaker are on it, but coverage under the BAA is not the same as a validated deployment. A governance engagement configures the data perimeter (VPC endpoints, KMS encryption, and scanning for PHI showing up in unexpected places) and then builds the validation and change-control documentation a covered entity or business associate needs on top of that.

Where an AWS-hosted AI system touches an FDA-regulated process, such as clinical trial data management or a manufacturing quality decision, 21 CFR Part 11's electronic records and electronic signature requirements and the GAMP 5 validation lifecycle apply regardless of which cloud platform hosts the system. The AWS-specific work is mapping those requirements onto SageMaker pipelines, model registry approval gates, and CloudTrail evidence instead of a traditional on-premises validated system.

This engagement runs alongside our broader healthcare and life sciences practice; the AWS governance work is the infrastructure-level layer that sits underneath the clinical and operational AI use cases that practice covers.

These are floors, not quotes. A scoping call covers what is actually running in the AWS account today, how many models, which services, and whether any guardrails already exist, before a fixed-scope number gets set.