Built Around Data Flows
- Pipeline-centric, not file-centric
- Designed for validation, not monitoring
- End-to-end traceability across data flows
The Proof Gap
Modern data systems lack reliable ways to prove how data was produced, transformed and used.
This creates risks in critical domains where decisions depend on data.
What SafeDataPath does
- Captures data provenance across pipelines
- Tracks every transformation and processing step
- Generates cryptographically verifiable evidence
- Enables independent validation of outputs
Built for Trusted Operations
SafeDataPath integrates into existing environments without requiring changes to data pipelines or infrastructure.
It enables organizations to:
- Establish trusted data provenance across distributed systems
- Preserve evidence of processing and transformations
- Support independent validation of outputs
- Maintain audit-ready proof across the data lifecycle
It operates across:
- Connected and disconnected environments
- From data sources to the end-users, across the most distributed data chains
- Heterogeneous and legacy infrastructures
Why SafeDataPath is Different
Pipeline-Centric
Evidence-Driven Trust
Cryptographic Proof by Design
- Evidence-driven, not log-driven
- Verifiable, cryptographic proof
No Data Exposure
Data Stays Where It Is
- Data / evidence separation
- No need to move or centralize data
Infrastructure Compatible
Seamless Integration
- Non-intrusive integration
- Cryptography-agnostic
- Integrates without disrupting existing workflows
Frequently Asked Questions
What is trustworthy data lineage?
Trustworthy data lineage is the ability to prove how data was produced, not just to describe where it comes from.
It combines execution traceability, cryptographic evidence, timestamps, and validation rules to make data pipelines auditable, verifiable, and legally defensible.
Unlike traditional data lineage, trustworthy lineage is designed by construction, not reconstructed after execution.
What is the difference between data lineage and data provenance?
Data lineage typically describes dependencies and flows between datasets.
Data provenance focuses on origin and transformations of a specific dataset.
Trustworthy data lineage goes further: it proves that a specific result was produced by a defined process, executed as expected, by identified actors, at a given time.
How is SafeDataPath different from cryptographic anchoring solutions?
Cryptographic anchoring solutions (DLTs, blockchains, ...) primarily prove that a data object existed at a given time and was not modified.
SafeDataPath proves something different:
- that a process was executed
- according to a defined schema
- by identified actors
- under specific rules
- producing a given result
in short:
- anchoring proves an object
- SafeDataPath proves a process
Does SafeDataPath require access to the data itself?
No.
SafeDataPath follows a strict data / proof separation principle.
it can operate entirely on:
- hashes
- metadata
- execution traces
- cryptographic proofs
This allows traceability without data filtration, including in zero-trust or disconnected environments.
Who needs trustworthy data lineage?
Trustworthy data lineage is critical when:
- data crosses organizational boundaries
- results must be certified or audited
- liability and responsibility matter
- disputes or regulatory scrutiny are possible
typical use cases include:
- earth observation and space situational awareness
- defence applications
- regulated data sharing
- algorithmic auditability
- AI explainability
- compliance and litigation support