Replacing Microsoft Sentinel with Enterprise Security

Getting data in

Splunk Enterprise Security excels with its ability to ingest, normalize, and analyze data from any source, offering unmatched flexibility and scalability. Its wide array of edge and ingest capabilities allows organizations to ingest only the most critical data, optimize costs, and integrate seamlessly with third-party technologies. By contrast, Microsoft Sentinel heavily favors Microsoft products and offers limited support for third-party sources, often requiring extensive configuration. There are now over 1,500 integrations supported by Splunk and the vendors that Splunk partners with to help get data into your SIEM.

Storage flexibility

With Splunk software, organizations can control where and how data is stored, whether on-premises, in the cloud, or at the edge. This flexibility ensures cost-effective data management while maintaining visibility and retention for critical use cases. In contrast, Sentinel limits data storage options to predefined tiers, forcing organizations into rigid logging configurations that reduce control over critical data and increase inefficiencies over time.

Content and detections

Many SIEMs try to dazzle procurement departments with large numbers of pre-built detections. Sentinel, while offering detections, lacks transparency and usability outside its console, making it harder for practitioners to identify updates or threat mappings until an attack occurs. This reactive approach can leave gaps in security posture. Splunk provides a flexible platform that is capable of advanced analytics while reducing noise. Splunk Enterprise Security also delivers over 1,500 curated detections aligned with frameworks like MITRE ATT&CK, providing immediate value for those who want a head-start. It also keeps users updated on emerging threats through automated content updates from the Splunk Threat Research Team, who are regularly first in the market to provide targeted queries to major global and local incidents.

Reducing noise and prioritizing what matters

Security teams are drowning in data and overwhelmed with alerts. When Splunk customers use risk-based alerting (RBA), they see a 50% to 90% reduction in alerting volume. The remaining alerts are higher fidelity, provide more context for analysis, and are more indicative of true security issues.

RBA provides teams with a unique opportunity to pivot cybersecurity resources from reactive to proactive while building out a flexible foundation to mature security operations across multiple departments. As alert fidelity and true positive rates increase, analysts are freed up to work on higher-value tasks, such as threat hunting, adversary simulation, or building up their skill sets and preparation to better face evolving threats.

Sentinel lacks these advanced alerting capabilities, leaving analysts to sift through a high volume of alerts without clear prioritization, which can delay responses to critical incidents.

Simplified analyst workflow

Splunk software unifies threat detection, investigation, and response (TDIR) workflows, integrating seamlessly with tools like Splunk Mission Control, Splunk User Behavior Analytics, and Splunk Attack Analyzer. This broad support for hybrid and third-party environments makes it ideal for addressing diverse SOC needs. Sentinel, though it offers automation through Logic Apps, is heavily tied to the Azure ecosystem, limiting its extensibility and its ability to support organizations operating in multi-cloud or hybrid environments.

Community

After a decade of using Splunk Enterprise Security, the Splunk community is vast and sometimes fanatical, with a large recruiting pool and world-class talent. The Splunk community is very active and community feedback is key to ongoing improvements in Splunk Enterprise Security. Splunk also demonstrates its commitment back to the security community by being a founding member of the Open Cybersecurity Schema Framework (OCSF) and providing architectural flexibility with predictable costs. Microsoft, while contributing minimally to OCSF, prioritizes its proprietary standards, which might leave customers less prepared for evolving industry challenges.

Capability and feature differentiation

Here is a detailed side-by-side comparison highlighting key capabilities and features where Splunk Enterprise Security differentiates itself from Sentinel:

Summary

Microsoft has a good range of tools and analytics built into its Defender platform with a good range of out of the box alerts focused on monitoring Azure. Splunk software can ingest all the raw logs and perform the same analytics, and when ingesting data from the Defender suite, can use the exact same incidents you see in Sentinel.

Splunk Enterprise Security differentiates itself by being the best in class SIEM tool for bringing all this activity from Azure and blending it with security content, analytics, machine learning, and threat intelligence from every technology that matters to you, allowing your organization the flexibility to choose the best platforms for each use case, knowing it will work in the Splunk platform.

