AI Digital Twin & Safe Replay Studio: One‑click production clone, anonymized traffic replay, and “what‑if” simulation

Problem

• Changes to productions (new DTLs, rules, adapters, timeouts, upgrades) are risky because test environments rarely mirror real traffic, partner quirks, or peak volumes.

• Reproducing an incident or validating a partner change requires manual data gathering, custom scripts, and coordination, delaying delivery and increasing outage risk.

Proposal

• Add a “Create Digital Twin” wizard in the Management Portal that clones an existing production into an isolated sandbox namespace and replays real traffic (anonymized) to validate changes before go‑live.

• The studio uses AI to auto‑generate targeted test cases, predict risk and capacity impact, and highlight behavioral diffs between “current” and “candidate” configurations.

Customer value

• Faster onboarding and safer releases; fewer production incidents; confident partner testing; clear audit trail for compliance.

• Shortens days/weeks of manual UAT to hours, with measurable risk scoring and automated rollback guidance.

Key capabilities

• One‑click clone

  • Copies Ens.Config.Production and Ens.Config.Item into a sandbox namespace.

  • Automatically stubs outbound Operations (switches to simulated endpoints or “Null” ops) to avoid touching partners.

  • Imports related classes/DTLs/BPL/RuleSets and compiles.

• Data capture and replay

  • Sources: Message Bank, Ens.MessageHeader + body payloads, journals, and Visual Trace links.

  • Choose time window, message types, and rate: 1x, accelerated, or peak profile.

  • Maintains causality/correlation (SessionId, MessageBodyId, Sync responses).

• PHI‑safe anonymization

  • Built‑in de‑identification transforms for HL7 (segment/field maps), FHIR (bulk deface via FHIRPath), JSON/XML (XPath/JSONPath rules).

  • Configurable tokenization so referential integrity across messages is preserved.

• AI test authoring and risk scoring

  • Analyzes historical errors/timeouts and auto‑creates edge‑case messages.

  • Suggests mutation tests for fields with high defect history or schema drift.

  • Produces a Risk Score per change (DTL edit, rule tweak, timeout change) with top contributing factors.

• Diff and impact analysis

  • Compares old vs new behavior at multiple levels:

    • Routing decisions (RuleSet outcomes)

    • DTL field‑level differences (added/removed/mutated values)

    • Operation call patterns, HTTP status mixes, latency distributions

  • Generates a human‑readable “Change Impact Report” and a machine‑readable JSON for CI/CD gates.

• Capacity and SLA simulation

  • Predicts queue growth, CPU/IO hot spots, and p95 latency under projected volumes.

  • Recommends adapter settings (pool size, retry/backoff), index suggestions, and throttling strategy.

• Release support

  • Exports a self‑contained zpm module with test corpus and replay scripts for CI.

  • Optional blue/green and canary assist: route X% of live traffic through the candidate flow and compare outcomes in real time (read‑only shadow mode).

Technical design (IRIS‑specific)

• Namespace topology

  • DTN_ sandbox namespace with roles limited to read/write locally; no external credentials.

  • Production definition copied via Ens.Config.Production/Item APIs; operations swapped to DTN.MockOperation subclasses.

• Replay engine

  • DTN.Replay.Service reads Message Bank/Ens.MessageBody by ts range; drives messages through the cloned Production preserving sequence and sync semantics.

  • Uses EnsLib.* adapters in loopback or embedded mocks; generates canned responses from recorded traces when needed.

• Anonymization

  • DTN.Deidentify.DTLs for HL7/FHIR/JSON with pluggable lookup/token services (HS.* libraries where available).

  • Policy stored in DTN.Security.Policy; enforced on import and before replay.

• Diff engine

  • DTN.Diff.Router compares RuleLog; DTN.Diff.DTL walks source/target objects and produces field‑level deltas with severity tags.

  • Results persisted in DTN.Results.* tables; exposed via SQL views for dashboards.

• AI integration

  • Embedded Python for test generation and risk scoring (scikit‑learn/xgboost) or IntegratedML models over DTN.Results.Aggregates.

  • LLM-powered explanations that cite exact rules/DTL nodes and link to source.

Portal UX

• New action: Interoperability > Productions > “Create Digital Twin”

• Wizard steps: Select production and time window -> choose anonymization policy -> select changes to test (new DTL, rule file, parameters) -> run replay -> view Impact Report.

• Dashboards: Diff Summary, Risk Score, Capacity Forecast, and “Open in Visual Trace” deep links for any anomaly.

Security and governance

• Never reuses production credentials; outbound endpoints are mocked by default.

• All PHI handling is policy‑driven with audit logs of every sample and transform.

• Role‑based access; approvals required to export artifacts or enable shadow traffic.

MVP acceptance criteria

• Clone a production into a sandbox namespace; swap outbound Operations to mocks.

• Import messages from Message Bank for a selected window; apply de‑identification.

• Execute replay and show:

  • Routing decision diffs

  • Field‑level DTL diffs for at least one message type

  • Latency and queue forecasts under 1x and 3x volume

• Generate an Impact Report and export a zpm bundle with test data and replay job.

• Performance: replay 10k messages in <10 minutes on a standard dev box.

Example scenarios

• Validate a new HL7→FHIR DTL before a payer go‑live; catch unexpected PID/OBX mapping changes and propose fixes.

• Reproduce a weekend outage using last week’s traffic and confirm that a timeout + retry policy prevents reoccurrence.

• Capacity plan for a new site onboarding by simulating 2x traffic with current hardware.

Success metrics

• 70% reduction in UAT cycle time for interface changes.

• 50% fewer post‑release defects tied to mapping/routing.

• Documented audit packages produced automatically for every release.