How do GDPR, HIPAA, and SOC 2 impact System Design?

What happens when compliance becomes part of System Design, rather than a checklist after deployment?

Today, system designers must operate within a regulatory environment that shapes technical decisions from the earliest stages. Frameworks like GDPRGeneral Data Protection Regulation, HIPAAHealth Insurance Portability and Accountability Act, and SOC 2System and Organization Controls 2 for managing sensitive data, ensuring privacy, and maintaining security are no longer external constraints. They guide how data is collected, processed, and stored. They influence access control models, logging strategies, and cloud infrastructure choices.

These regulations also affect how businesses operate. Platforms that embed compliance into their systems are better positioned to earn user trust, scale across regions, and pass vendor assessments. Compliance is no longer separate from design. It is shaping the structure, behavior, and resilience of systems.

This newsletter dives into how GDPR, HIPAA, and SOC 2 reshape System Design. We’ll cover their core requirements, impact on architecture, and the trade-offs system designers face to ensure compliance, reliability, and speed.

To begin, let's break down each regulation’s demands and how its focus areas differ.

Regulatory primer: What each framework requires#

Before diving into design patterns and architectural solutions, it’s essential to understand the requirements of each regulation. While GDPR, HIPAA, and SOC 2 share a focus on safeguarding data and building trust, each targets different domains, use cases, and risk models. System designers must know these distinctions early in the design life cycle, as they directly influence data handling, storage models, and access policies.

General Data Protection Regulation (GDPR)#

General Data Protection Regulation (GDPR), a privacy regulation introduced by the European Union (EU), governs how personal data is collected, stored, and processed. Its scope applies to companies based in the EU and any organization handling EU citizens’ data. This extraterritorial reach makes it a global concern for most internet-facing platforms.

At a high level, GDPR requires systems to support:

1. Purpose limitation: Use personal data only for the specific, clearly defined purposes communicated to the user during collection.

2. Lawfulness, fairness, and transparency: Ensure that data is processed legally, users are informed about how their data is used, and no hidden or unfair practices are involved.

3. Data minimization: Collect only the data needed for a specific purpose.

4. Integrity and confidentiality: Protect personal data against unauthorized access, accidental loss, or destruction through appropriate technical and organizational measures.

5. Accuracy: Keep personal data accurate and up to date. Incorrect or outdated data must be corrected or deleted without delay.

6. Accountability: Be able to demonstrate compliance with GDPR principles through policies, documentation, audits, and System Design choices.

7. Storage limitation: Keep personal data only as long as necessary for the stated purpose. Delete or anonymize data when it’s no longer needed.

The following image shows the seven rules of GDPR:

These requirements push system designers to build support for things like updating or deleting user data, tracking user consent, and storing data to make removing or exporting specific pieces easy when needed.

Health Insurance Portability and Accountability Act (HIPAA)#

HIPAA is a U.S. law that applies to health care providers, insurers, and health tech companies. Its focus is safeguarding protected health information (PHI), including any identifiable medical data.

To comply with HIPAA, systems handling PHI must be built with specific safeguards in mind. Some of the core safeguards include:

• Access and audit controls: Systems must log who accessed PHI, when, and why.

• Data integrity and breach detection: Systems must detect and respond to unauthorized access or tampering.

• Notification workflows: In case of a breach, systems must support alerting regulators and affected individuals.

Designing for HIPAA compliance often requires fine-grained access control, immutable audit logs, and architecture that separates sensitive health data from general application logic.

Systems and Organization Controls 2 (SOC 2)#

SOC 2 is not a law but a framework developed by the AICPAAmerican Institute of Certified Public Accountants. It evaluates a service provider’s internal controls on storing, processing, and protecting customer data. These controls are assessed against five trust service criteria: security, availability, processing integrity, confidentiality, and privacy.

What makes SOC 2 particularly relevant is its growing use as a baseline for trust in SaaSSoftware as a Service and cloud-native platforms. Startups and established vendors often pursue SOC 2 compliance to meet enterprise procurement standards.

Each of the five trust service criteria represents a specific area where systems must demonstrate strong and reliable controls:

• Security: Protect systems from unauthorized access through authentication, firewalls, and intrusion detection.

• Availability: Ensure systems remain operational as committed in service level agreements (SLAs)These are formal agreements between a service provider and a customer. These agreements define the expected level of service, including metrics like uptime, response time, resolution time, and performance thresholds..

• Confidentiality: Safeguard sensitive data through encryption, access restrictions, and masking.