Summary

The Splunk platform is flexible, extensible, and can ingest a broad range of Microsoft data sources. The main advantages of Splunk software with Azure data are its vendor-neutrality, ability to correlate with any source, custom data handling, and control over deployment, retention, and automation.

Summary

Splunk PS provides a standardized approach to SIEM replacement with the Splunk Enterprise Security SIEM on Splunk Cloud Platform or on-premises with Splunk Enterprise, driving success through best practice design, rapid adoption and knowledge transfer. This standardized approach includes:

• Splunk Solutions Architects design the migration plan specifically around your organization's needs.
• Splunk software is designed, installed, and configured based on best practices.
• Identified data sources are onboarded to bring your security data into the Splunk platform.
• Splunk Enterprise Security is installed and configured.
• Key security use cases are implemented prescriptively.

Phased migration

In a phased migration, the transition happens gradually over multiple stages. This approach breaks the migration into smaller, manageable pieces, focusing on specific components or functionalities at a time.

• Key benefits: Reduced risk, flexibility to learn and improve during phases, and easier resource allocation.
• Challenges: Longer transition period, dual maintenance of old and new systems, and potential complexity in coordination.
• Best for: Large or complex systems and organizations that prioritize caution and gradual change.

Parallel migration

With parallel migration, the old and new systems run side by side for a period. This allows the new system to be tested and validated while the old system remains operational as a safety net.

• Key benefits: Risk mitigation with a fallback option, live testing in a real environment, and minimal disruption to operations.
• Challenges: High resource demands, complexity in keeping both systems synchronized, and added operational costs.
• Best for: High-stakes or mission-critical systems where business continuity is essential.

"Big bang" - all at once migration

A full cutover migration involves switching entirely to the new system in one event. The old system is decommissioned, and all data and processes are moved at once.

• Key benefits: Quick transition, simplified management, and elimination of dual-system overhead.
• Challenges: High risk if issues arise, extensive planning required, and potential downtime during the cutover.
• Best for: Smaller or low-risk migrations, or when speed and simplicity are key priorities.

Summary

The choice of migration approach depends on factors such as the complexity of the system involved, your organization’s tolerance for risk and downtime, resource availability, and the criticality of the system being migrated. Whatever your size and scale, we can adapt Sentinel to Splunk migrations to align with your business needs, technical challenges, and resource constraints.

KQL

SecurityEvent
| where EventID == 4625
| where TimeGenerated >= ago(1h)
| summarize Count = count() by AccountName, IpAddress
| sort by Count desc

SPL

index=security_event EventID=4625 earliest=-1h
| stats count as Count by AccountName, IpAddress
| sort -Count

Explanation of query components

Data mapping and transformation

While conversion between KQL and SPL is fairly straightforward, you can go further and make this use case applicable to all your security data sources using the Common Information Model (CIM) and leverage the Authentication data model. The Authentication data model in Splunk software is a normalized model that organizes authentication-related logs (e.g., logins, logouts, failures) into consistent field names, regardless of the original data source.

So let's adapt the original KQL detection into a Splunk search that will search not just the Windows server logs for failed log-ins, but all failed log-ins across all authentication data in the CIM, regardless of technology.

You'll need to adapt the query to use the CIM's standardized field names and structure.

Steps to convert to CIM-compliant SPL query

| datamodel Authentication Authentication
| search action=failure earliest=-1h
| stats count by user, src
| sort - count

Explanation of the CIM-compliant SPL query

The CIM allows you to normalize data from diverse sources (e.g., Windows logs, Linux logs, and firewall logs) into a consistent schema, mapping events like Windows Event 4625 (failed login) to action=failure in the Authentication data model. This ensures that other sources, like SSH logs, are similarly standardized. CIM-compliant queries are portable across environments without needing source-specific adjustments, and the use of the datamodel command.

Summary

Converting queries from one language to the other is fairly trivial, but a simple conversion is typically not the best option. Splunk has a wide range of flexible capabilities that can greatly improve on or expand your use case.