TraceFlux

AI INTELLIGENCE · TECHNICAL ARCHITECTURE

AI Control Plane Architecture

TraceFlux integrates AI modules within a deterministic control plane. AI provides ranking, prediction, and recommendation — while governance, replay validation, and tenant isolation enforce execution authority.

System overview

Telemetry Ingestion Layer

  • • Alerts, metrics, logs, flow, BGP, DNS
  • • Configuration changes
  • • Topology & service graph signals

Deterministic Core

  • • Incident formation engine
  • • Trust & suppression logic
  • • Automation governance
  • • Replay & parity validation
  • • Immutable audit ledger

AI Intelligence Layer

  • • Feature extraction pipeline
  • • Signal ranking engine
  • • Risk & drift modeling
  • • Remediation recommendation engine
  • • Investigation assistant
  • • Model validation loop

Signal ranking pipeline

  1. 1. Feature extraction from correlated telemetry.
  2. 2. Context enrichment with topology and historical patterns.
  3. 3. Similarity scoring against historical incidents.
  4. 4. Confidence weighting based on service criticality.
  5. 5. Annotation of ranked signals within incident timeline.

AI outputs are annotations and prioritization signals. They do not create or merge incidents. Deterministic boundaries remain authoritative.

Predictive risk & drift modeling

Drift and change candidates are evaluated using blast radius graphs, service dependencies, and historical change impact patterns.

  • • Risk scoring of configuration drift
  • • Predicted affected surface estimation
  • • Recommended governance scope
  • • Policy evaluation prior to remediation

Execution enforcement contract

  1. 1. AI generates suggestion or risk annotation.
  2. 2. Policy engine evaluates eligibility.
  3. 3. Approval requirements are checked.
  4. 4. Tenant scope validation is enforced.
  5. 5. Scoped execution occurs (if authorized).
  6. 6. Replay validation confirms correctness.
  7. 7. Immutable audit entry records rationale and outcome.

Replay-augmented model validation

AI recommendations are tested against historical telemetry through replay execution. False positives and regressions are measured prior to model or policy promotion.

  • • Historical dataset replay testing
  • • Regression detection
  • • Confidence threshold validation
  • • Controlled promotion of model refinements
Explore Replay & Parity Control →

Tenant isolation & data boundaries

AI operates within strict tenant partitions. Feature vectors, telemetry context, and inference pipelines are scoped per tenant. No cross-tenant signal mixing or shared inference leakage occurs.

Failure & uncertainty handling

  • • Low-confidence outputs are flagged.
  • • AI may recommend no action.
  • • Policy override remains authoritative.
  • • Suppressed suggestions are audit-logged.

Download the full AI Architecture Whitepaper

Detailed control plane diagrams, scoring logic, replay validation flow, and governance enforcement model.