A reference architecture for detecting and repairing runtime logic failures: semantic drift, cross-system inconsistencies, silent process death. Caught as they happen, not weeks later in an audit. Written for enterprise architects, CIOs, and principal engineers.
Enterprise data estates are instrumented at two extremes. At the infrastructure layer, APM tooling verifies that pipes are up, latency is bounded, and status codes are green. At the storage layer, data observability tooling profiles tables after ingestion. Between those two control points sits the runtime: the live interval in which payloads mutate, fields drop, transactional states drift apart, and stale feeds re-enter production with fresh timestamps.
mmune adds the missing control point: an autonomous immune system for the data estate, and the only runtime integrity layer built for the legacy, on-prem, and air-gapped estates the modern data stack was never designed to reach. It connects to the systems you already run, read-only, learns how your data actually behaves in production, and surfaces the unknown unknowns before they spread downstream.
Validates uptime, latency percentiles, and HTTP status codes. Structurally blind to payload content: a 200 OK carrying a currency code silently swapped from USD to JPY is, by design, a healthy transaction.
Profiles data at rest inside the warehouse or lake, after the fact. Detection happens hours to days after polluted records have already moved through billing runs, ML features, and downstream syncs.
Every architectural decision in mmune comes from a single premise: an integrity layer must never itself become a source of production risk, deployment friction, or compliance exposure.
mmune connects to databases, applications, and integration infrastructure through read-only connections. There is no inline interception, no proxy hop in the request path, no agent in the data plane. Production traffic never routes through an mmune process, and a global read-only default blocks any write action unless the client explicitly enables it.
Deployment means pointing mmune at the estate and letting it work. The AutoPilot onboarding loop discovers services on the network, configures and tests connections, registers each integration, and introspects its schema with no per-integration human action. No SDKs, no code changes, no redeployment of application services, no schema registration.
The engine works on metadata and mathematical signatures of data behavior: schemas, distributions, cadences, health scores. It keeps no bulk copies of client records. A small rolling validation sample, roughly ten records per source, supports drift detection and is purged automatically. For regulated and sovereign environments, the full stack, including local LLM models, runs air-gapped on premise.
The AI Context Graph is a continuously updated model of how the estate's data actually behaves: its schemas, lineage, semantic mappings, and learned baselines. Six watchdog dimensions each track a distinct aspect of runtime behavior. None of them needs configuration, schema declarations, or rule authoring. The graph converges on its own as mmune watches the estate.
Samples live values and learns statistical and semantic baselines for every field: value distributions, categorical vocabularies, magnitude envelopes. Flags the moment a stream departs from its fingerprint.
Learns each source's structure, types, and nullability directly from introspection. Catches a field that flips from integer to string, or quietly disappears, before a downstream parser does.
Maps every producer, consumer, and dependency into a live lineage graph, maintaining a picture of which systems legitimately feed which. Routes that were never part of it get flagged.
Learns the rhythm of every stream: hourly patterns, batch windows, burst envelopes. Catches silent blockages and starved feeds that heartbeat monitors keep reporting as healthy.
Maps every field to a shared canonical model, so the same business entity is comparable across CRM, ERP, and core databases. When two systems stop agreeing about the same record, it surfaces as an incident.
Tracks timestamps, identity, and message metadata across sources to spot recycled feeds, replayed batches, and duplicate transactional events posing as fresh traffic.
Each mode below runs with green infrastructure dashboards and syntactically valid transport. The matrix maps the technical cause, the downstream operational impact, and the mmune dimension that catches it. Duplicate & freshness coverage (modes 07 and 10) is opt-in: operators must set MMUNE_AUTO_DUPLICATE_SCAN=true because that path samples live row values; it is off by default.
Detection starts as contextualized, deduplicated alerting in Slack and the Health Center: one root-caused incident per failure, not a page of symptoms. As confidence in the context graph grows, teams switch the healing mode from alert to autonomous. Every healing action targets mmune's own connections and mappings, never your production data, and the global read-only default blocks anything beyond that unless the client enables it.
Correlates related symptoms into a single root-caused incident and routes it to Slack and the dashboard. Deduplication keeps one failure from turning into forty pages at 3am.
Continuously checks the health of every integration, classifies failures by error type, and retries and restores broken connections automatically. Degraded links surface long before they go dark.
When a schema drifts, mmune proposes corrected field mappings for review. In autonomous mode, suggestions above a confidence threshold apply automatically; everything else waits for a human.
The end-to-end pathway from your infrastructure to your team's dashboard. Sources stay in place. mmune connects, it never copies. Only metadata leaves your environment, TLS-encrypted, and for air-gapped deployments nothing leaves at all.
Thirty minutes of setup. Ten days of read-only observation against your live estate. One forensic report listing every runtime failure mode currently running undetected, with zero production risk, zero code changes, and no bulk data retained.
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