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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.

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.”

When it comes to life sciences cybersecurity, security and compliance are more than IT checkboxes—they’re the foundation of trust, patient safety, and scientific innovation. At the February 27, 2025 panel hosted by PTP, Hector Rodriguez, Principal Industry Specialist at AWS, delivered an eye-opening session on how biotech and health organizations can strengthen their security posture while accelerating innovation.

With 30+ years in health and life sciences, Rodriguez shared valuable insights on scalable security frameworks, the role of automation and AI, and why cloud-native approaches—especially AWS—enable life sciences companies to meet compliance requirements without slowing down scientific progress.

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The Risk of “Adding on” Security

Rodriguez emphasized a shift in mindset: security must be embedded from day one. Too often, organizations treat it as an afterthought, leading to gaps in data integrity and regulatory exposure. In modern cloud-native environments, security should be integrated into the entire software development lifecycle—from architecture to automation.

He highlighted tools like Amazon Q Developer, which helps flag vulnerabilities in real time during code development, and stressed the dangers of hardcoding credentials or neglecting security in CI/CD pipelines.

Let AWS Do the Heavy Lifting

Rodriguez urged attendees to take advantage of what cloud providers do best—manage compliance at scale. With support for over 140 global compliance frameworks out of the box, AWS allows customers to “inherit” baseline security measures like HIPAA, GxP, and ISO standards. This helps reduce the need for expensive security consultants while empowering life sciences compliance teams to stay focused on innovation.

Serverless and Auto-Scaling for Security at Scale

One of the most impactful takeaways was the power of serverless architectures. Rodriguez explained that serverless computing allows AWS environments to scale automatically—without compromising security. Tools like Secrets Manager, Key Management Service, and Auto Scaling help protect sensitive information and increase resilience with minimal manual overhead.

The New Security Chain of Custody

Rodriguez also called attention to the importance of third-party supply chain security. Breaches in unknown software vendors have disrupted major clinical systems, underscoring the need for visibility across the full environment.

AWS Config, Certificate Manager, and Security Hub enable continuous compliance monitoring, drift detection, and encryption verification—making them essential components of a secure, cloud-native infrastructure, and a critical part of PTP’s Security Monitoring approach.

Resilience > Ransomware

Rodriguez stressed that the most dangerous cybersecurity threat today isn’t ransomware—it’s a lack of resilience. For biotech and clinical research organizations, downtime can delay clinical trials and ultimately harm patients.

Through services like AWS Resilience Hub and the AWS Well-Architected Framework, life sciences companies can establish recovery plans, audit environments, and minimize business disruptions.

“The question is no longer if a breach or failure will happen—it’s how quickly your systems can recover when it does.” – Hector Rodriguez

AI, Automation, and the Road Ahead

Rodriguez concluded with a look at the future: AI and automation are critical for scalable security operations. AWS uses machine learning to detect anomalies and automate remediation—reducing human error while increasing visibility and control.

For companies balancing security and compliance with innovation, the message was clear: embed security early, lean on cloud-native solutions, and let AWS do the heavy lifting so your team can focus on R&D, clinical trials, and commercialization.

🔗 Learn more: Full Panel Recap: Privacy, Cybersecurity & Compliance in Life Sciences

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.

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💡 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.

Catch the Highlights from our recent Lunch & Learn session featuring Jon Myer and Hong Pun from PTP! In this insightful discussion, they dive deep into the AWS Well-Architected Framework Review , highlighting its critical role in optimizing cloud environments, enhancing security, reducing costs, and aligning with AWS best practices. This review is particularly valuable for organizations seeking cloud cost optimization and stronger architectural governance through certified AWS MSP support.

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What’s covered in the video:

• The Six Pillars of AWS Well-Architected Framework: Operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability—how these pillars form the foundation of a well-optimized AWS environment.
• The Value of Regular Reviews: Why conducting a Well-Architected Review isn’t a one-time task but a continuous improvement process that keeps your AWS environment secure, efficient, and cost-effective.
• Tools and Strategies: Hong walks through the specific tools and techniques used to evaluate cloud setups, uncovering performance bottlenecks, security vulnerabilities, and areas for cost optimization.
• Collaboration with AWS-Certified Partners: How working with experienced AWS partners like PTP ensures you’re following best practices while improving operational efficiency and security, especially in regulated industries like managed IT services for life sciences.
• Managing Complex Workloads: Learn tips for handling multi-account, multi-region setups and optimizing workloads across diverse environments.

Key Takeaways

• Understand the six pillars of AWS best practices
• The process and benefits of conducting regular Well-Architected Reviews
• How to identify gaps in your AWS setup, optimize costs, and strengthen security
• The importance of working with certified AWS partners like PTP for ongoing success