Governance and Operating Model

FA is not only a technical architecture for distributed computation, but also a governed operating model for multi-party analysis. Even where raw data remains local, participating organisations must still agree on who may submit queries, what types of analyses are permitted, how outputs are reviewed, and under what conditions results may be released.

A typical FA deployment therefore requires clear roles, agreed analytical scope, and procedures for query approval, execution, and release.

3.1 Key Roles in an FA Deployment

1. Participating organisations

   Each participating organisation retains control over its local dataset and is responsible for operating its local compute node in accordance with the agreed governance framework. This may include applying local access controls, maintaining data quality, and ensuring that only approved queries are executed on its data.

2. Platform operator or coordinating party

   Where a centralised or secure centralised architecture is used, a coordinating party typically manages query distribution, orchestration, logging, and aggregation. Depending on the trust model, this party may also enforce system-wide policies on authentication, query structure, and result handling.

3. Authorised query users

   Queries should only be submitted by approved users acting within a defined analytical scope. In practice, this often means restricting access to named individuals, specific teams, or approved use cases, rather than allowing unrestricted querying across participating datasets.

4. Approvers or release authorities

   Many FA deployments include a governance step before results are released. Approvers may review outputs for disclosure risk, validate that the query falls within permitted scope, and confirm that release conditions have been met.

3.2 Key Governance Considerations

1. Permitted analytical scope

   Participants should agree upfront on what types of analysis are allowed. In many deployments, this is implemented through predefined query templates, parameter bounds, or a restricted library of supported analyses. This helps reduce ambiguity and limits the risk of inappropriate or overly disclosive queries.

2. Access control and authorisation

   FA systems should ensure that only authorised users and approved nodes can participate. This typically includes authentication of users and nodes, role-based access controls, and restrictions on who may view intermediate or final outputs.

3. Output review and release

   Even where only aggregated results are shared, outputs may still create disclosure risks. FA deployments should therefore define what checks apply before results are released, such as minimum cell thresholds, validation against agreed scope, or manual review by an authorised approver.

4. Auditability and accountability

   Query submissions, execution events, approvals, and releases should be logged to support traceability and accountability across participants. In multi-party settings, these records can be important for incident review, compliance, and maintaining trust between organisations.