Best Practices: Splunk to Sentinel Migration¶

Status: Authored 2026-04-30 Audience: SOC Managers, Security Architects, Migration Project Leads Purpose: Proven best practices for executing a successful Splunk-to-Sentinel migration with CSA-in-a-Box security analytics integration

1. Migration strategy: use-case-by-use-case¶

The golden rule: do not attempt a big-bang migration¶

The single most important best practice: migrate use case by use case, not all at once. A use case is a complete detection-and-response workflow: data source + detection rule + response playbook + dashboard.

Example use cases:

Migration order¶

Migrate use cases in this order:

Phase 1 -- Low risk, high value (Weeks 3-8):

1. Microsoft-native data sources (M365, Entra ID, Defender XDR)
2. Detection rules that operate on these free data sources
3. Basic automation (assign, tag, notify)
4. Overview dashboards

Why first: These data sources are free to ingest, natively integrated, and represent the lowest migration risk. Quick wins build confidence.

Phase 2 -- Core security operations (Weeks 9-16):

1. Perimeter security (firewall, IDS/IPS via CEF)
2. Endpoint security (Windows/Linux via AMA)
3. Core detection rules (brute force, port scan, malware)
4. Primary SOAR playbooks (block, disable, isolate)
5. SOC operational dashboards

Why second: These are the bread-and-butter SOC operations. They require data connector deployment (AMA, CEF forwarders) but are well-understood use cases with high Content Hub coverage.

Phase 3 -- Advanced analytics (Weeks 17-24):

1. Cloud security (AWS, GCP, Azure)
2. Identity analytics (behavioral, impossible travel)
3. Advanced detection rules (multi-stage, statistical)
4. Complex SOAR playbooks (multi-step orchestration)
5. Threat hunting queries and notebooks

Why third: These require more sophisticated KQL and deeper Sentinel knowledge. SOC analysts have built KQL proficiency by this point.

Phase 4 -- Specialized and compliance (Weeks 25-32):

1. Compliance-specific use cases (PCI, HIPAA, FISMA)
2. Custom data sources and proprietary formats
3. Executive/CISO dashboards (Power BI via CSA-in-a-Box)
4. Long-term historical data migration to ADX
5. Remaining Splunk-specific automation

2. Parallel-run strategy¶

Why parallel run is essential¶

Running Splunk and Sentinel simultaneously for a period is critical for:

• Detection coverage validation -- confirm Sentinel catches what Splunk catches
• SOC analyst confidence -- analysts can verify Sentinel behavior against familiar Splunk
• False positive tuning -- identify and fix KQL translations that behave differently than SPL
• Stakeholder assurance -- demonstrate to leadership that migration is safe

Parallel-run implementation¶

flowchart LR
    subgraph Sources["Data Sources"]
        S[Security Data Sources]
    end

    subgraph Parallel["Parallel Run"]
        SPL[Splunk - read-only, validation]
        SENT[Sentinel - primary, operational]
    end

    subgraph Validation["Validation"]
        COMP[Alert comparison dashboard]
        COVER[MITRE coverage comparison]
        METRIC[Metrics: FP rate, MTTR, coverage %]
    end

    S --> SPL
    S --> SENT
    SPL --> COMP
    SENT --> COMP
    COMP --> COVER
    COMP --> METRIC

Parallel-run checklist¶

Dual-forwarding configuration¶

During parallel run, data sources send to both SIEMs:

# For syslog/CEF sources: configure rsyslog to forward to both
# /etc/rsyslog.d/60-dual-forward.conf

# Forward to Splunk Heavy Forwarder
*.* @@splunk-hf.agency.gov:514

# Forward to Sentinel AMA log forwarder
*.* @@sentinel-forwarder.agency.gov:514

For Windows endpoints, run both Splunk UF and AMA simultaneously during the parallel period.

3. Detection coverage validation¶

MITRE ATT&CK coverage mapping¶

Before migration, document your Splunk detection coverage by MITRE ATT&CK technique:

Splunk (export current coverage):

| rest /servicesNS/-/-/saved/searches
| search action.correlationsearch.enabled=1
| rename action.correlationsearch.annotations.mitre_attack as mitre
| mvexpand mitre
| stats count by mitre

Sentinel (verify post-migration coverage):

SecurityAlert
| where TimeGenerated > ago(30d)
| extend Tactics = parse_json(ExtendedProperties).Tactics
| mv-expand Tactics
| summarize RuleCount = dcount(AlertName) by tostring(Tactics)
| sort by RuleCount desc

Coverage gap resolution¶

4. SOC analyst training program¶

Training curriculum¶

KQL proficiency milestones¶

Security Copilot as training accelerator¶

Security Copilot dramatically reduces the KQL learning curve:

1. Natural language to KQL: Analysts describe what they want to find; Copilot generates the query
2. Query explanation: Analysts paste a KQL query; Copilot explains what it does
3. Query optimization: Copilot suggests improvements to existing queries
4. SPL-to-KQL translation: Analysts paste an SPL query; Copilot translates to KQL

Training exercise: Have analysts use Copilot to translate their 10 most-used Splunk searches to KQL. This builds both Copilot and KQL proficiency simultaneously.

