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Key Topics Covered:

📌 Data Privacy Isn’t Static

Startups must understand that data privacy obligations shift as their business grows and data types evolve. Whether handling personal health information (PHI), sensitive personal data, or data sourced internationally, companies must align with state-specific privacy laws and prepare for broader federal regulations that may soon emerge.

🔒 Cybersecurity Requires Proactive Planning

Jason emphasized the need for robust cybersecurity controls early on—even for pre-revenue startups. He discussed how vendor selection, staff training, and best practices (such as those from AWS) play a crucial role in protecting data, ensuring audit-readiness, and safeguarding IP.

🤖 AI Risks Extend Beyond Technology

Generative AI and ML models bring efficiency—but also introduce risk. Improper use of public LLMs like ChatGPT can accidentally disclose confidential information, jeopardize IP, or even violate partner contracts. Jason advised establishing internal policies that govern employee use of AI tools to protect data and maintain compliance.

🌐 International Data Considerations

With many startups sourcing datasets globally, Jason warned of cross-border regulatory issues, including EU GDPR, China’s data protection laws, and future U.S. tariff policies. Businesses must weigh the legal and logistical implications of using non-U.S. data sources.

🧠 Training Is Non-Negotiable

Finally, Jason highlighted the importance of regular staff training. Many data breaches are due to human error—not technical failure. Educating teams on privacy, security, and responsible AI use is essential to reducing risk and maintaining trust.

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.

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🔍 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.

At the heart of the life sciences industry lies sensitive data—whether it's personal identifiable information (PII), genomic sequences, or proprietary research. Ensuring that this data is handled securely and remains compliant with regulatory frameworks isn’t just a matter of best practice—it’s essential for innovation and trust.

At the recent Charting a Path for Compliance in Life Sciences panel hosted by PTP, Aaron Jeskey, Principal Cloud Architect at PTP, shared expert insights on how early compliance strategies, secure cloud infrastructure, and well-structured security frameworks can mitigate risk and support biotech growth from startup to scale.

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Why Compliance Starts at the Seed Stage

Jeskey emphasized that successful compliance journeys begin not during audits but at the very start of company formation. From day one, organizations should be thinking about IQ (Installation Qualification), OQ (Operational Qualification), and PQ (Performance Qualification)—core pillars of FDA GxP frameworks.

• IQ: Ensures infrastructure is installed and documented correctly—especially critical for security controls like encryption and secure backups.
• OQ: Validates how the system operates in production, mapping out how instruments process data.
• PQ: Measures outcomes against documented processes—essential for audit readiness and demonstrating control.

Building Secure Foundations with AWS

Jeskey demonstrated how AWS services like GuardDuty, IAM, and AWS Config can play a central role in automating and maintaining security best practices across cloud infrastructure.

• Encryption of data at rest and in transit
• Access control through Identity and Access Management (IAM)
• Continuous compliance with AWS Config and CloudTrail

By building infrastructure as code and implementing scalable monitoring, biotech teams can reduce human error, minimize security drift, and strengthen resiliency.

Real-World Lessons: The High Cost of Delay

Jeskey shared a cautionary story about a diagnostics company that failed to prioritize compliance. A single sticky note with a password uncovered deeper issues: no encryption, shared credentials, and no chain of custody. The fallout included over $12 million in recovery costs, six months of disruption, and loss of trust.

Choosing the Right Path: Early Adoption vs. Refactoring

Jeskey outlined three common trajectories:

1. Early adoption: The most scalable, cost-effective option. Build compliance from the start.
2. Adoption during a new project: Practical for startups pivoting into regulated markets.
3. Refactoring: The most expensive path—requiring rework, third-party audits, and major delays.

Compliance as Competitive Advantage

For biotech companies building diagnostics, therapies, and AI models, compliance isn’t a checkbox—it’s a differentiator. Jeskey urged startups to use compliance maturity to attract investors, accelerate partnerships, and build long-term market trust.

As part of the Charting a Path for Compliance in Life Sciences panel hosted by PTP on February 27, 2025, Han Chiu, Chief Technology Officer at Click Therapeutics, delivered a compelling presentation on how cloud-first infrastructure and software-as-a-medical-device (SaMD) models are transforming patient outcomes while aligning with life sciences compliance standards.

The Rise of Prescription Digital Therapeutics

Click Therapeutics stands at the forefront of digital therapeutics innovation, developing FDA-regulated, AI-powered software treatments for mental health and neurological conditions. These software-based therapies are prescribed by physicians, clinically validated through randomized trials, and built with privacy, scalability, and security—a core requirement in managed IT services for life sciences.

