Modern Data Pipeline & Strategy

Stop paying the "Splunk Tax" for low-value logs.

1. The Broken Model (Dump & Pray)

Traditionally, orgs installed Forwarders on every server and dumped everything into a SIEM (Splunk/Sentinel).

• Problem: 90% of logs (Verbose Windows Events, Debug logs) are never queried but cost millions to store.
• Latency: Searching petabytes of data is slow.

2. The Solution: Data Pipelines (Cribl Stream / OCSF)

Using a stream processor (like Cribl Stream or Vector) before the data hits the SIEM.

graph LR
    A[Sources: Endpoints, Firewalls, Cloud] -->|Raw Logs| B(Stream Processor)
    B -->|Parse, Enrict, Route| C{Router}
    C -->|High Value: Alerts| D[SIEM: Splunk/Sentinel]
    C -->|Bulk: Compliance| E[Data Lake: S3/Blob]
    C -->|Metrics| F[Observability: Grafana]

A. Routing & Forking

• High Value (Alerts): Route directly to SIEM (Splunk) for immediate Triage.
• Compliance (Bulk): Route to cheap object storage (AWS S3 / Azure Blob) aka Data Lake.
  • Result: Massive cost reduction. SIEM is fast and lean; data lake is cheap and deep.

B. In-Stream Detection (The "Zero Latency" Detection)

Detect threats in the pipeline before storage.

// Example Cribl Function: Detect Canary Token
if (event.url.includes("canarytokens.com")) {
    // Fire Webhook immediately
    sendWebhook("https://soar.internal/webhook", event);
    // Add tag for SIEM
    event.criticality = "CRITICAL";
}

• Scenario: A "Canary Token" triggers on the wire. The pipeline sees it in real-time and fires an alert/webhook immediately, effectively bypassing SIEM indexing latency.

3. In-Source Detection (API-Centric)

Why ingest terabytes of EDR telemetry when the EDR console already has it?

• Concept: Use Python/SOAR to query the Source Technology APIs (CrowdStrike/SentinelOne API) periodically or on-demand.
• Example:
  • Old Way: Ingest all ProcessRollup2 events to SIEM. Search for cmd.exe.
  • New Way: Script queries EDR API: Get-Processes | Where Name -eq 'cmd.exe'.
• Benefit: Zero ingestion cost. You use the vendor's compute, not yours.

4. OCSF (Open Cybersecurity Schema Framework)

Mapping all data to a common language.

• Problem: CrowdStrike calls a user User_Name. Okta calls it actor.displayName. Windows calls it TargetUserName.
• Solution: Normalize ALL logs in the pipeline to OCSF (user.name).
• Impact: You write ONE detection rule ("Detect user.name failed login > 5") and it works for AWS, Okta, and Windows simultaneously.