April 3, 2025 | Bio-IT World Conference & Expo | Boston, MA
Biotech organizations are accelerating adoption of AI and machine learning (ML) to drive breakthroughs—but that brings critical challenges in data integrity, compliance, and infrastructure. At Bio-IT World 2025, Aaron Jeskey, Principal Cloud Architect at PTP, presented a session on building secure, AI-ready cloud pipelines using AWS—tailored for life sciences teams operating in regulated environments.
As an AWS Life Sciences Competency Partner, PTP supports secure AWS environments built to meet 21 CFR Part 11, HIPAA, and NIST 800-53—ensuring traceability, reproducibility, and security across machine learning pipelines in biotech.
🔍 The Sticky Note Crisis: A Real-World Wake-Up Call
Aaron opened with a real-world GxP compliance failure: during an FDA audit, a life sciences client was flagged for a sticky note with shared login credentials left on lab hardware—undermining system trust and traceability.
PTP was brought in post-crisis to rebuild the client’s AWS cloud architecture—enabling secure collaboration and audit-ready ML workflows in compliance with GxP standards.
🧰 AWS Tools for Secure, Compliant ML Pipelines
To ensure infrastructure readiness, model traceability, and continuous compliance, Aaron highlighted a collection of AWS services that streamline AI/ML in healthcare and biotech research.
✅ AWS Config & Conformance Packs
- Includes pre-built rule sets for:
- 21 CFR Part 11
- HIPAA Security Rule
- NIST 800-53 Rev. 5
- Annex 11
- Monitors and enforces compliance across AWS accounts
✅ AWS Landing Zone Accelerator
- Automates deployment of secure multi-account AWS environments
- Enables logical separation of dev, test, and clinical workloads
- Ideal for organizations managing GxP-regulated ML training environments
✅ Amazon SageMaker Model Registry
- Maintains ML model lineage, metadata, and versions
- Links model objects to datasets and parameters
- Supports audit-ready AI environments with full version control
✅ AWS Artifact
- Centralizes compliance reports and audit documentation
- Reduces burden of GxP submissions and third-party validation
✅ AWS Security Hub
- Aggregates findings from AWS security tools (e.g., GuardDuty, Inspector)
- Provides a unified dashboard for monitoring risk posture
⚙️ Practical Outcomes: From Chaos to Confidence
After implementation, the biotech client:
- Passed a GxP re-audit confidently
- Established secure access control and model versioning
- Reduced audit prep time through centralized event logging
- Enabled consistent collaboration between IT and research teams with no manual policy enforcement
💬 Final Takeaway
Building AI-safe infrastructure for life sciences goes far beyond model tuning. It requires proactive compliance engineering, with tools and controls baked into every layer of your cloud stack.
If your organization is planning or scaling machine learning in biotech, ensure your foundation meets both scientific and regulatory demands from day one.
🔎 Transcript Highlights
0:00 – Aaron introduces himself, PTP’s role in biotech cloud security, and their AWS Life Sciences Competency status.
1:40 – Shares a story of a failed GxP audit where a sticky note with a shared password on lab equipment triggered an incident.
3:15 – Overview of PTP’s remediation: building a secure AWS Landing Zone and aligning workloads with GxP zones.
4:45 – Deep dive into AWS Config and conformance packs to enforce compliance frameworks like 21 CFR Part 11 and HIPAA.
6:02 – Discusses use of SageMaker Model Registry to track model lineage, parameters, and metadata for audit visibility.
7:20 – Highlights the role of AWS Artifact in surfacing documentation for internal reviews and regulatory inspectors.
8:31 – AWS Security Hub discussed as a central pane for risk visibility, configuration drift, and control enforcement.
9:40 – Summary of tools used and the outcome: a successful follow-up GxP audit and fully compliant ML pipeline.
10:45 – Final advice: build compliance into infrastructure—not as an afterthought—when scaling AI in regulated environments.