DPIA for Biotech Research: A Worked Example Template

Conducting a Data Protection Impact Assessment (DPIA) for biotechnology research involving health and genetic data is not merely a compliance exercise; it is a fundamental component of scientific integrity and ethical governance. Under the General Data Protection Regulation (GDPR), such processing is considered “high-risk,” mandating a DPIA before the processing begins (Article 35). This requirement stems from the nature of the data—special category data under Article 9—which requires heightened protection due to its potential to reveal intimate details about an individual’s health, ancestry, and predispositions. For European research institutions, biotech startups, and hospitals, the DPIA serves as a bridge between legal obligations and scientific feasibility. It forces a structured dialogue between data protection officers (DPOs), principal investigators, IT security teams, and ethics committees. This article provides a detailed, practical framework for executing a DPIA in this context, drawing on guidance from the European Data Protection Board (EDPB) and practices from supervisory authorities such as the CNIL (France) and the BfDI (Germany).

Establishing the Context: The Research Ecosystem

Before diving into the assessment itself, one must map the data flow. Biotech research rarely involves a single dataset. It often encompasses genomic sequencing, electronic health records (EHRs), lifestyle data from wearables, and biometric identifiers. The legal basis is usually a complex mix. While Article 9(2)(j) (scientific research) provides the exemption for processing special categories of data, it must be coupled with a lawful basis under Article 6. Furthermore, the “public interest” condition is frequently invoked, particularly in publicly funded research or clinical trials.

It is crucial to distinguish between the research scope and the processing scope. A common pitfall is defining the DPIA scope too narrowly, focusing only on the immediate clinical trial data while ignoring downstream bio-banking or potential secondary uses. Regulators emphasize that a DPIA must cover the entire lifecycle of the data, including retention periods and eventual destruction. In a cross-border context, such as a pan-European clinical trial involving the transfer of genetic data to a non-EU cloud provider, the assessment must also integrate Transfer Impact Assessments (TIAs), ensuring that the level of protection guaranteed by the GDPR is not undermined.

Identifying Stakeholders and Roles

In a typical biotech setting, roles are often shared. The “Controller” is usually the research institution or the sponsor of the trial. The “Processor” might be a laboratory performing sequencing, a CRO (Contract Research Organization) managing the trial data, or a SaaS provider hosting the biobank. The DPIA must clearly delineate these responsibilities. If a university collaborates with a private AI company to analyze genetic markers for disease prediction, the legal arrangement must be codified in a Data Processing Agreement (DPA) that strictly limits the processor’s ability to use the data for its own purposes (e.g., improving its proprietary algorithms).

Step-by-Step DPIA Workflow for Biotech

The EDPB recommends a seven-step process for DPIAs. We will adapt this specifically for a hypothetical project: Project Gen-Next, a multi-center study aiming to correlate genetic markers with rare autoimmune diseases using patient registries and saliva samples.

Step 1: Systematic Description of Processing

We must describe the “nature, scope, context, and purposes” of the processing.

The Purpose Specification

Researchers often struggle with the tension between scientific openness and data minimization. The purpose must be defined with precision. “Scientific research” is a broad term, but the specific objective—identifying biomarkers for Sjögren’s syndrome—anchors the processing. This specificity prevents “function creep,” where data collected for one study is silently repurposed for another unrelated study without renewed consent or legal basis.

The Data Categories

The template must list data types explicitly:

• Special Category Data: Whole genome sequencing data, blood type, medical history.
• General Personal Data: Age, gender, postal code (to assess environmental factors), unique patient identifiers.
• Pseudonymized Data: A key held by the hospital linking the genetic ID to the patient’s name, kept separate from the research database.

Step 2: Necessity and Proportionality

This is the legal heart of the DPIA. The processing must be strictly necessary to achieve the research goal. If a less invasive method exists, or if anonymized data would suffice, the processing of personal data is disproportionate.

Regulator Interpretation: The French CNIL considers that if the research can be conducted using fully anonymized data (where the key is destroyed), then processing personal data is not “necessary.” However, in biotech, re-identification is often possible if the genetic data is unique. Therefore, pseudonymization is the standard, and the necessity of retaining the link to the identity (for longitudinal follow-up) must be justified.

