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Introduction:
Security operations centers (SOCs) generate thousands of Splunk queries daily, many of which are inefficient, unoptimized, and time-consuming to review manually. By integrating large language models (LLMs) like into your blue team workflow, you can automate query benchmarking, optimization, and validation—turning a tedious review process into a continuous improvement loop. This article walks through Anton Ovrutsky’s “ForBlueTeam” Day 21 technique, showing how to leverage AI to refactor Splunk searches, reduce runtime, and maintain sanity checks.
Learning Objectives:
- Implement an automated workflow to benchmark and optimize existing Splunk queries using ’s API.
- Integrate Obsidian notes as a source of truth for query storage and retrieval.
- Apply iterative testing with before/after performance comparisons to validate optimizations.
You Should Know:
1. Setting Up Your Query Optimization Environment
This section establishes the foundational tools: Splunk (free tier or enterprise), Obsidian (Markdown notes), and API access. You’ll also need Python to glue everything together.
Step‑by‑step guide:
- Install Obsidian and create a vault named
SplunkQueries. Add a folder `Queries/` containing Markdown files—each file holds one Splunk query as plain text. - Get API credentials from Anthropic (console.anthropic.com). Store them as environment variables:
– Linux/macOS: `export ANTHROPIC_API_KEY=’your-key’`
– Windows (Command Prompt): `set ANTHROPIC_API_KEY=your-key`
– Windows (PowerShell): `$env:ANTHROPIC_API_KEY=’your-key’`
3. Install Splunk SDK for Python and the Anthropic library:
pip install splunk-sdk anthropic python-dotenv
4. Create a Python script `query_optimizer.py` that:
- Reads a query from an Obsidian note (parse Markdown file)
- Connects to Splunk using your credentials (Splunk host, port, token)
- Runs the query with `| stats count` (or a small limit) to benchmark execution time
- Sends the query to with a prompt: “Optimize this Splunk SPL query for performance without changing its logic. Return only the optimized query.”
- Runs the optimized query, compares runtime and result counts
- Writes the optimized query back to a new Obsidian note with performance metrics
Example snippet (connection & benchmark):
import splunklib.client as client
import time
service = client.connect(
host='localhost', port=8089,
username='admin', password='yourpass'
)
def benchmark(query, job_args={'exec_mode': 'blocking'}):
start = time.time()
job = service.jobs.create(query, job_args)
job.refresh()
result_count = job['resultCount']
elapsed = time.time() - start
return elapsed, result_count
2. Crafting the Prompt for Splunk Optimization
The quality of optimization depends heavily on prompt engineering. must understand Splunk’s search processing language (SPL) and common performance pitfalls.
Step‑by‑step guide:
- Define constraints in the system prompt: “You are a Splunk performance expert. Only return valid SPL. Do not change search logic or field names.”
2. Provide examples of bad vs. good patterns:
- Avoid `| regex` when `| where match()` works
- Replace `| search` with indexed field searches
- Move filters before `| eval` or `| join`
3. Use this template prompt:
Optimize the following Splunk query for speed and resource usage. Original query: <QUERY> Rules: Keep same output fields and events. Prefer indexed fields, avoid subsearches, replace regex with match() when possible, and push time filters left. Return only the optimized query in a code block.
4. Test with a sample inefficient query (e.g., index=main sourcetype=syslog | regex _raw="error.auth" | eval hour=strftime(_time,"%H")). might suggest `index=main sourcetype=syslog “error” “auth” | where match(_raw,”error.auth”)` or better, `index=main sourcetype=syslog error auth` then filter.
5. Iterate – if ’s output breaks, feed error messages back for correction.
3. Automating the Benchmark-Compare Loop
To create a “sweet iteration loop with sanity checks,” your script must compare both runtime and result set integrity.
Step‑by‑step guide:
- Run original query using `benchmark()` function – capture time and result count.
2. Run ‑optimized query – same benchmarking.
- Compare result counts – if they differ by more than a tolerance (e.g., 0.1% due to event ordering), flag a warning.
