SIEM Optimization Services for Enterprises

How We Work

Integration fails at the schema, not the connector.

Security domains do not fail to talk to each other because the tools lack connectors. They fail because each domain’s telemetry lands in a different schema, at a different fidelity, with a different notion of identity, so a correlation that is obvious to an analyst is not expressible as a query. The integration work that matters is the normalization contract.

So every source publishes into one normalized security data plane, and detection, hunting, response automation, and compliance evidence are all built against that plane rather than against each source tool.

Detection outlives the tool.

Detection content is authored against the normalized plane and against MITRE ATT&CK techniques, so a detection survives a platform replacement. Tuning is an engineering deliverable with a governance trail, not an analyst’s local adjustment that disappears with the analyst.

Identity is the correlation key.

Authentication, entitlement-change, and privileged-session events belong in the same plane as everything else. That is what makes identity-correlated detection and insider-threat analytics possible at all. In hunting we prioritize T1078 and T1556, because valid accounts and modified authentication are how a capable adversary stops looking like an intrusion and starts looking like a user.

A detection with no response path is an alert you will tune away within a quarter.

Before a detection reaches production we define its automated response path, its integration points, its decision branches, and its escalation conditions. Detection first and automation retrofitted later is the common failure, and the retrofit is rarely funded.

Three rules govern what we automate:

• Automate the deterministic, escalate the ambiguous. An automated action on an ambiguous signal is an outage waiting for a maintenance window. Confidence thresholds are explicit inputs, not assumptions buried in a rule.
• Every automated action is reversible, or is logged as irreversible. Containment that cannot be undone escalates to a human by design, not by exception.
• Measure the manual work removed. Reduction is only measurable if the baseline was captured before the automation, which makes baselining a first-phase deliverable rather than an afterthought.

Evidence is a by-product, not a project.

Control monitoring and compliance evidence are generated from the same data plane as detection, rather than assembled separately at assessment time. That is the difference between continuous compliance and an annual reconstruction.

On AI: we orchestrate commercial models. We do not train them on your data.

Large language models draft parsers and detection logic from log samples and technique descriptions, and assemble investigation context so an analyst starts with the picture instead of building it across consoles. A human engineer reviews and owns every artifact that reaches production.

We deploy and tune the behavioral analytics that ship inside SIEM, EDR, and identity platforms. What we do not do is train, build, or fine-tune models, and we do not put customer telemetry into a training pipeline. Those are different disciplines with different data-governance and model-risk obligations, and the distinction is the one most often blurred.

The knowledge lives in the repository.

Normalization contracts, detection content, and orchestration playbooks live in version control with the rationale recorded beside each. That makes the work inspectable at any moment, and it means the operational knowledge sits in the repository rather than in one person’s head.

Case Study: Stand-Up and Optimization of Microsoft Sentinel on GCCH for U.S. Government Compliance

Over a six-month engagement in 2025–2026, SIEMtune architected and optimized Microsoft Sentinel from the ground up within the Microsoft Cloud for US Government (GCC High / GCCH) environment for a U.S.-based organization requiring FedRAMP High and CMMC 2.0 alignment. This deployment integrated Microsoft Defender for Cloud and other native sources to deliver secure, scalable threat detection and response capabilities tailored to government workloads.

Key accomplishments included:

• Configuration and validation of 19 out-of-the-box data connectors compatible with GCC High, ensuring seamless ingestion from Azure services, Microsoft 365 GCC High, and Defender products while maintaining sovereign cloud boundaries.
• Development of 20 custom data pipelines covering endpoint collection, ingestion, transformation, table normalization, and delivery to Sentinel Log Analytics for observation via custom content and advanced hunting queries.
• Implementation and optimization of 284 analytic rules and 610 hunting queries, tuned for low false-positive rates, high fidelity, and alignment with MITRE ATT&CK techniques relevant to government threats.
• Assistance in building and integrating custom Machine Learning (ML) and programmatic analysis models leveraging Microsoft Graph Security APIs for deep, automated analysis of Azure logging data.
• Significant contributions to achieving and maintaining CMMC 2.0 compliance across three separate enterprises, including mapping controls to Sentinel analytics, workbooks, and alerting mechanisms.
• Execution of over 445 targeted SOC optimization steps, encompassing rule tuning, playbook automation, alert grouping, and incident response workflow refinement.