With over 23 years in tech and four years at Click, Han Chiu oversees everything from product design to machine learning and cloud infrastructure. In his presentation, he walked through Click’s journey—from launching migraine and major depressive disorder treatments to managing high-stakes clinical trials that demand compliance with HIPAA, GDPR, ISO 27001, and SaMD requirements.

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Security and Compliance: A Startup Imperative

Despite being a fast-growing startup, Click runs over 60 isolated Kubernetes clusters to manage its concurrent clinical trials, real-world evidence programs, and commercial development. To maintain data privacy and meet evolving regulatory frameworks, Chiu outlined how their team leverages secure IT services for biotech with best practices tailored to life sciences IT support environments:

• AWS-native security tools such as AWS Certificate Manager, AWS Config, GuardDuty, and CloudTrail
• Serverless and managed services like EKS on Fargate, S3, DynamoDB, and RDS
• Zero-trust identity architecture with Okta to enforce access control from company-managed devices only
• End-to-end encryption for data at rest and in transit across all services
• Private certificates to secure internal cluster communications
• Infrastructure as Code (IaC) for repeatability, traceability, and scalability

Infrastructure as Code: Enabling Repeatability and Trust

For Chiu and his team, infrastructure as code isn’t optional—it’s foundational. Click defines its entire infrastructure in software, ensuring no changes are deployed without code review, unit testing, and continuous delivery pipelines. This approach supports biotech IT support needs while reducing risk, streamlining audits, and enabling rapid innovation.

Key Takeaways for Life Sciences Startups

Chiu closed with a reality check for startups: business complexity increases dramatically in life sciences due to high compliance stakes. Rather than manually building secure systems from scratch, he encouraged organizations to offload as much as possible to trusted cloud providers like AWS. By embracing serverless services, zero-trust access, and automation through IaC, companies can adopt IT managed services for biotech that scale without compromising compliance or patient safety.

“The only way to operate efficiently at scale is infrastructure as code,” Chiu emphasized. “It’s how our small team supports over 60 secure Kubernetes clusters while focusing on innovation.”

In this PTP LinkedIn Live Lunch and Learn, host Jon Myer welcomes Scott Scheirey, PTP’s Director of Cloud Architecture, to break down the complexities of data orchestration in life sciences. This 15-minute session delivers fast, actionable insights into how life sciences organizations can eliminate data silos, streamline workflows, and accelerate AI and machine learning initiatives using AWS and managed IT services for life sciences.

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The Complexity of Life Sciences Data

Life sciences organizations handle a range of sensitive and structured data—from genomics and proteomics to imaging and real-time lab instrumentation. As Scott Scheirey explains, orchestration isn't just about data transfer; it’s about routing, transforming, and structuring that data to be usable for research, insights, and decision-making. Compared to other industries, biotech IT support must be tailored to high-throughput environments and evolving research methods, often delivered through managed IT for labs.

Breaking Down Silos to Unlock Latent Data

Data silos are common in research labs, where genomics, imaging, and proteomics operate independently. PTP works with teams to centralize and standardize access using tools like Amazon S3, enabling collaborative insight generation. Cross-domain synergy is essential for uncovering latent relationships and driving real-time discovery in clinical research and beyond. This is where IT services for biotech labs and IT infrastructure for life sciences companies come into play.

How AWS Supports Scalable Data Orchestration

Scott outlines how AWS services—including AWS Batch, AWS HealthOmics, and AWS Step Functions—support high-performance orchestration pipelines. These tools empower research teams to scale workflows, reduce latency, and meet compliance requirements like HIPAA and GxP across geographies, supported by HIPAA-compliant MSP for biotech and IT services for CROs.

From Startups to Scale: Tailoring the Strategy

For biotech startups, the orchestration challenge often begins with centralizing fragmented data and building minimal pipelines. More mature organizations, on the other hand, face challenges related to optimization, automation, and cost-efficiency. PTP tailors its approach to help companies at any stage unlock the value of their data with IT consulting for biotech startups and MSP services for lab compliance.

Laying the Groundwork for AI and ML

Data orchestration is foundational to AI and machine learning success. Without structured, formatted data, training and inference become ineffective. PTP ensures clients have the infrastructure and workflow automation in place to power these models efficiently, supporting life sciences IT support for everything from clinical trials to drug discovery with research data IT services and scientific computing IT support.

Steps to Orchestration Success

Key Orchestration Steps

1. Centralize data using Amazon S3 or other cloud-native storage platforms.
2. Define and deploy pipelines aligned with key research use cases.
3. Leverage orchestration tools like Nextflow and AWS Step Functions for automation.
4. Continuously evaluate and refine pipelines to enhance performance and reduce cost.

This approach supports sustainable growth through managed cloud services for life sciences and scalable IT managed service provider for biotech companies.