• Processing integrity: Ensure systems operate reliably and process data as intended, without being altered by unauthorized actions or errors.

• Privacy: Ensure personal information is collected, used, retained, and disclosed in line with the organization’s privacy notice and relevant regulations.

Unlike GDPR or HIPAA, SOC 2 allows flexibility in implementation. However, it requires extensive documentation, internal controls, monitoring pipelines, and policy enforcement. It often influences System Design by encouraging modular service boundaries, secure CI/CD pipelines, and real-time observability.

The following table shows the different scopes, mandates, and industries each regulation targets.

We have learned the foundational requirements for building systems in light of GDPR, HIPAA, and SOC 2. Let’s investigate how these translate into concrete architectural decisions, considering the regulatory mandates.

4 core architectural impacts#

Understanding regulations is only the starting point. GDPR, HIPAA, and SOC 2 shape how systems manage data, enforce controls, and define operational boundaries. Their impact extends deep into architecture, affecting how data is retained, who can access it, where it resides, and how it is secured. Whether you build health care software or cloud platforms, these frameworks influence decisions across the stack. Let’s explain how they affect the following four key areas in System Design:

• Data life cycle management

• Access control and auditability

• Geographic data residency and isolation

• Encryption and security by design

Let’s start with data life cycle management

1. Data life cycle management#

Imagine data as a living entity within a system. Compliance mandates define its birth, life, and even its lawful demise. The “right to be forgotten,” a cornerstone of GDPR, demands that systems implement enforceable data retention and deletion workflows. This means architects can no longer simply store data indefinitely. They must design automated or semi-automated processes for identifying, archiving, and securely erasing personal data upon request or after predefined periods. For instance, a system might use a time-based data expiration policy or a user-driven deletion request for personal profiles.

However, not all data can be removed on demand. For instance, immutable audit logs must be preserved for audit or legal purposes. This introduces two parallel paths for managing data: mutable stores that support user-driven deletion and immutable logs that guarantee traceability. The design must keep both in sync without compromising compliance. For example, a user profile might be deleted from an active database, while a record of that deletion event remains permanently in an audit log.

Furthermore, tagging and tracing data across microservices becomes paramount in complex microservice architectures. When a user requests data deletion, a system must identify all instances of that user’s data across various services, databases, and caches. Implementing robust data lineage solutions, perhaps using unique identifiers or metadata tagging, allows for comprehensive tracking and ensures no data is left behind, satisfying regulatory obligations.

2. Access control and auditability#

Who can see what, and when? This fundamental security question is heavily influenced by compliance. Modern systems require designing for fine-grained role-based access control (RBAC) or attribute-based access control (ABAC) models. Instead of simple user roles, access might depend on specific attributes of the user, the data, or even the context of the request.

For instance, only a specifically authorized clinician accessing a secure network might be granted access to sensitive patient health information. This distinction is illustrated in the comparison between RBAC and ABAC. RBAC maps users to predefined roles like “Clinician” or “Admin,” with each role assigned fixed permissions, making managing it straightforward but less adaptable to dynamic conditions. Conversely, ABAC enables more granular and context-aware control by evaluating multiple attributes (such as user role, location, device type, data sensitivity, and access time) before granting access.

To balance simplicity and flexibility, systems often combine both models, enforced through identity-aware proxies, service meshes like Istio, or policy engines such as OPAhttps://www.openpolicyagent.org/docs.

Immutable audit logs are crucial for proving who performed what action and when. Platforms like AWS CloudTrail or dedicated Kafka log sinks provide tamper-resistant records of every system access, change, or administrative action. These logs are crucial for demonstrating compliance to auditors and for forensic analysis in case of a security incident or breach.

Beyond initial design, ongoing vigilance is key. This includes periodic access reviews, where permissions are regularly examined and adjusted. Concepts like just-in-time accessThe process where elevated permissions are granted only for the duration of a specific task. minimize exposure. Thoughtful user session design ensures that sessions are secure, time-limited, and properly terminated, reducing the window for unauthorized access.

3. Geographic data residency and isolation#

In our interconnected world, data often crosses borders, yet regulations frequently mandate its physical location. This creates a tension between multi-region data distribution for availability and strict localization requirements, particularly under GDPR. While spreading data across multiple data centers enhances resilience, GDPR may require the personal data of EU citizens to remain within the EU.

To address this, architects employ specific design patterns like region-aware data sharding. This involves partitioning databases or storage such that data belonging to users from a specific geographic region resides only in data centers within that region. Geo-tagged resource policies can enforce where compute resources process certain data types, ensuring compliance with data residency rules. For example, a serverless function processing EU customer data might only be allowed to run in an EU region.

