AI Governance & Assurance — Evaluation, Risk, and Compliance on AWS

Most organizations know generative AI belongs in their roadmap but cannot prove their deployments are safe, compliant, and defensible — to regulators, auditors, or their customers' security teams. This offering closes that gap with governance programs that produce evidence, not just documentation.

JLB Data Science builds AI governance and assurance programs on AWS, grounded in NIST AI RMF, ISO 42001, and where applicable the EU AI Act, GDPR, and HIPAA. Unlike advisors who arrive from law or policy, our background is engineering: 10+ years building production AI/ML systems. We design the controls and build the evaluation infrastructure that makes the policy provable.

How this relates to AWS services

Engagements are delivered AWS-native. Controls and evidence map directly to Amazon Bedrock Guardrails (content filtering, denied topics, PII handling, and contextual grounding checks), Amazon Bedrock model evaluation, Amazon SageMaker Clarify (bias and explainability), AWS CloudTrail (audit logging), AWS Audit Manager (evidence collection), and AWS IAM (least-privilege access for AI workloads). Compliance evidence comes from the platform your systems already run on.

What we deliver

• AI Governance Readiness Assessment: a 2-3 week gap analysis scored against NIST AI RMF and ISO 42001, covering use-case intake, risk classification, lifecycle controls, and your AWS control surface, with a prioritized remediation roadmap.
• Evaluation and red-teaming: LLM-as-judge evaluation calibrated to your quality taxonomy, faithfulness and hallucination scoring, bias and fairness testing, adversarial red-teaming, and regression suites that run when your corpus or models change.
• Governance program build: review board design, use-case intake and risk tiering, lifecycle controls, bias analytics, and an evidence trail your team can operate without us.
• Deal-ready compliance: a fixed-scope program to pass a named enterprise vendor-risk review, reach ISO 42001 readiness, or clear a HIPAA assessment blocking a deal.
• Fractional AI governance: an ongoing named owner for AI risk — running reviews, maintaining the risk register, and keeping evidence audit-ready.
• Training and workshops: responsible AI fundamentals through hands-on framework implementation, for technical and executive audiences.

Who this is for

Enterprises deploying generative AI on AWS in regulated or trust-sensitive environments — financial services, insurance, and healthcare in particular — that need to move from "we think it works" to "we can prove it" before a regulator, auditor, or enterprise customer asks.

All engagements are fixed-scope and priced per engagement through private offers. Contact us for scoping; we respond within one business day.