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Trust & Research Integrity

Trust shouldn't be a feature.
It should be part of the architecture.

OVAITY combines secure infrastructure, FAIR data management and the OVAITY TRACE™ Framework to create a foundation for trustworthy AI-native research.

Secure by design. FAIR by design. TRACEable by design.

Trust architecture

TRACE builds on a foundation of secure infrastructure and FAIR data management.

AI-native research needs a stronger foundation.

Scientific AI is increasingly able to search, analyse, transform and act on research data. That creates enormous opportunity — but also a new requirement: scientific work must remain understandable, governed and connected to evidence.

OVAITY approaches this through three layers of trust.

SECURE

Protect the research.

FAIR

Make the data usable.

TRACE

Make the research trustworthy.

Foundation 01

Secure infrastructure

Protect the research before AI ever touches it.

Security is the lowest foundational layer supporting everything above it. OVAITY is built around governed access, Swiss-hosted infrastructure and encryption so scientific work can remain protected as AI becomes part of the research process.

Data Sovereignty

Research data remains within clearly defined infrastructure and jurisdictions. OVAITY is designed around Swiss-hosted infrastructure to support data sovereignty for sensitive research.

Governed Access

Users, services and AI agents only receive access to the data and capabilities they are authorised to use — through role-based permissions and project-level controls.

Encryption & Isolation

Research data is protected in transit and at rest, with clear separation between organisations and workspaces.

Customer Control

Organisations remain in control of access, integrations, AI permissions and approval requirements within their research environment.

Foundation 02

FAIR data by design

Trustworthy AI depends on scientific data that can be found, understood and reused. OVAITY is designed to support the FAIR Guiding Principles throughout the research lifecycle.

  1. Secure infrastructureenables
  2. FAIR dataenables
  3. TRACEable research

FAIR sits above Security and below TRACE — structuring scientific data so trustworthy research becomes possible.

Findable

Research objects are organised with context and metadata so humans and machines can discover them.

Accessible

Data access remains governed, while authorised users and systems can retrieve the information they need.

Interoperable

Structured data, integrations and shared context help information move between tools, workflows and agents.

Reusable

Provenance, metadata and scientific context preserve the meaning required to reuse research outputs.

The OVAITY TRACE™ Framework

From FAIR data to TRACEable research.

FAIR defines how scientific data should be managed. TRACE extends trust from the data itself to the experiments, analyses, AI agents, decisions and conclusions surrounding it.

TRACE builds on secure infrastructure and FAIR data management to define how trustworthy AI-native research should operate.

Traceable

Know where it came from.

Every dataset, experiment, transformation, analysis, AI action and decision should have a verifiable origin.

Examples

  • Data lineage
  • Scientific provenance
  • Source attribution
  • Analysis history
  • Decision history

Reproducible

Know how it was produced.

Research should preserve enough context to understand, reconstruct and repeat how results were generated.

Examples

  • Inputs and outputs
  • Methods and protocols
  • Parameters
  • Analysis history
  • Tool and workflow context

Accountable

Know who — or what — did it.

Human and AI actions should remain attributable.

Examples

  • Human vs AI attribution
  • Audit trails
  • Ownership
  • Approval history
  • Clear responsibility

Controlled

Autonomy within boundaries.

AI agents should operate within explicit permissions, datasets, tools and approval requirements.

Examples

  • Permissions
  • Dataset allowlists
  • Human approval gates
  • Pause / resume / revoke
  • Scope-limited agent actions

Evidence-Grounded

Know what supports the conclusion.

Scientific claims and AI outputs should remain connected to the underlying evidence.

Examples

  • Dataset references
  • Experiment references
  • Literature references
  • Source-backed AI responses
  • Distinction between evidence and AI interpretation

How the layers connect

One system. Three layers. One direction of trust.

TRACE is not replacing FAIR. TRACE extends FAIR principles from scientific data into the broader AI-native research process.

Security protects the environment.
FAIR structures the data.
TRACE governs the research process.

TRACE builds on secure infrastructure and FAIR data management.

Product implementation

Principles become useful when they shape the product.

OVAITY capabilities are designed so secure infrastructure, FAIR data management and TRACE principles show up in everyday research work — not only in policy language.

Analysis Studio

Analysis workflows and generated outputs remain connected to project context.

  • → Reproducible
  • → Traceable

Connected Project Workspace

Projects, studies, experiments, files and collaborators live in one structured environment.

  • → Findable
  • → Accessible

Scientific Memory

Preserves relationships, provenance and decisions so knowledge remains reusable over time.

  • → Reusable
  • → Evidence-Grounded
  • → Traceable

Trustworthy AI starts before the prompt.

OVAITY is building an environment where scientific data remains governed, research remains reproducible and AI remains connected to evidence.
Secure by design. FAIR by design. TRACEable by design.

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