Evidence is generated, not written up.
A new layer of signed evidence on top of the MLOps systems your team already uses.
From your team’s work to signed evidence
Froga generates signed evidence to document your AI system and prove compliance with applicable regulatory requirements.
- risk and treatment
- documented justification
- dataset
- data lineage
- training algorithm
- run manifest
- metrics
- measured thresholds
- model
- signed provenance
Where it hooks in
Froga integrates with the organisation’s MLOps and DevSecOps layers to gather evidence for the technical documentation, through the available integrations.
Within the organisation
MLOps
Data versions, execution records and model metrics, according to what each tool provides.
Within the organisation
DevSecOps
Versioned code and continuous integration, where Froga verifies deliveries according to each connection’s capabilities.
Result in Froga
Technical documentation
Froga assembles and signs the evidence, linked to the data, model and documented controls.
What it works with
MLOps · data and executions
| Tool | Contribution | Scope |
|---|---|---|
| Lock file fingerprint | A third party can recompute it with the repository and its dependencies. | |
| Run metrics fingerprint | The metrics remain in MLflow’s store. | |
| Execution material fingerprint | The material remains in Dagster’s store. | |
| Provenance in the execution history | Data is referenced through pointers. |
DevSecOps · repositories and continuous integration
| Tool | Contribution | Scope |
|---|---|---|
| Delivery verification | According to Froga’s continuous integration configuration. | |
| Delivery verification | According to Froga’s continuous integration configuration. | |
| Repository connection | No delivery verification. |
Oversight and incidents
Froga gets your product’s oversight requirements ready and validated, and every incident goes back into the risk cycle. Reporting it to the authority and investigating it stay within your post-market surveillance.
If it fits so far, the next step is a conversation.
Let's talk