# SAFE Framework - VectorCat

> The SAFE framework extends FAIR principles with enterprise security and auditability for biopharma. Zero-trust infrastructure, grounded audit evidence and conditional FAIR access.

Source: https://www.sci2sci.com/safe-principles

# SAFE: Secure & Auditable FAIR Environment

A framework for enterprise biopharma data management that extends FAIR principles with the security and compliance controls required in regulated environments.

[### Security Zero-trust infrastructure & least surface exposure](#security)[### Auditability Audit evidence as a first-class entity](#auditability)[### FAIR Environment Conditional FAIR through governance first](#fair-environment)

## Overview

In biopharma enterprises, FAIR (Findable, Accessible, Interoperable, Reusable) principles alone are insufficient. Data cannot simply be shared freely — even within the organization. SAFE provides an enabling layer that delivers full security and auditability to control and govern FAIR use of data.

Security

## Security

Our core security framework implements two main principles: **Zero-Trust Security of Infrastructure** and **Least Surface Exposure Processing**.

### Zero-Trust Security

Full Transparency

Our solution supply chain is fully reviewable. Each release includes a complete Software Bill of Materials (SBOM), and all critical functionality is available for code review upon request.

Infrastructure Control

Any data storage, encryption and AI functionality is controlled by the customer. We provide functions — you decide how data is stored, processed and encrypted to match your strictest policies on GxP data handling.

Full Isolation

We support flexible deployment models to meet your security requirements:

-   Isolated private cloud installation
-   On-premises deployment
-   Air-gapped deployment with minimal dependencies

We run with zero home-calls, and any third-party APIs are optionalBy the way, you may have noticed that this website doesn't have cookie banners. That's because it doesn't track you. for integration.

### Least Surface Exposure

All functionality follows the principle of least privilege and is designed to minimize data exposure at every stage.

#### Progressive Access Model

Each data object begins with processing only its metadata. Default access permissions are granted only to admins and compliance personnel. As data passes through clearance gates, we gradually extend surfaces:

| Stage | Processing | Access |
| --- | --- | --- |
| Ingested | Metadata only | Admins & Compliance |
| Indexed | Deterministic checks + Local ML | \+ Data Protection Stewards |
| Cleared | Content Access + LLM processing | \+ Domain Stewards |
| Governed | LLM + Human in the loop | \+ Domain users (per policy) |
| Assembled | Enterprise system access | \+ Internal API + End users |

Governance policy is progressively shaped based on feedback from the Compliance/Data Protection/Domain Steward, ensuring that access and processing capabilities expand only as appropriate controls are validated.

AI Agent Constraints

AI agents available to users are always bound by user permissions:

-   AI cannot modify data without explicit user approval
-   AI cannot access any data inaccessible to the user
-   All AI actions inherit the user's read permissions

Auditability

## Auditability

In addition to logging access, events and changes — and validating critical changes with stewards — we implement **audit evidence as a first-class entity**. Evidence entities must satisfy three core principles: **Grounded**, **Transparent** and **Verifiable**.

### Grounded

Evidence objects must have a clear origin in ground truth — metadata or documents. We do not permit evidence without quotes, except in the edge case of evidence of absence.

### Transparent

The algorithm or reasoning chain for assigning evidence and selecting ground truth is immediately available for review. This applies to all methods of deriving data:

-   Deterministic algorithms
-   Statistical methods
-   AI reasoning chains

There are no hidden, non-auditable decisions.

### Verifiable

Each consumer must have sufficient information to understand how data is supported by evidence and sufficient context to falsify the decision:

-   Quotes include adequate context
-   Source documents must be available for independent summarization, review or reprocessing
-   Results must be reproducible

After verification, evidence is linked to the reviewer, creating a complete chain of accountability.

FAIR Environment

## FAIR Environment

FAIR principles — Findable, Accessible, Interoperable, Reusable — are the north star for scientific data collaboration. In highly competitive, regulated industries like biopharma, implementing these principles is particularly challenging.

### Our Approach: FAIR Through Governance First

Instead of fighting between utility and transparency on one side, and trade secrets and strict regulations on the other, we first implement FAIR principles for **compliance, data protection and data governance teams** enterprise-wide.

This provides them with the leverage to:

-   Shape organization policies in a semi-automated manner
-   Build the foundation of organization-wide FAIR access policies

Real-time propagation of both new policies and their immediate application to newly indexed data allows us to classify, govern, annotate and utilize data faster than any manual process would allow.

### The Result: Conditional FAIR

The result is an environment where data is:

Findable — but only for those who need to find it

Accessible — but only after checking permissions

Interoperable — but only with other assets you have access to

Reusable — but only if contracts and regulations allow

The only way to implement FAIR in the enterprise is by making it SAFE.

## How SAFE Enables FAIR

| FAIR Principle | Challenge in Biopharma | How SAFE Addresses It |
| --- | --- | --- |
| Findable | Sensitive data cannot be exposed in enterprise catalogs | Tiered metadata visibility; users see only what they're permitted to find |
| Accessible | Regulatory and IP constraints restrict enterprise access | Permission-gated access with steward validation at each stage |
| Interoperable | Data silos exist for legitimate security reasons | Controlled interoperability within user's access boundary |
| Reusable | Contracts, consent and regulations limit reuse | Policy-aware reuse; governance rules enforced automatically |

## Ready to make your data SAFE?

See how VectorCat implements the SAFE framework and how it can transform your organization.

[Book a Call](https://www.sci2sci.com/enroll) [See Use Cases](https://www.sci2sci.com/usecases)

Based on [GO FAIR Principles](https://www.go-fair.org/fair-principles/)