Building effective SIEM dashboards and telemetry pipelines¶

Core objectives¶

• Correlate attacker tactics, techniques and procedures (TTPs) with event data across hybrid environments

• Identify visibility gaps through systematic mapping against the MITRE ATT&CK framework

• Empower security teams with actionable indicators and historical context for investigations

Dashboard development principles¶

Essential components for all platforms¶

• TTP correlation views - Map detection rules to ATT&CK techniques

• Process lineage visualisation - Parent/child relationships with command-line context

• Network activity overlays - GeoIP mapping and protocol analysis

• Time-bound analysis - Sliding windows for incident timeframes

Platform-specific implementations¶

Elastic stack (Kibana) dashboards¶

Recommended visualisations:

1. ATT&CK technique matrix with detection coverage gaps highlighted

2. Process creation events (Sysmon Event ID 1) with parent process context

3. Network connection maps enriched with threat intelligence feeds

Sample event schema:

event:
  category: ["process", "network"]
  module: "powershell"
process:
  name: "powershell.exe"
  parent:
    name: "explorer.exe"
network:
  direction: "outbound"
destination:
  ip: "185.234.219.112"

Splunk dashboards¶

Key search patterns:

index=sysmon (process_name=powershell.exe OR command_line="*Invoke*")
| stats count by user, host, parent_process_name, command_line
| lookup mitre_technique_lookup command_line OUTPUT technique_id, tactic
| table technique_id, tactic, count

Effective visualisations:

• Kill chain phase progression charts

• Notable events by severity/frequency heatmaps

• Detection rule efficacy over time

Telemetry pipeline architecture¶

Recommended data flows¶

Critical enrichment steps¶

1. Field normalisation (ECS/CIM standards)

2. Threat intelligence lookups (MISP, Sigma)

3. ATT&CK technique tagging

4. Business context mapping (asset criticality)

Threat hunting enablement¶

Data retention strategy¶

• High-fidelity logs: 90 days minimum for endpoints/network

• Alert metadata: 12 months for trend analysis

• Enriched events: 30 days in hot storage

Correlation improvements¶

# Example Sigma-to-Splunk conversion logic
def convert_sigma_rule(rule):
    splunk_query = f"index={rule['logsource']} "
    splunk_query += " AND ".join(rule['detection']['conditions'])
    return {
        'search': splunk_query,
        'tags': [f"attack_{t}" for t in rule['tags']]
    }

Tooling matrix¶

Common challenges and mitigation¶

Visibility gaps¶

• Problem: Alert-centric monitoring misses stealthy activity

• Solution: Baseline normal activity and hunt for deviations

Schema issues¶

• Problem: Field name mismatches break correlations

• Solution: Enforce ECS/CIM standards with validation checks

Dashboard relevance¶

• Problem: Static views become outdated

• Solution: Quarterly reviews against current threat intel

Recommended improvements¶

1. Develop YAML-based detection rule converters

2. Integrate purple team exercise results

3. Build ATT&CK coverage scorecards

4. Implement automated detection validation

Detection validation workflows¶

Automated rule testing framework¶

# Pseudocode for detection validation
def test_detection_rule(rule, test_cases):
    alerts_triggered = 0
    for case in test_cases:
        if execute_siem_query(rule.query, case.log_entry):
            alerts_triggered += 1
    efficacy_score = (alerts_triggered / len(test_cases)) * 100
    return {
        'rule_id': rule.id,
        'efficacy': f"{efficacy_score}%",
        'false_positives': run_against_benign_logs(rule.query)
    }

Implementation steps:

1. Atomic test case generation

   • Create log entries matching ATT&CK techniques using tools like Caldera or Atomic Red Team

   • Example test case for T1059 (PowerShell):

     log_entry:
       process.name: "powershell.exe"
       command_line: "Invoke-Mimikatz -Command '\"sekurlsa::pth /user:admin /domain:corp /ntlm:hash\""'
     

2. Benign activity profiling

   • Collect 30 days of normal business activity logs

   • Use as control group for false positive measurement

3. Validation schedule

   • Weekly: Automated atomic tests

   • Quarterly: Purple team exercises with new TTPs

Storage tier cost-benefit analysis¶

Financial services data retention model¶

Cost optimisation strategies:

1. Selective retention

   • Keep full-fidelity logs only for critical assets (SWIFT terminals, DCs)

   • Sample non-critical endpoints at 20% (reduces storage by ~65%)

2. Compression benchmarks

   • Zeek logs: 80% reduction with Zstandard

   • Windows events: 70% reduction with LZ4

3. Cloud cost controls

   • AWS S3 lifecycle policies to auto-transition after 30 days

   • Elasticsearch cold tier with frozen indices

Team competency requirements¶

Cross-functional skills matrix¶

Staffing consideration: A 24/5 SOC covering 50k endpoints typically requires:

• 6 L1 analysts (4 shifts)

• 3 threat hunters

• 2 SOC engineers

• €85k/year in training budget

Implementation checklist¶

Phase 1: Foundation (Weeks 1-4)¶

• Deploy log collection infrastructure

• Normalise critical field names (ECS/CIM)

• Build 5-10 core ATT&CK-aligned dashboards

Phase 2: Validation (Weeks 5-8)¶

• Establish detection testing framework

• Profile benign activity baselines

• Conduct first purple team exercise

Phase 3: Optimisation (Ongoing)¶

• Implement storage tiering policies

• Monthly detection efficacy reports

• Quarterly skills gap analysis