The image illustrates a data architecture where an application routes sensitive “regulated data” to dedicated “InCountry” storage to meet legal compliance requirements, while “all other data” is directed to a general-purpose database to maintain operational flexibility.

Even content delivery networks (CDNs) are not exempt. CDN compliance may involve setting up IP filtering or making sure regional caches only store content allowed in specific regions, avoiding accidental data transfer across restricted borders. Designing for residency means striking a balance. Systems must be available worldwide but also respect location-specific restrictions on storage and processing. This often calls for localized deployments combined with clear data flow isolation.

4. Encryption and security by design#

Encryption is foundational to data protection, yet compliance pushes its implementation beyond basic measures. While Transport Layer Security (TLS)It is a cryptographic protocol that ensures privacy and data integrity between communicating applications. It’s commonly used to secure data transmitted over networks (e.g., HTTPS on the web). for data in transit and AES-256ES-256 is a highly secure symmetric encryption algorithm that uses a 256-bit key to protect data, making it virtually uncrackable by current brute-force methods and widely adopted for sensitive information. for data at rest are industry standards, regulations demand deeper system implications. This means encryption influences how data flows throughout the entire application stack.

Consider end-to-end encryption in message buses like Kafka or gRPC. Data remains encrypted as it moves between microservices, even if it traverses internal networks. This prevents sensitive information from being exposed at any point during internal data transfer.

The following illustration shows how encryption is preserved end-to-end between the sender and receiver:

Furthermore, advanced techniques like pseudonymizationPseudonymization replaces direct identifiers with artificial ones, making it difficult to identify individuals without additional information. or tokenizationTokenization replaces sensitive data with a non-sensitive data, or “token,” which can be used in internal systems while the original sensitive data is stored securely in a separate, highly controlled data vault. layers, decoupled from operational data flows, are gaining prominence. This significantly reduces the attack surface and helps satisfy data minimization principles by ensuring operational systems handle less sensitive information.

The following illustration demonstrates how personal information is transformed using a key into a pseudonymized code:

Understanding these core architectural shifts highlights a deeper need to move beyond reactive fixes and design systems where compliance is part of the foundation. This requires applying patterns that make compliance a structural aspect of System Design.

Compliance-informed System Design patterns#

The real goal of compliance is to build systems that respect privacy, ensure integrity, and minimize risk by default. This requires applying regulatory principles directly to the system’s structure. Over time, certain design patterns have emerged that align naturally with GDPR, HIPAA, and SOC 2 goals. These patterns help teams embed compliance into the fabric of their systems.

• Privacy by design at service boundaries: Considers privacyhttps://arnab-k.medium.com/privacy-first-apis-implementing-gdpr-compliant-microservices-63e6f068abea at every stage of the software development life cycle, ensuring microservices collect, process, and retain only the minimum necessary data, and expose only essential data through their APIs, promoting data minimization.

• Zero-trust architecture: The zero-trust architecturehttps://en.wikipedia.org/wiki/Zero_trust_architecture operates on continuous verification, never automatically trusting any user or device. It is implemented via identity-aware proxies and service mesh access control like Istiohttps://istio.io/latest/docs/overview/what-is-istio/ or Envoy, rigorously verifying every access request.

• Secure enclaves and data vaults: Provides robust isolation for highly sensitive information such as PHI or PIIPersonally Identifiable Information through segregated computing environmentshttps://www.linkedin.com/pulse/secure-data-enclaves-protecting-your-intellectual-popoola-miet-cc--xwqpe or specialized, encrypted storage layers, accessible only via tightly controlled APIs, minimizing data exposure.

• Data access gateways with policy as code: Queries pass through a gateway that enforces access rules defined in code. Tools like Open Policy Agent (OPA)https://www.openpolicyagent.org let teams version, test, and deploy policies alongside codehttps://raunakbalchandani.medium.com/enforcing-policy-as-code-open-policy-agent-opa-508883d6c0e8. This makes compliance enforceable and auditable within CI/CD. 

By adopting these patterns, system designers can turn compliance from a constraint into an architectural advantage. These practices improve isolation, simplify audits, and reduce unintended exposure, while also building systems that are easier to scale and secure. However, achieving compliance often comes with trade-offs that must be carefully managed.

