Research summarization
Unpublished findings or experimental notes submitted for drafting or synthesis.
Research value and AI governance
Protect Research Data and Intellectual Property in the Age of AI
Enable researchers to benefit from advanced AI while reducing uncontrolled movement of unpublished findings, proprietary data, source code and emerging intellectual property into unmanaged environments.
Valuable before it is classified
Protect what has not been discovered yet
A research observation today may become a patent, licence, startup, dataset, algorithm, software product, clinical insight or commercial partnership tomorrow. The institution may possess significant value before it has formally classified that value as intellectual property.
AI governance for research therefore needs to account for unpublished findings, sponsored work, experimental data, source code, laboratory records, student work, confidential partnerships and commercialization pathways—not only documents already marked confidential.
Protect the discovery process without preventing researchers from benefiting from useful AI.
Common exposure pathways
How valuable research can enter unmanaged AI environments
The goal is not to assume every external service behaves the same. It is to know which service, account, terms and information are involved before sensitive work is submitted.
Code assistance
Proprietary algorithms, scripts or source code shared during troubleshooting.
Dataset analysis
Sensitive or commercially valuable datasets provided to external AI tools.
Sponsored research
Material subject to industry, government, publication or confidentiality agreements.
Patent timing
Potentially patentable details disclosed before legal or commercialization review.
Student and faculty use
Distributed users making individual decisions without a consistent institutional routing framework.
A better institutional response
Give researchers somewhere safe to use AI
Prohibition alone sacrifices value and can encourage workarounds. A stronger model is to combine clear information classification with sanctioned AI environments that are sufficiently capable for real academic work.
AFA can help institutions map research information classes to approved cloud, enterprise, private or sovereign environments; integrate institutional identity and permissions; evaluate models; ground AI in trusted knowledge; and design training that helps researchers understand where work may safely happen.
Institutional pathway
From policy ambiguity to usable research AI
Protecting research requires technical, policy and human layers to agree.
Map obligations
Identify ethics, sponsorship, privacy, IP, patent, publication and data requirements.
Classify information
Define research information classes and the AI environments permitted for each.
Provide approved tools
Make sanctioned AI useful enough for researchers to choose it.
Train & support
Give students, faculty and staff practical decision rules and escalation routes.
Review continuously
Update the approach as services, terms, models and research programs change.
Continue the journey
Related pathways
Confidential researchPatent timingCommercializationInstitutional control
The value at risk
Research is not ordinary organizational data
Universities and research organizations create economic and public value through knowledge, inventions, methods, software, partnerships and commercialization pipelines.
Separate public, internal, confidential, regulated and commercialization-stage information.
Define what may use approved public services and what requires additional controls.
Review permissions, retention, vendor terms, jurisdiction and approved users.
Consider invention disclosure, patent timing, sponsor obligations and value leakage.
Request the next conversation
Share the workload, decision or outcome you need to clarify. A human will review the context.
Enable discovery without surrendering institutional value.
Start with the information, obligations and commercialization pathways that must remain protected.