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Observability & audit — Use case

Real-time API and AI telemetry on top of Elasticsearch, audit enforced at the persistence layer, and an evidence packet the regulator actually accepts.

Platform teams · Telemetry · For platform teams

Real-time telemetry. Auditor-grade evidence. Same plane.

Every request, every change, every grant — captured at the framework boundary, indexed in Elasticsearch, queryable by humans and exportable to regulators.

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The problem

The problem

Observability and audit are usually two separate budgets — and two separate gaps.

Ops gets a dashboard. Compliance gets a spreadsheet. The two don't reconcile, and when a regulator asks 'what happened on Wednesday at 14:32', someone joins three tools and prays. Apinizer treats both as the same plane: the audit ledger is queryable telemetry, and the telemetry is auditor-grade.

Capabilities

Audit at the persistence layer

Enforced at the framework boundary — bypass is rejected, not policed by convention. Who, what, when, from where; immutable, exportable, regulator-shaped.

Elasticsearch-backed analytics

Every request indexed in real time — request, response, latency, headers (with PII masked), and the policy chain that handled it.

Anomaly detection

EMA + Bollinger bands on traffic shape, latency, and error rate. Set sensitivity once; the gateway opens alarms only when the curve actually breaks.

Severity-aware alarms

Four grades, nine action types, fifteen-plus connectors. P1 reaches on-call; P3 lands in the daily digest.

Forensic timelines

Join request logs to audit events to policy changes — for any window, any consumer, any endpoint. A single query answers the regulator.

Per-tier dashboards

Platform sees everything. Project owners see their domain. Environment operators see the lane they run. Same data, different scopes.

Real-world examples

Banking

Scenario: Istanbul Tier-1 bank ships BDDK quarterly evidence as one export

Outcome: What used to be a three-week project becomes a saved query. Every grant, change, and request joins on one timeline.

Metric: 3 weeks → 1 query

Healthcare

Scenario: Munich hospital network detects a quiet PHI exfiltration attempt

Outcome: Traffic shape on a low-volume endpoint drifted 4× in three hours. Anomaly fired in 12 minutes; SOC stopped the integration before the next batch.

Metric: 12 min to detection

Government

Scenario: Riyadh ministry serves a regulator's 'who, what, when' query in 90 seconds

Outcome: Audit is queryable — not a forensic build-out. The regulator's request closes the same business day.

Telecom

Scenario: Madrid carrier joins customer support tickets to API audit timelines

Outcome: Support ticket → consumer → audit chain → root cause. Mean time to explain drops from days to minutes.

Insurance

Scenario: Amsterdam insurer alerts on claim-pricing drift in 8 minutes

Outcome: Bollinger bands on pricing API response distribution. A bad model push moved the curve; alarm fired before customers noticed.

Metric: 8 min to alarm

Energy

Scenario: Prague utility audits SCADA gateway access for 14000 sessions

Outcome: Operators see only their substation; auditors see the union. The annual compliance file is generated automatically on December 31.

Public sector

Scenario: Rome ministry exports GDPR Article 30 records of processing

Outcome: Personal-data flows joined to consent records, retention windows, and access logs. Article 30 is a saved view, not a yearly project.

Banking

Scenario: Baku bank catches an SLA breach on payments before the regulator does

Outcome: Anomaly + severity-aware fan-out: P1 reached on-call in 90 seconds; root cause posted to the SOC in 7 minutes.

Capture, index, query, alarm — in one plane.

  • 01 · Capture — Every request, change, grant, and view is captured at the framework boundary — no opt-in, no convention.
  • 02 · Index — Telemetry and audit land in Elasticsearch in real time. PII masked on the way in; raw bodies optional and scoped.
  • 03 · Query — Platform, project, environment views; saved searches; ad-hoc joins. Regulator-shaped exports on a schedule.
  • 04 · Alarm — Anomaly + severity policy fan out across nine action types and fifteen-plus connectors. P1 to on-call, P3 to digest.
  • Analytics Engine — Elasticsearch-backed real-time analytics for every request and policy decision.
  • Monitoring — Uptime probes, anomaly detection, and severity-aware action chains in one module.
  • API Gateway — The runtime where audit is enforced at the persistence layer, not bolted on later.
  • AI Gateway — Same observability and audit for LLM, MCP, and agent traffic.

Resources

  • Observability overview — How telemetry and audit share the same plane — and what regulators look for.
  • Analytics Engine — Real-time per-endpoint, per-consumer, per-region telemetry on Elasticsearch.
  • Monitoring — Uptime, anomaly detection, severity-aware alarms — one module.
  • Compliance posture — Mapping to BDDK, KVKK, GDPR, ISO 27001 evidence requirements.
  • APIops manifests — Changes flow through Git; audit captures the apply, the actor, and the diff.
  • Architecture overview — Where audit and telemetry live in the data plane.

Next step

Telemetry meets evidence

One plane for ops and audit.

A 30-minute walkthrough — capture, index, query, alarm — on a Kubernetes of your choice.

Book a Demo · Read the docs

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