Auxiliary analysis · not outcome proof

One SAFE model, two execution systems.

Data/AI architecture and DHF govern different business objects, but both need explicit specification, authority, facts, and recovery. This comparison illustrates transferability; it does not prove deployment or adoption.

Data / AI architecture

Events, schema, data quality, and model decisions.

Delivery Harness Framework

Requirements, changes, execution, and verification results.

Shared control

SAFE contract

Data / AI

Specification binds schema and expected decisions; authorization limits data and model actions; facts preserve provenance; error recovery supports replay and rollback.

DHF

Specification binds completion; authorization limits effects; facts bind current receipts; error recovery returns to trusted state without restoring permission.

TRUST 01

Quality

Schema and data quality

  • Canonical schema aligns downstream meaning.
  • Truth and challenge sets test completeness and ordering.
  • Idempotent writes prevent bad state from replacing trusted results.

Requirements and fresh receipts

  • Acceptance criteria define completion.
  • Validation gates stop unverified promotion.
  • Fresh receipts bind command, result, and time.
TRUST 02

Risk

Evidence and review tiers

Evidence quality controls model decisions; privacy, bias, and other high-risk concerns enter human review.

Authority and lanes

Local, demo, and production authority stay separate; unknown or stale authorization fails closed.

TRUST 03

Speed

Adapters and backfill

Adapters isolate source variation while stream and backfill paths reuse decision semantics.

Routing and profiles

Lifecycle routing and risk profiles load only the context and governance the current task needs.

TRUST 04

Continuity

Replay and recovery

Raw archives, retry/DLQ, backfill, and drift checks support recomputation.

Checkpoint and rollback

Append-only state, rollback, handoff, and fresh readback support safe continuation.

TRUST 05

Scale

Provenance

Source identity and layered data state support attribution and audit.

Evidence layers

Source, runtime, publication, production, and customer outcomes remain separate claims.

Implementation ownership

Ownership

Adapters and runtime platform

Domain quality rules, drift thresholds, telemetry, and service recovery belong to the implementing data platform.

Generic control contract

DHF defines specification, authority, facts, and recovery interfaces without claiming to implement every platform capability.

Evidence boundary: this is a cross-domain analysis. It is not evidence that a Data/AI platform, production enforcement, customer adoption, or commercial result currently exists.