<!-- Copyright (c) 2026 defconxt. All rights reserved. -->
<!-- Licensed under AGPL-3.0 — see LICENSE file for details. -->
---
name: performing-log-volume-optimization
description: >-
  Optimize security log volumes through intelligent filtering, aggregation,
  sampling, and deduplication while preserving detection fidelity and
  compliance retention requirements.
domain: cybersecurity
subdomain: security-data-engineering
tags:
  - log-optimization
  - filtering
  - aggregation
  - sampling
  - cost-reduction
version: "1.0"
author: defconxt
license: AGPL-3.0
metadata:
  frameworks: ["NIST 800-92", "CIS Controls v8"]
  tools: ["python3", "vector", "logstash", "cribl"]
  mitre-attack: ["T1070"]
---

# Performing Log Volume Optimization

## Overview

Security log costs scale with volume. Optimization reduces storage and compute
costs without sacrificing detection capability. This skill covers filtering
low-value events, aggregating repetitive patterns, sampling high-volume sources,
deduplicating redundant data, and measuring the detection impact of each
optimization.

## Prerequisites

| Requirement | Details |
|---|---|
| Log volume metrics per source and event type | Required |
| Detection rule inventory mapped to required data sources | Required |
| Understanding of compliance retention mandates | Required |
| Access to log pipeline configuration (Vector, Logstash, Cribl) | Required |

## Key Concepts

- **Value-based filtering**: Drop events with zero detection utility
- **Aggregation**: Collapse repetitive events into summary records
- **Sampling**: Probabilistic reduction for high-volume, low-signal sources
- **Detection impact analysis**: Verify no active rules lose required telemetry
- **Compliance hold**: Exempt regulated sources from optimization

## Workflow

### Step 1: Analyze Volume Distribution

```python
def analyze_volume(events: list[dict]) -> dict:
    """Analyze event volume by source and type."""
    distribution: dict[str, int] = {}
    for event in events:
        key = f"{event.get('source_type', 'unknown')}:{event.get('class_uid', 0)}"
        distribution[key] = distribution.get(key, 0) + 1
    sorted_dist = dict(sorted(distribution.items(), key=lambda x: x[1], reverse=True))
    return {"total": len(events), "distribution": sorted_dist}
```

### Step 2: Identify Optimization Candidates

```sql
-- Find top volume sources with low detection rule coverage
SELECT
    source_type,
    COUNT(*) AS daily_volume,
    COUNT(DISTINCT class_uid) AS event_types,
    COALESCE(r.rule_count, 0) AS detection_rules
FROM security.events e
LEFT JOIN detection_rule_coverage r ON e.source_type = r.source_type
WHERE time_dt >= CURRENT_DATE - INTERVAL '1 day'
GROUP BY source_type, r.rule_count
ORDER BY daily_volume DESC;
```

### Step 3: Apply Optimization Rules

```yaml
# Vector pipeline: filter and aggregate
transforms:
  filter_noise:
    type: filter
    condition: .severity_id >= 2 || .class_uid in [3001, 3002, 4001]
  aggregate_dns:
    type: aggregate
    interval_ms: 60000
    group_by: ["src_endpoint.ip", "dns.query.hostname"]
    merge_strategies:
      event_count: sum
```

### Step 4: Validate Detection Integrity

```bash
node agent.js analyze --input events.jsonl --output volume-report.json
node agent.js simulate --config optimization.yml --rules-dir ./rules/ --verify
```

## Detection

```yaml
title: Log Volume Optimization Detection
id: 6e14427f-0d91-4320-a6f8-2a32d337a74e
status: experimental
description: Detects suspicious activity related to performing log volume optimization techniques in security data engineering context
logsource:
  category: process_creation
  product: linux
detection:
  selection:
    CommandLine: "*performing*log*"
  condition: selection
level: medium
tags:
  - attack.t1070
  - attack.collection
falsepositives:
  - Data pipeline ETL processes during scheduled batch windows
```


**Detection Opportunities**

| Indicator | Source | Detection Logic |
|---|---|---|
| Log Volume Optimization Detection | linux/process_creation | Sigma rule (medium) |
| ATT&CK Coverage | MITRE ATT&CK | T1070 |

## References

- [Cribl Stream](https://cribl.io/) — Observability pipeline
- [Vector](https://vector.dev/) — Log processing
- [NIST SP 800-92](https://csrc.nist.gov/publications/detail/sp/800-92/final)