5. Data quality validation¶

Ensuring data parity¶

During migration, validate that Sentinel receives the same data quality as Splunk:

// Data completeness check: compare event counts per source
// Run daily during parallel-run period
union withsource=TableName *
| where TimeGenerated between (ago(24h) .. now())
| summarize EventCount = count() by TableName
| sort by EventCount desc
// Compare with equivalent Splunk query:
// | metadata type=sourcetypes | table sourcetype, totalCount

Field mapping validation¶

Ensure critical fields are present and correctly mapped:

// Validate field completeness for security events
SecurityEvent
| where TimeGenerated > ago(1h)
| summarize
    TotalEvents = count(),
    HasSourceIP = countif(isnotempty(IpAddress)),
    HasAccount = countif(isnotempty(TargetAccount)),
    HasComputer = countif(isnotempty(Computer)),
    HasEventID = countif(isnotempty(EventID))
| extend
    SourceIP_Pct = round(100.0 * HasSourceIP / TotalEvents, 1),
    Account_Pct = round(100.0 * HasAccount / TotalEvents, 1),
    Computer_Pct = round(100.0 * HasComputer / TotalEvents, 1)

6. CSA-in-a-Box security analytics integration¶

When to set up CSA-in-a-Box¶

Deploy CSA-in-a-Box security analytics capabilities after Phase 2 (core security operations) of the migration, when Sentinel is handling primary detection:

Cross-domain security analytics patterns¶

The unique value of CSA-in-a-Box for security: combining SIEM data with business data in Fabric:

Security data product example¶

Publish a curated "Incident Metrics" data product via CSA-in-a-Box:

# domains/security/data-products/incident-metrics/contract.yaml
data_product: incident_metrics
domain: security_operations
owner: soc_engineering_team
model: gold.fact_incident_metrics
classification: internal
columns:
    - name: incident_id
      data_type: string
      tests: [not_null, unique]
    - name: created_date
      data_type: date
      tests: [not_null]
    - name: severity
      data_type: string
      tests: [accepted_values: [critical, high, medium, low, informational]]
    - name: time_to_triage_minutes
      data_type: integer
    - name: time_to_resolve_minutes
      data_type: integer
    - name: mitre_tactics
      data_type: string
    - name: automated_resolution
      data_type: boolean
sla:
    freshness: PT1H
    availability: 99.5
consumers:
    - ciso_office
    - compliance_team
    - risk_management
compliance:
    frameworks: [fedramp_high, nist_800_53]

7. Change management¶

Stakeholder communication plan¶

Resistance mitigation¶

8. Post-migration optimization¶

30-day post-cutover checklist¶

• All high-severity detection rules verified (generating alerts as expected)
• False positive rates within acceptable range (tuning complete for critical rules)
• Playbook execution verified (end-to-end automation functional)
• SOC analysts cleared to work independently in Sentinel
• Commitment tier right-sized based on actual consumption
• Basic Logs tier applied to appropriate high-volume tables
• Data Collection Rules optimized (filtering unnecessary events)
• Historical data export to ADX verified and queryable
• Executive dashboards deployed in Power BI (CSA-in-a-Box)
• Splunk decommission plan finalized with stakeholder sign-off

90-day post-cutover checklist¶

• Detection coverage validation against MITRE ATT&CK baseline
• Security Copilot adoption metrics reviewed (usage, value assessment)
• Cost comparison vs Splunk documented and shared with finance
• KQL proficiency assessment for SOC analysts
• Content Hub solutions reviewed for new coverage opportunities
• UEBA and Fusion rules assessed for effectiveness
• CSA-in-a-Box cross-domain analytics use cases implemented
• Compliance documentation updated for new SIEM architecture
• Lessons learned documented and shared

9. Common pitfalls and how to avoid them¶

Summary: the 10 commandments of SIEM migration¶

1. Migrate use case by use case, not all at once
2. Start with free Microsoft data sources for quick wins
3. Run parallel for at least 8 weeks before cutover
4. Validate MITRE ATT&CK coverage before and after
5. Train analysts on KQL with Security Copilot as an accelerator
6. Optimize costs with commitment tiers, Basic Logs, and DCR filtering
7. Deploy CSA-in-a-Box for cross-domain analytics and long-term retention
8. Update compliance documentation (SSP, POA&M, control mappings)
9. Communicate continuously with stakeholders at all levels
10. Document everything for the next agency that follows your path

Next steps:

• Migration Playbook -- execute the migration
• Federal Guide -- federal-specific considerations
• Benchmarks -- performance validation data

Maintainers: csa-inabox core team Last updated: 2026-04-30