Metadata-Only Telemetry: Reducing Sensitive Data in LLM Observability

As Large Language Models (LLMs) transition from internal playgrounds to external customer-facing features, enterprise developers face a massive paradox: they cannot optimize what they cannot measure, but they cannot measure what Infosec blocks.

Traditional software tools rely on structured databases. AI agents, however, interact using unstructured conversations. A customer support agent might ingest medical IDs, credit cards, or internal API keys mid-conversation. Sending these raw prompt traces to a third-party SaaS observability platform is a can create regulatory, contractual, and corporate data-governance risk.

The Proxy Vulnerability

Most LLM observability tools run as an inline API proxy. They require you to change your API base URL so that all your LLM traffic is routed through their servers. While convenient, it means every raw prompt and system instructions—including highly proprietary business logic—travels through a third-party gateway before reaching OpenAI or Anthropic.

For banks, healthcare providers, and high-security SaaS, this is an immediate dealbreaker.

Introducing Compliance Mode

To bridge the gap between data governance and observability, Observyze provides a metadata-only configuration called Compliance Mode. When direct provider routing and content capture controls are both enabled, SDK-generated telemetry omits raw prompts and completions before buffering.

1. Bypass the Proxy (`enableProxyRedirect: false`)

This forces the SDK to route your LLM traffic directly from your own servers to OpenAI or Anthropic. Your raw prompts do not pass through the Observyze cloud gateway. The model provider still receives the request under your provider agreement.

2. Local SDK-side PII Redaction (`enablePiiRedaction: true`)

Before transmitting telemetry asynchronously, deterministic patterns redact supported values such as SSNs, emails, payment-card numbers, and common API-key formats in memory. Regex masking is not NER and cannot reliably identify arbitrary names.

Drop-in Compliance Setup

Configure local redaction and content omission when initializing the SDK:

import { Observyze } from '@observyze/sdk';

const obs = new Observyze({
  apiKey: process.env.OBSERVYZE_API_KEY,
  enableProxyRedirect: false, // Bypass the Cloud Proxy
  enablePiiRedaction: true,   // Redact PII locally in VPC
  captureContent: false,      // Omit prompts/completions from SDK telemetry
});

Multi-Year Retention & Cold Storage Archival

Retention requirements vary by data type, jurisdiction, contract, and customer policy. Keeping large prompt/completion traces in a hot primary database can also increase cost and affect query performance.

Observyze provides a configurable S3-compatible archival worker:

• Automated Payload Pruning: Traces older than the retention window (e.g. 30 days) have their raw text payloads safely streamed to S3-compatible cold storage in compressed NDJSON format (.jsonl.gz).
• Queryable Metadata: High-level trace metadata remains in MongoDB; latency depends on deployment size and indexes.
• Archive Manifests: Each batch writes a JSON manifest with trace counts and timestamps. Configure versioning and Object Lock in the bucket if immutability is required.
• Customer-Managed Storage: Operators can configure an S3-compatible bucket and are responsible for its encryption, access, retention, and backup controls.