Local 27B LLM for Splunk + Sysmon: What It Found—and What It Got Wrong

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A local 27B language model helped audit and repair one home Splunk-and-Sysmon setup—but the account also includes false positives, a failed fix, and work left unresolved. The result is a useful case study in how an LLM can assist with security-stack maintenance, not evidence that a model can reliably run a SOC or fix any installation on its own.

What the local LLM experiment involved

A security analyst studying for CySA+ used a quantized Qwen3.8-27B model to examine a home SOC stack: Splunk under a dev license, Sysmon collecting Windows endpoint telemetry, and an agent sending work to the model through llama-server. The reported configuration used IQ2_M quantization, a 128K context window, and one 16 GB GPU.

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The author describes the workflow as local, with no third-party data or borrowed infrastructure involved. That is a claim about this particular setup, not an independently measured guarantee that every local-model installation keeps all data on-device. Network access, application behavior, logging, and other components still matter.

This was a first-person lab account, not a controlled evaluation. The configuration and outcomes below are the author’s reported experience; they do not establish that the same model, context length, or results will work on other hardware or in a production SOC.

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What the model audited and found

The author ran five types of audit: configuration inspection, data-pipeline review, logging hardening, analysis of 24 hours of logs, and indicator-of-compromise detection. The account reports ten findings in total. Specific examples included duplicate log ingestion, disabled Windows event channels, a performance-counter issue involving a missing parameter and a Spanish-language Windows installation, and a Sysmon configuration that did not detect access to LSASS.

Some issues were corrected; at least one binary debugging problem remained unresolved. The reported total is a result from this lab, not a measure of expected findings from another Splunk or Sysmon deployment.

Two findings needed human skepticism

• One audit said an event channel was disabled after checking the wrong channel. The channel was enabled and recording events.
• Another rule treated processes accessing temporary files under a user profile as suspicious, which included ordinary Electron applications and the author’s own agent.

The account says the model later caught and corrected those assumptions. That is a reason to check the evidence behind a finding—not proof that an LLM will consistently identify or repair its own mistakes.

Why Sysmon still needs configuration and a SIEM

Sysmon records Windows system activity in the Event Log; it does not interpret that activity as an alert or block it. Microsoft Learn puts it plainly: “Sysmon records system activity and writes events to the Windows Event Log,” but “Sysmon doesn’t analyze events or generate alerts” and “Sysmon doesn’t block or prevent activity.” A SIEM such as Splunk can analyze collected events downstream, but it can only work with the data that reaches it.

Configuration determines which events Sysmon records and therefore affects both visibility and volume. Microsoft documents XML configuration for event types and include/exclude filters, including Process Create, Network Connect, and File Create. It advises reviewing and testing configuration before broad deployment. Splunk’s Sysmon add-on guidance likewise recommends tailoring filters to the security team’s needs: an uncustomized setup may capture too little useful data or send excessive events into the Event Log and Splunk.

Verify collection before treating an absence as evidence

1. Inspect the configuration. Review the deployed Sysmon XML and confirm that the event types and filters cover the activity the audit is meant to examine.
2. Check the source events. In Event Viewer, inspect Applications and Services Logs > Microsoft > Windows > Sysmon > Operational. Confirm that expected events are actually present before concluding that an activity did not occur.
3. Check the ingestion path. Confirm that the relevant Operational log events are reaching Splunk and that duplicate ingestion is not inflating counts or obscuring analysis. Splunk describes collecting Sysmon events from that log; Windows Event Forwarding/Windows Event Collector is another possible route that also needs careful tuning.
4. Test configuration changes. Microsoft documents applying a configuration with sysmon -c <configfile>; changes take effect without a restart. Validate the resulting events rather than assuming that a successful command proves the intended telemetry is being collected.

The author’s report of a missed LSASS-access event illustrates the practical consequence of a gap in telemetry: a downstream audit cannot find an event that the active configuration never records.

Audit work and remediation work need different boundaries

The experiment exposed a useful distinction: an audit should gather evidence broadly, while a repair should target a specific, evidenced root cause. The author’s working rules asked the agent to be exhaustive during audits but to stop remediation once it reached a root cause supported by evidence.

The account says the model proposed a configuration change that did not fix a binary that continued to fail. Rather than claim success, the report left the issue as requiring further debugging. That is the right kind of unresolved result: a suggested edit is not proof of a repair.

Keep inspection separate from changes to system state

• Read-only inspection: collect configuration, logs, and relevant system details without changing the installation.
• Proposed edits: show the exact intended change and the evidence that motivates it; retain the original configuration so it can be restored.
• State-changing actions: require a human to review the target and consequences before running commands, uninstalling software, or applying configuration changes.

The author describes carefully distinguishing a removable Universal Forwarder from Splunk itself, which held the lab data, during an MSI-uninstall scenario. That example underlines why an agent should verify what a command targets before performing an irreversible or disruptive operation.

Mechanical edits are also a poor use of a model’s reasoning context. The author recommends scripts for repeatable operations and explicit checks of technical identifiers rather than relying on a generated value or a confident-sounding explanation.

Context limits and prompt rules shaped the reported result

In one run, the author reports that the model used 97.4% of its 128K context window while investigating an irrelevant detail. In a later run, after lowering temperature from 0.8 to 0.3 and adding working rules, reported context use was 49.7%. The author also reports four findings in a row in the later run, compared with one finding in 30 minutes earlier. These are observations from the same lab account; they do not isolate the effect of temperature or establish a general performance improvement.

The rules included answering only the requested question, not inventing facts, checking identifiers before asserting them, and using scripts for mechanical work. One especially useful instruction was to stop a fix investigation when the root cause could not be supported by evidence. These rules can make an agent’s work easier to inspect, but they cannot guarantee correct output.

Local inference is not the same as prompt-level audit logging

Running inference locally can reduce reliance on an external model service, but it does not automatically create an audit trail of prompts and responses. Splunk’s October 31, 2025 article about its Ollama technology add-on says recent standard Ollama versions no longer log individual prompt and response contents in server logs. An organization that needs prompt-level records must implement application-layer logging and confirm behavior for the specific deployed version.

Splunk documents a separate local-model route through its Data Science and Deep Learning app: pull a model from Ollama in the LLM management interface, then use the Standalone LLM dashboard to run inference on text stored in Splunk. That is a documented integration option; it was not the integration used in the reported experiment.

What this case study does—and does not—show

The experiment shows that a locally served model and agent can help inspect a particular Splunk-and-Sysmon lab, surface configuration and ingestion issues, and assist with some fixes when a human checks the evidence. It also shows why findings need validation: the audits included a wrong-channel false positive, an overbroad process rule, context spent on an irrelevant detail, and a fix that did not resolve a binary problem.

It does not establish a universal hardware requirement for running a 27B model, a reliable rate of successful SOC findings, or that a local model can safely make production changes without oversight. The reported 16 GB GPU, quantization, context configuration, server, and agent are one environment—not a compatibility guarantee or benchmark.

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