These efforts resulted in a production-ready Sentinel deployment capable of ingesting and analyzing high-volume government logging with minimal noise, enabling proactive threat hunting and rapid incident response in a compliant environment. The project earned internal recognition for exceptional delivery and was awarded a Microsoft Cybersecurity Scholarship in acknowledgment of technical excellence and impact on secure cloud adoption.

Cross-Platform and Compliance Integration

This Sentinel stand-up leveraged native integrations with Microsoft Defender for Cloud and Defender XDR (available in GCC High/DoD clouds) to centralize alerts and incidents. Custom content ensured full coverage for CMMC 2.0 Level 2 requirements, including audit logging (AU family), incident response (IR family), and system monitoring (SI family). SIEMtune’s approach emphasized secure-by-design configurations, such as managed identities for Azure Functions and least-privilege access, while operationalizing detections across hybrid and multi-cloud workloads.

Example: Optimized Hunting Query for Anomalous Azure Activity

// Sample KQL Hunting Query (GCC High compatible)
// Detect anomalous sign-ins or resource changes in Azure Government
SigninLogs
| where TimeGenerated > ago(7d)
| where ResultType == 0  // Successful sign-ins
| where IPAddress !in (known_ip_list)  // Replace with allowlist
| summarize Count = count() by UserPrincipalName, IPAddress, Location
| where Count > 50  // Threshold for anomaly
| project TimeGenerated, UserPrincipalName, IPAddress, Location, Count
| order by Count desc

This query style, combined with custom analytic rules, enabled early detection of insider threats and misconfigurations while supporting ongoing compliance monitoring. SIEMtune provided full documentation and training to empower the client’s SOC team for sustained self-sufficiency.

Case Study: Devo Gov Cloud Deployment for a U.S. Cybersecurity Company

In 2024, we deployed Devo Gov Cloud for a U.S. cybersecurity firm, integrating Workday, Okta, Windows Lab, Palo Alto firewalls, AWS, Azure, O365, Salesforce, Wiz, Infoblox, and more. Leveraging Cribl and alert tuning, we substantially reduced data ingestion costs and improved threat detection fidelity while maintaining CMMC 2.0 compliance.

Our strategy included cloning and reprioritizing dropped alerts, SOAR-based entity grouping, and role-based monitoring with daily O365 admin reports instead of analyst floods.

Cross-Platform Detection with Sigma Rules

We utilize Sigma rules for portable, high-fidelity detections across Microsoft Azure Sentinel Defender, Splunk, Elastic, QRadar, Devo, CrowdStrike Falcon, and others, mapped to MITRE ATT&CK techniques (e.g., T1059.001, T1574, T1218). We operationalize rules with threshold tuning, false-positive reduction, Cribl Stream, and SOAR, ensuring actionable alerts.

This framework supports CMMC 2.0 compliance, proactive threat hunting, and SOC self-sufficiency through training and documentation.

Example: Multi-SIEM Translation for Process Hijacking Detection

# Splunk
CurrentDirectory="C:\ProgramData\huo" Image IN ("*isp.exe", "*SmdHidLib.DLL", "*pas.ps1")
# Microsoft XDR
CurrentDirectory="C:\ProgramData\huo" AND (Image endswith "isp.exe" OR Image endswith "SmdHidLib.DLL" OR Image endswith "pas.ps1")

Single Sigma logic adapts seamlessly across platforms, with support for SentinelOne, ArcSight, NetWitness, Logpoint, and more.