Your Breakthrough Is on Someone Else's Hard Drive
In 2019, a major cloud provider's employees were caught listening to user recordings to "improve services." In 2022, a university's cloud-based research management system was breached, exposing years of unpublished work.
Every time you upload to a cloud-based tool, you're making a trust decision—often without reading the 47-page terms of service that govern what happens to your data.
For casual documents, this rarely matters. For unpublished research, it can matter enormously. Let's talk about what's actually at stake.
- Grant proposals containing novel ideas
- Unpublished papers with breakthrough findings
- Datasets with sensitive information
- Code implementing proprietary methods
Understanding what happens to this data—and what could happen—is essential for protecting your work. Local-first tools that process everything on your device offer an alternative.
What Cloud Services Know
The Data They See
When you use cloud-based writing tools, the service has access to:
Your content:
- Every word you write
- Every equation you type
- Every figure you include
- Every revision you make
Your metadata:
- When you work
- How long you spend on each section
- Who you collaborate with
- What topics you research
Your behavior:
- Features you use
- Problems you encounter
- Patterns in your workflow
What They Do With It
Service providers typically use your data for:
Service improvement: Training autocomplete, improving error detection, optimizing performance.
Feature development: Building new tools based on how users work.
Analytics: Understanding user behavior for product decisions.
In some cases, training AI: Your content may train machine learning models.
Read the terms of service carefully. Many users are surprised by what they've agreed to.
The Risks Researchers Face
Intellectual Property Concerns
Your unpublished work has value. Before publication, you have limited protection:
- No patent claims established
- No publication priority proven
- No copyright registration (though automatic protection exists)
If your ideas leak before publication, you may lose the ability to:
- Claim priority
- File patents
- Establish yourself as the originator
Regulatory Compliance
Some research has legal data handling requirements:
HIPAA (Health data): Patient information must be handled according to strict guidelines.
FERPA (Education data): Student records require specific protections.
GDPR (EU data): Personal data of EU residents has extensive requirements.
Export controls: Some research cannot legally leave certain jurisdictions.
Using a cloud service may violate these requirements, even unintentionally.
Competitive Intelligence
In competitive fields, early access to research directions is valuable. Consider who might want your work:
- Competing research groups
- Companies monitoring academic advances
- State actors interested in strategic technologies
This isn't paranoia—it's risk assessment.
Reading the Fine Print
Key Terms to Find
When evaluating a cloud service, look for:
Data ownership: Who owns the content you create?
Data usage: How can the service use your content?
Data sharing: With whom can they share your data?
Data retention: How long do they keep your data?
Data location: Where is your data physically stored?
AI training: Is your content used to train models?
Red Flags
Watch for terms like:
- "Worldwide, royalty-free license" to your content
- Vague language about "improving our services"
- Unclear data retention policies
- Limited or no ability to delete your data
- Jurisdiction in countries with weak privacy laws
Green Flags
Prefer services with:
- Clear statements that you own your content
- Explicit limits on data usage
- Transparent data handling practices
- Strong encryption claims (and ideally, proof)
- Local data processing options
The Cloud vs. Local Trade-off
Cloud Advantages
Cloud services offer genuine benefits:
- No software installation
- Automatic updates
- Access from any device
- Easy sharing and collaboration
- Built-in backup
These conveniences have real value. The question is whether they're worth the privacy trade-off for your particular use case.
Local Advantages
Local-first tools provide:
- Complete data control
- No third-party access
- Offline functionality
- No terms of service for your content
- Regulatory compliance by design
Modern local-first tools can match many cloud conveniences while maintaining privacy.
A Framework for Decisions
Categorize Your Work
Not all documents need the same protection:
Low sensitivity:
- Class assignments
- Public documentation
- Already-published work
Medium sensitivity:
- Work in progress
- Internal reports
- Non-competitive research
High sensitivity:
- Grant proposals
- Unpublished breakthroughs
- Proprietary methods
- Regulated data
Match Tools to Sensitivity
Low sensitivity work can safely use any convenient tool.
Medium sensitivity work deserves privacy-respecting tools, but cloud services with good policies may be acceptable.
High sensitivity work should use local-first tools with no cloud component, or services with end-to-end encryption where the provider cannot access your content.
Evaluate Each Tool
For each tool in your workflow:
- What data does it access?
- Where is that data stored?
- Who can access it?
- What do the terms of service permit?
- What's the worst-case scenario if the data leaks?
Practical Steps
Audit Your Current Tools
List every tool you use for research:
- Document editors
- Reference managers
- Data analysis software
- Collaboration platforms
- Cloud storage
For each, find and read the privacy policy.
Implement Tiered Security
Create different workflows for different sensitivity levels:
For sensitive work:
- Use local-first editors
- Encrypt sensitive files
- Limit cloud sync
- Vet collaborators carefully
For routine work:
- Use convenient tools
- Accept reasonable privacy trade-offs
- Focus on productivity
Stay Informed
Privacy policies change. Services get acquired. New risks emerge. Periodically:
- Review terms of service for changes
- Check security news for your tools
- Reassess your risk profile
The Local-First Option
Local-first software represents a different approach to cloud services:
How It Works
Instead of storing your data on company servers, local-first tools:
- Keep your data on your device
- Process everything locally
- Sync directly between your devices (if you choose)
- Never require cloud access to function
The Privacy Benefit
With local-first tools:
- No company servers store your content
- No terms of service govern your data
- No data breach can expose your research
- No policy change can affect your privacy
Your documents stay on your computer. Period.
The Trade-off
Local-first isn't perfect:
- You're responsible for backups
- Collaboration requires more setup
- Some features may be harder to implement
These are manageable trade-offs for many researchers.
When Cloud Is Appropriate
Not everyone needs maximum privacy. Cloud services may be fine if:
- Your research isn't competitively sensitive
- You're not handling regulated data
- The convenience significantly improves your productivity
- You've read and accept the privacy policy
- You have no institutional restrictions
There's no universal right answer—only the right answer for your situation.
Conclusion
Your research represents years of effort, expertise, and creativity. It deserves thoughtful protection.
This doesn't mean avoiding all cloud services. It means:
- Understanding what you're giving up
- Making conscious choices about trade-offs
- Using appropriate tools for appropriate tasks
- Protecting your most sensitive work
The tools you choose shape the risks you take. Choose wisely.