In our example, collecting the full genome is necessary because the hypothesis involves a Genome-Wide Association Study (GWAS). However, collecting the patient’s full name and date of birth in the research database is not necessary; these should be pseudonymized immediately at the point of collection.

Step 3: Risk Assessment (The Matrix)

Here, we identify risks to the rights and freedoms of data subjects. In biotech, the risks are rarely just financial; they are often physical, psychological, and social. We assess Likelihood (how probable is the risk event?) and Severity (how bad is the impact?).

Typical Risks in Genetic Research

1. Re-identification (The “Mosaic Effect”)
Even if data is pseudonymized, combining a genetic dataset with a public database (e.g., a public genealogy database or a voter list) can re-identify individuals. This is a high-severity risk, particularly in rare disease research where the patient pool is small and the genetic signature is unique.

2. Secondary Use and Profiling
There is a risk that the data could be used to train AI models for purposes other than the original research, such as developing insurance risk models or employment screening tools. This is a fundamental rights violation.

3. Genetic Discrimination
If the data is breached, subjects could face discrimination from insurers or employers based on predispositions to diseases. While the GDPR does not cover post-mortem data directly, the processing of data from living relatives (family studies) carries implications for the wider family tree.

4. Security Breach (Confidentiality)
A ransomware attack on the biobank. The impact is severe because genetic data cannot be “changed” like a password. Once leaked, it is compromised forever.

Step 4: Mitigation Measures

For every risk identified, we must propose a control. This transforms the DPIA from a theoretical document into an operational security plan.

Technical Measures

• Encryption: Data at rest (AES-256) and in transit (TLS 1.3). For genetic data, this is non-negotiable.
• Pseudonymization: Using a one-way hash or a secure tokenization service. The “key” should be held by a different entity (e.g., the hospital’s data manager) than the researcher.
• Privacy-Enhancing Technologies (PETs): For collaborative research, consider Federated Learning. Instead of pooling data into a central server (high risk), the algorithm travels to the data, learns from it, and brings back only the insights (mathematical models). This minimizes data movement.
• Access Controls: Role-based access (RBAC) and Multi-Factor Authentication (MFA). Logs of who accessed which genetic record must be immutable.

Organizational Measures

• Data Minimization Protocols: Strict Standard Operating Procedures (SOPs) ensuring that researchers only download the minimum dataset required for their specific analysis.
• Training: Specific training for lab technicians and researchers on the sensitivity of genetic data.
• Retention Policy: A hard-coded deletion date. For example, “Data will be deleted 10 years after the publication of the final study results, unless the subject has explicitly consented to longer storage in a biobank.”

Legal and Governance Measures

• Consent Management: Implementing granular consent. Can the subject opt out of future re-contact? Can their data be shared with other researchers? This must be tracked in a consent registry.
• Vendor Audits: If using a cloud provider, ensuring they are ISO 27001 certified and that their subprocessors are identified.
• Insurance: Ensuring professional indemnity insurance covers data breach liabilities.

Step 5: The Consultation Process

Under Article 35(3), if a DPIA indicates a high risk that cannot be mitigated by reasonable measures, the controller must consult the supervisory authority (SA) prior to processing. Even if the risk is deemed mitigated, consulting the DPO is mandatory. In many European countries, the DPO has a veto power.

Furthermore, if the research involves children or vulnerable subjects, or if it involves large-scale processing of genetic data, the SA consultation is often required by default. In Germany, for instance, the state data protection authorities often require explicit approval for biobank projects.

Step 6: Integrating the DPIA into the Project Plan

The DPIA is a living document. It should not be filed away in a drawer. It must be integrated into the project management lifecycle (e.g., Agile or Waterfall).

The “Privacy by Design” Approach:
If the DPIA reveals that the planned database schema does not support pseudonymization, the project timeline must be adjusted to fix this before coding begins. In biotech, this is often managed through the Ethics Committee submission. The Ethics Committee (in the EU, governed by the Clinical Trials Regulation) and the DPO should review the protocol in parallel.

Step 7: Review and Monitoring

Biotech projects are long. A study running for 5 years will see staff turnover, software updates, and changes in threat landscapes. The DPIA must be reviewed annually. Triggers for a new DPIA include:

• Changing the purpose of the processing (e.g., deciding to commercialize the data).
• Changing the technical architecture (e.g., moving from on-premise to cloud).
• A security incident that reveals a gap in the original assessment.