Trade-offs in System Design#

Achieving compliance is a strategic imperative, but it rarely comes without architectural and operational considerations. Architects must navigate a landscape of choices, where meeting one regulatory demand might impact another aspect of the system. Understanding these trade-offs is essential for making informed decisions and building compliant and practical systems. Let’s discuss some of the core trade-offs as follows:

• Performance degradation due to extensive audit trails: Generating, storing, and processing comprehensive, immutable logs can introduce latency and consume significant computational resources, requiring optimized logging pipelines.

• Data portability APIs: Implementing robust APIs for users to extract their data, as GDPR mandates, demands considerable engineering effort for data aggregation, formatting, and secure transfer.

• Cost vs. compliance: Achieving strong isolation for each customer’s data in multi-tenant systems significantly increases infrastructure costs and management overhead, requiring a balance between compliance needs and financial implications.

• Balancing observability with data overcollection: While monitoring is crucial, collecting excessive personal or sensitive data for observability can inadvertently expand regulatory scope, increasing the compliance burden; careful data masking or aggregation is often needed to prevent this.

Understanding these inherent trade-offs allows architects to engage in realistic planning and decision-making, optimizing for compliance without sacrificing critical aspects of system functionality or cost effectiveness.

Let’s look at a real-world implementation where leading companies have successfully navigated compliance challenges.

Real-world implementation examples#

Seeing compliance patterns in production delivers powerful lessons. Let us review how Stripe, Epic Systems, and SOC 2-focused startups have embodied these practices in real systems:

• Stripe: It uses tokenizationhttps://stripe.com/resources/more/payment-tokenization-101 to prevent raw card data from reaching the merchant infrastructure. Card details are collected by client-side Stripe components and sent directly to Stripe’s secure vault, so applications handle only tokens. This approach dramatically reduces the Payment Card Industry Data Security Standard (PCI DSSA set of security standards designed to ensure that all entities that store, process, or transmit credit card information maintain a secure environment.) and GDPR scope while maintaining security through token rotation and vault isolation.

• Apple (Privacy by design for GDPR compliance): Apple deeply embeds privacy by designhttps://www.apple.com/legal/privacy/en-ww/governance/ into its products, minimizing data collection, processing data on the device, and providing transparent user controls. This architectural approach directly addresses GDPR’s data protection and user rights demands.

• Google Cloud (Comprehensive HIPAA-compliant infrastructure): Google Cloud offers a HIPAA-complianthttps://cloud.google.com/security/compliance/hipaa-compliance infrastructure with a Business Associate Agreement, ensuring default encryption at rest and in transit, granular Identity and Access Management (IAM), and extensive audit logging for eligible services, supporting health care applications.

• Amazon Web Services (AWS) and Microsoft Azure (SOC 2 Compliance for Cloud Platforms): AWS and Microsoft Azure maintain SOC 2 compliancehttps://aws.amazon.com/compliance/soc-faqs/ for their vast infrastructure services. Their designs incorporate a shared responsibility model, offer extensive security services like KMSKey management service and CloudTrail, provide global infrastructure with isolation for data residency, and perform continuous monitoring and auditing validated by third parties.

These examples highlight how major technology companies integrate compliance as a core part of their platform design. This approach enables their customers to build compliant applications on a secure and trusted foundation.

Developer and organization readiness#

Compliance is not a static audit but an ongoing journey integrated into daily development practices through proactive organizational effort. One powerful approach centers on policy as code, which defines compliance rules in machine-readable code using tools like Rego or Sentinel. Its integration into continuous integration pipelines provides automated checks, preventing noncompliant configurations from reaching production.

Beyond code-based policies, compliance observability proves vital for understanding compliance status in real time. Internal dashboards dedicated to audit logs, access tracing, and data flow maps achieve this, enabling proactive monitoring and rapid response.

Furthermore, shifting left integrates security and privacy early in the development life cycle by conducting threat modeling, implementing secure defaults, and incorporating Privacy Impact Assessments (PIAs) into pull request reviews. This significantly reduces costly rework.

Wrapping up#

Compliance is not a constraint that follows engineering. It is now a foundational part of how systems are designed. Regulations such as GDPR, HIPAA, and SOC 2 shape how data moves, how services interact, and how users trust what is built. They bring clarity to access patterns, force better defaults, and elevate the importance of transparency.

Systems that account for these expectations early are easier to secure, scale, and govern. Compliance encourages modular design, observability, and strong isolation boundaries — all of which improve system health beyond regulatory needs.

As technology becomes more embedded in critical decisions and everyday lives, trust is no longer a side effect of good systems. Ask yourself this: If trust is now something we must design for, are your systems ready to carry that responsibility?