In this insightful PTP Lunch & Learn session, hosted by Jon Myer, special guest Micah from PTP takes us on a deep dive into the fundamentals of leveraging AI in the life sciences sector. From essential data management strategies to the critical importance of computing power, Micah breaks down the components necessary for implementing AI effectively using AWS tools such as SageMaker, Bedrock, and S3, backed by managed IT services for life sciences organizations.

Let’s explore the key takeaways and break down what you need to know to kickstart your AI journey using IT services for life sciences companies focused on innovation and compliance.

Watch the full video on YouTube

💡 Key Discussion Points

1. The Four Building Blocks of AI: Intent, Data, Compute, and Human Expertise
2. AWS Tools in Action: Leveraging SageMaker, Bedrock, and S3 for AI and Machine Learning
3. Data Management: Why it’s crucial to get your data strategy right from the start
4. Computing Resources: The often overlooked but vital role of computing efficiency in scaling AI models
5. Life Sciences & AI: Key considerations for companies looking to implement AI solutions

Understanding the Building Blocks of AI

Micah introduces the four critical building blocks needed to implement AI solutions: data, models, compute resources, and human expertise. Each plays an indispensable role in ensuring the success of AI applications. But one key element often overlooked, Micah emphasizes, is intent—knowing why you want to use AI before jumping in is essential. Whether it's improving research workflows, supporting clinical trials, or driving biotech innovation, having a clear strategy matters. PTP helps align these goals through managed IT for research labs and AI consulting tailored to life sciences.

“AI is just another tool in the toolbox,” Micah explains, “but knowing what you want to achieve with it is critical.”

Defining Your AI Intent: Where Do You Start?

Before diving into data and technology, it's essential to define the intent behind your AI initiative. Do you want to enhance productivity, improve research outcomes, or extract deep clinical insights? Clarifying this from the start ensures that your strategy aligns with your business goals and meets the unique needs of regulated environments like biotech and pharmaceutical research.

Micah advises that intent should be the first building block: "Without a clear target, you're just throwing tools at the wall to see what sticks."

AWS Tools for AI: SageMaker, Bedrock, and S3

When it comes to applying AI in life sciences, AWS offers an arsenal of tools to help companies navigate the complexities of data and machine learning. These platforms are essential for model development, data processing, and automation. PTP integrates these tools as part of our IT services for biotech labs and our role as an AWS MSP for life sciences.

Micah points out that managing data properly is crucial, and AWS tools offer scalable ways to store, control, and transform data. S3 is foundational for data storage, while services like Glue help shape datasets for machine learning. With support from a HIPAA-compliant MSP for biotech, this infrastructure remains secure and audit-ready.

AWS’s computing power—via EC2, ECS, and Kubernetes—gives life sciences companies the processing scale needed to run complex AI models. These services are core to our managed cloud services for life sciences clients who rely on performance, uptime, and compliance.

Data Management: The Foundation of AI Success

No AI model is effective without quality data. In life sciences, the stakes are even higher given regulatory pressure, data privacy, and the complexity of clinical research. PTP delivers IT support for clinical research and biotech companies to ensure that data is accurate, secure, and AI-ready.

Micah emphasizes that human oversight is essential when training AI models. Our team supports clients through research IT support services that guide data management strategy and optimize long-term AI outcomes.

The Role of Compute Resources in AI

A commonly overlooked aspect of AI implementation is computing infrastructure. PTP works with life sciences companies to configure scalable, compliant systems through managed IT services for labs. From tuning workloads to minimizing latency, we ensure resources are aligned with AI demands.

Whether scaling a Kubernetes cluster or deploying thousands of cores via EC2, planning and cost control are key. Our team helps monitor usage and performance, ensuring sustainable operations with optimized costs.

Don’t Forget the Costs: AI and Compute Resources

Scaling AI infrastructure is powerful—but expensive if unmanaged. Micah highlights the need for visibility and planning. Using services like AWS Spot Instances and forecasting compute needs can drastically reduce operational costs. Our role as a managed IT provider for biotech companies includes identifying cost-saving strategies without compromising performance.

Key Considerations for Life Sciences Companies

For life sciences companies considering AI, Micah offers these key recommendations:

1. Start with intent: Define what success looks like from the start
2. Build a scalable data strategy: Centralize and clean your data early
3. Deploy IT infrastructure aligned with research goals
4. Control costs: Monitor usage and optimize AWS spend

Wrapping It Up

Micah closes the session with a reminder that success with AI requires both technology and strategy. Whether you're a startup building pipelines or a large research organization optimizing scale, having the right support in place is critical. PTP provides outsourced IT services for life sciences companies, helping teams use AI responsibly, securely, and effectively.

To learn more about how PTP supports biotech IT services, scientific computing IT support, and AWS MSP solutions for research, visit ptp.cloud.