Practical Template Structure

Below is a structural template that can be adapted for internal use. It follows the logic of the EDPB guidelines but is formatted for a research administration context.

Section A: Project Overview

• Project Name: [Name]
• Project Lead: [Name/Role]
• Date of Assessment: [Date]
• Version: [Version Number]
• Summary of Research Objective: [Brief description]

Section B: Data Flow Mapping

• Data Sources: (e.g., Hospital EHR, Patient recruitment portal, Saliva samples)
• Processing Activities: (e.g., DNA extraction, sequencing, statistical analysis, storage)
• Data Destinations: (e.g., Secure Research Environment, External Lab, Publication)
• Third Parties Involved: (List all vendors, cloud providers, CROs)
• Data Retention Period: (Specific duration or trigger for deletion)

Section C: Legal Basis and Necessity

• Article 6 Basis: (e.g., Public Task, Legitimate Interest, Consent)
• Article 9 Basis: (e.g., Scientific Research – 9(2)(j))
• Necessity Justification: Why is personal data (vs. anonymous data) required? (e.g., “Longitudinal follow-up requires re-contacting patients.”)
• Consent Mechanism: Is it explicit? Is it written? Is it easy to withdraw?

Section D: Risk Assessment Matrix

Risk Scenario	Likelihood (1-5)	Severity (1-5)	Risk Score	Mitigation Measures	Residual Risk
Re-identification of pseudonymized genetic data via public databases.	2	5	High	Use of differential privacy techniques; strict access controls; prohibition of data sharing outside the consortium.	Low
Unauthorized access by a researcher to full patient identity.	3	4	Medium	Separation of duties (key management); audit logs; role-based access control.	Low
Transfer of data to a third country lacking adequacy decision.	2	5	High	Standard Contractual Clauses (SCCs); Transfer Impact Assessment (TIA); encryption of data in transit and at rest.	Medium

Section E: Security Measures (Technical & Organizational)

• Encryption: [Details of key management]
• Access Control: [MFA, VPN, Bastion hosts]
• Physical Security: [Server room access, sample storage security]
• Incident Response Plan: [Procedure for breach notification within 72 hours]

Section F: Consultation and Sign-off

• DPO Opinion: [Attached or summarized]
• Ethics Committee Approval: [Reference Number]
• Supervisory Authority Consultation: [Date/Status if required]
• Project Lead Sign-off: [Signature]

Specific Considerations for Cross-Border Research (EU/EEA)

Biotech research is rarely confined to one member state. A German university might collaborate with a Greek hospital and a French sequencing lab. This triggers the “one-stop-shop” mechanism. The lead supervisory authority is usually where the “main establishment” is located (e.g., where the research coordination takes place). However, local branches must still engage with their local DPOs.

If the data is transferred outside the EU (e.g., to a US-based AI analysis firm), the DPIA must reference a Transfer Impact Assessment (TIA). This involves assessing the laws of the destination country (e.g., the US Cloud Act) and determining if they undermine GDPR protections. Mitigations might include “supplementary measures” such as splitting the encryption key so that neither the EU controller nor the US processor can decrypt the data alone.

Handling the “Right to be Forgotten” in Research

A major friction point in the DPIA is reconciling the Right to Erasure (Article 17) with the Scientific Research Exemption (Article 17(3)(b)). The GDPR acknowledges that enforcing the right to be forgotten is often incompatible with scientific research purposes.

However, this is not a blanket exemption. The DPIA should outline the protocol for withdrawal of consent. If a participant withdraws consent, can their data be deleted? Usually, if the data is already anonymized or aggregated, deletion is impossible. If it is still identifiable, the data should generally be deleted. However, the DPIA should note that if the research has reached the publication stage, or if the data is necessary for proving the validity of the results, the controller might have grounds to refuse deletion based on the public interest of the research. This nuance must be explained clearly to the data subject at the start of the trial.

Common Pitfalls to Avoid in Biotech DPIAs

Based on enforcement actions and guidance from European regulators, here are frequent errors to avoid when drafting your DPIA:

1. Vague Retention Periods: Using phrases like “until the research is finished” is insufficient. Define a specific timeframe or a clear event-based trigger (e.g., “deletion 1 year after the final publication”).
2. Ignoring the “Re-use” Phase: Many DPIAs cover