TraceFlux

AI INTELLIGENCE

Evidence-first intelligence, governed by design.

AI accelerates prioritization, prediction, and investigation — while deterministic boundaries, governance policies, and replay validation determine authority.

AI Control Plane

Deterministic Core → Incident Engine · Governance · Replay · Audit
AI Layer → Ranking · Risk Modeling · Recommendations · Investigation Assistant
Enforcement → Policy Scope · Approval Gates · Tenant Isolation

AI enhances control — it does not replace it.

  • • AI ranks and explains signals
  • • AI predicts risk and drift likelihood
  • • AI suggests bounded remediation
  • • Deterministic logic defines incident boundaries
  • • Governance determines execution authority
  • • Replay validates AI-influenced decisions

What AI is not

  • • Not black-box incident authority
  • • Not unbounded automation execution
  • • Not policy bypass
  • • Not unverifiable decision-making

Intelligence services

Signal Ranking & Confidence

Input

Alerts, telemetry, historical patterns

Output

Prioritized signals with confidence scores

Guardrail

Cannot create or merge incidents — annotation only

Incident Explanation

Input

Deterministic incident timeline

Output

Evidence-linked natural language explanation

Guardrail

Must reference incident timeline anchors

Risk & Drift Prediction

Input

Configuration change + topology + blast radius model

Output

Predicted impact zones and risk score

Guardrail

Remediation requires policy + approval

Remediation Recommendation

Input

Incident context + runbook library

Output

Bounded action suggestions with required approvals

Guardrail

Execution remains policy-scoped

Replay-Augmented Validation

Input

Historical telemetry replay

Output

Validation of recommendation impact

Guardrail

Promotion requires replay pass

Every AI output has an audit-grade decision trace

AI Output
Evidence Anchor
Policy Outcome
Approval
Execution
Replay Result
Rank Incident #432
Topology + latency spike
Eligible
Not required
Escalated
Validated

Replay-driven model lifecycle

  1. 1. Observe telemetry and deterministic outcomes.
  2. 2. Generate AI recommendation.
  3. 3. Validate recommendation on historical replay data.
  4. 4. Measure false positives / false negatives.
  5. 5. Promote refined model or policy update.
  6. 6. Record lifecycle update in audit ledger.

Isolation is non-negotiable

AI models operate within strict tenant partitions. No cross-tenant telemetry mixing. No shared inference leakage. Every AI rationale and input is recorded for audit visibility.

Adopt AI without weakening your control plane.