Modern research is collaborative. Biotech companies partner with academic institutions. Research consortiums span organizations and countries. Clinical trials involve multiple sites. Drug discovery requires external computational resources.

Each collaboration involves data sharing. And each data share creates security risk.

The tension is real: research culture values openness; security demands controls. Researchers want frictionless collaboration; security teams want auditability and boundaries. Overly restrictive security kills partnerships; insufficient security risks intellectual property and regulatory violations.

This guide provides a framework for enabling secure research data sharing—balancing protection with the collaboration that makes research productive.

The Landscape of Research Data Sharing

Research data sharing takes many forms:

Types of Collaborations

Academic partnerships: Sharing data with university research groups for analysis, validation, or joint research.

Consortium participation: Multi-party research initiatives where data is pooled or federated.

Contract research organizations (CROs): Outsourced research functions requiring data access.

Computational partnerships: External processing using your data—cloud HPC, specialized analytics providers.

Data licensing: Providing data to partners under license for their research purposes.

Regulatory submissions: Sharing data with regulatory agencies, often through third-party submitters.

Types of Data Shared

Raw research data: Experimental results, observations, measurements.

Processed/analyzed data: Derived data sets, analysis outputs, models.

Clinical data: Patient-level data from trials, often with identifiers.

Proprietary methods/protocols: How research was conducted, often sensitive IP.

Pre-publication findings: Conclusions not yet publicly released.

Each data type has different sensitivity and regulatory implications.

The Risk Framework

Before designing controls, understand what you’re protecting against.

Intellectual Property Theft

Research data often represents significant investment and competitive advantage. Theft scenarios:

• Partner personnel misappropriating data for their own research
• Nation-state actors targeting research through academic partnerships
• Data leaking to competitors through informal sharing

Risk factors:

• Novel/unpublished research (higher value to steal)
• Competitive research areas (higher attacker motivation)
• Partners with weak security (easier target)

Regulatory Violations

Improper data sharing can violate:

• HIPAA (patient data shared without authorization)
• GDPR (EU data transferred without adequate protections)
• Export controls (research with dual-use applications)
• IRB protocols (consent violations)

Risk factors:

• Patient-identifiable data
• International transfers
• Controlled research areas

Publication and Priority

Data leaking before publication can:

• Allow others to scoop your research
• Undermine patent priority
• Damage research reputation

Risk factors:

• Competitive research areas
• High-impact potential findings
• Patent-dependent commercialization

Contamination and Integrity

Data received from partners could:

• Introduce errors that propagate through your research
• Create dependency on unreproducible results
• Expose you to data of questionable provenance

Risk factors:

• Critical dependence on external data
• Limited ability to validate externally-sourced data
• High-stakes conclusions based on shared data

The Tiered Approach

Not all data sharing requires the same controls. Tier based on sensitivity.

Tier 1: Highly Sensitive

What it includes:

• Novel, unpublished research data
• Patient-identifiable clinical data
• Proprietary methods central to competitive advantage
• Data subject to export controls

Required controls:

• Formal data sharing agreement with security terms
• Encryption in transit and at rest
• Access limited to named individuals
• Audit logging of all access
• Time-limited access (expiration dates)
• No download without specific authorization
• Legal review of agreement

Transfer mechanisms:

• Secure collaboration platforms with DLP controls
• Virtual data rooms
• Federated access (data stays in your environment)

Tier 2: Sensitive

What it includes:

• Pre-publication research data
• Aggregated/anonymized patient data
• Standard research collaborations
• Data under typical confidentiality expectations

Required controls:

• Data sharing agreement
• Encryption in transit
• Access controls (not necessarily named individuals)
• Reasonable security requirements for partner
• Defined retention and destruction requirements

Transfer mechanisms:

• Secure file transfer
• Cloud collaboration platforms
• Partner-controlled environments with security attestation

Tier 3: Limited Sensitivity

What it includes:

• Published data sets
• Non-proprietary methods
• Validation/benchmarking data
• Educational data sets

Required controls:

• Basic agreement or terms of use
• Reasonable attribution expectations
• Standard transfer security

Transfer mechanisms:

• Standard file transfer
• Data repositories
• Direct sharing

Key Controls for Research Data Sharing

Data Sharing Agreements

Every non-trivial data share needs a written agreement covering:

What data is covered: Specific description of data elements, not vague references.

Permitted uses: What can the recipient do with the data? What’s prohibited?

Security requirements: Minimum security controls the recipient must maintain.

Access limitations: Who at the recipient can access? Are there named individuals?

Duration: How long can data be retained? When must it be destroyed?

Return/destruction: What happens when the collaboration ends?

Audit rights: Can you verify compliance?

Breach notification: What happens if the recipient is compromised?

Publication rights: Who can publish what, when?

Liability: Who’s responsible if something goes wrong?

Don’t skip this because “it’s just an academic collaboration.” Agreements protect both parties.

Technical Controls

Access management:

• Unique credentials for each partner user
• Avoid shared accounts
• Time-bound access (expires after project end)
• Access reviews for long-running collaborations

Data protection:

• Encryption in transit (TLS)
• Encryption at rest
• Consider client-side encryption for highest sensitivity
• Data loss prevention monitoring

Audit logging:

• Record all access to shared data
• Record data downloads/exports
• Retain logs for agreement duration plus buffer

Environment security:

• Understand where data will reside
• Assess partner environment security
• For highest sensitivity, consider federated access (no data transfer)

Federated and Privacy-Preserving Approaches

For highest-sensitivity data, consider approaches that don’t require data transfer:

Federated analysis: Partners run analysis against your data in your environment, receiving only results—not underlying data.

Secure enclaves: Data is transferred to an isolated environment where partners access but can’t extract.

Differential privacy: Add noise to data or results to protect individual records while enabling aggregate analysis.

Synthetic data: Generate data that statistically resembles real data but doesn’t contain actual records.

These approaches are more complex but may enable collaborations that would otherwise be too risky.

Operationalizing the Program

Request and Approval Process

Establish a process for data sharing requests:

1. Request intake: Standard form capturing data type, recipient, purpose, duration.
2. Classification: Determine data tier and applicable requirements.
3. Risk assessment: For higher tiers, assess specific risks.
4. Approval: Appropriate authority based on tier (research lead, security, legal, executive).
5. Agreement execution: Appropriate agreement for tier.
6. Provisioning: Technical setup for access.
7. Monitoring: Ongoing oversight during collaboration.
8. Termination: Orderly end of access at completion.

Partner Security Assessment

For Tier 1 and Tier 2 data, assess partner security:

For academic partners:

• Does the institution have a security program?
• What’s their track record with research security?
• Who specifically will access data?
• How will data be stored and protected?

For commercial partners:

• SOC 2 or equivalent attestation
• Security questionnaire completion
• Specific commitments in agreement

Academic institutions often have weaker security than commercial partners. Adjust expectations and controls accordingly.

Monitoring and Compliance

Don’t just set up sharing and forget it:

• Monitor access logs for anomalies
• Periodic access reviews to verify continued need
• Check-ins on compliance with agreement terms
• Plan for termination before it’s needed

Common Failure Modes

“It’s just academic collaboration” Academic doesn’t mean low-risk. Nation-state actors target academic partnerships. Academic security is often weaker than commercial. Treat academic partners with appropriate rigor.

Researcher-to-researcher informal sharing Data shared via personal email, personal cloud accounts, USB drives. No oversight, no agreements, no controls. Build processes that are easy enough that researchers use them.

Indefinite access Access granted for a project never revoked. Ten years later, data is still accessible to partner who’s moved on. Build expiration into every share.

No visibility into partner security Data transferred to partner environment with no understanding of their security. Trust but verify—or adjust what you share based on what you can verify.

Over-restriction killing collaboration Security requirements so onerous that researchers avoid the process entirely, or valuable collaborations don’t happen. Balance protection with enablement.

Building Research-Friendly Security

The goal isn’t to prevent data sharing—it’s to enable it safely.

Make the secure path easy. If the approved process is harder than emailing an Excel file, researchers will email the Excel file.

Understand research workflows. Security designed without understanding how research actually works will be circumvented.

Tiered controls match tiered risk. Don’t apply maximum controls to everything. Save rigor for what needs it.

Build relationships with research teams. Security that understands and supports research goals earns cooperation.

Accept that some risk is necessary. Research requires collaboration. Collaboration requires sharing. Zero risk means zero collaboration.

The tension between openness and security is real but manageable. Build a program that acknowledges both, and you enable the partnerships that drive research forward.