- Generate a comparison report – output a Markdown table:
| Metric | Original | Optimized |
|–|-|–|
| Time (s) | 12.4 | 3.2 |
| Result count | 1,234 | 1,234 |
| Improvement | – | 74% faster |
- Optional: Save optimized query – write to a new file `Queries/Optimized_{original_name}.md` with a frontmatter block containing benchmark dates.
- Schedule the script via cron (Linux) or Task Scheduler (Windows) to run weekly on your query library.
Windows Task Scheduler example:
$Action = New-ScheduledTaskAction -Execute "python.exe" -Argument "C:\scripts\query_optimizer.py" $Trigger = New-ScheduledTaskTrigger -Weekly -DaysOfWeek Monday -At 2am Register-ScheduledTask -TaskName "SplunkQueryOptimizer" -Action $Action -Trigger $Trigger
4. Handling API Security and Authentication
When integrating LLM APIs into security workflows, protect credentials and query data. Never send raw logs containing PII or secrets to an external LLM.
Step‑by‑step guide:
- Sanitize queries before sending to – remove any hardcoded credentials, IP addresses, or usernames. Use regex replacement:
import re sanitized = re.sub(r'password="[^"]+"', 'password="REDACTED"', query)
- Use a local or private LLM alternative for sensitive environments (e.g., Ollama with CodeLlama). Modify the script to call a local endpoint.
- Store API keys in a vault – use HashiCorp Vault or Azure Key Vault, then retrieve via REST API. For simplicity, use `.env` files never committed to git.
- Enable audit logging – log every query sent to (sanitized) and every optimization received, including timestamps and user ID.
- Apply network controls – restrict outbound API calls to allowlists only; use a forward proxy with TLS inspection in high‑security SOCs.
5. Extending to Other SIEMs and Query Languages
The same pattern works for Microsoft Sentinel (KQL), Chronicle (YARA-L), or Elastic (EQL). Adapt the prompt and parser.
Step‑by‑step guide for KQL (Azure Data Explorer):
- Modify prompt: “Optimize this KQL query for Azure Sentinel. Prefer `where` over `extend` +
filter, use `materialize()` for repeated subqueries.” - Connect to Azure Log Analytics using the Azure Identity library:
from azure.identity import DefaultAzureCredential from azure.monitor.query import LogsQueryClient
3. Benchmark using `LogsQueryClient.query_workspace()` with `timespan` parameter.
- Compare result sets using row hashes or count.
- Automate via Azure Functions or GitHub Actions for serverless execution.
Linux cron example for weekly runs:
0 3 1 /usr/bin/python3 /home/analyst/splunk_optimizer.py --vault /path/to/queries >> /var/log/query_opt.log 2>&1
6. Mitigating Risks of AI‑Generated Optimizations
While powerful, LLMs can produce syntactically correct but logically flawed queries. Always validate with a second run and a human review.
Step‑by‑step guide:
- Implement a dry‑run mode – script outputs the optimized query but does not replace the original until approved.
- Create a validation diff – show side‑by‑side comparison of original vs. optimized SPL, highlighting changes.
- Run a canary query – use a small time window (e.g., 1 hour) to ensure results match before scaling to full time range.
- Integrate with a change management system – automatically create a Jira or ServiceNow ticket when an optimization exceeds a speedup threshold (e.g., >50% improvement), requiring analyst sign‑off.
- Monitor LLM drift – periodically test with known inefficient queries to ensure optimization quality hasn’t degraded after model updates.
What Undercode Say:
- Key Takeaway 1: LLMs like can safely and effectively optimize Splunk queries when integrated into a benchmark‑compare loop with integrity checks.
- Key Takeaway 2: The real value is not just speed—it’s the automation of a repetitive cognitive task, freeing blue teams to focus on threat hunting.
Analysis: Anton Ovrutsky’s Day 21 technique bridges the gap between generative AI and operational security analytics. By using Obsidian as a lightweight query store and as an optimization engine, SOC analysts can continuously refactor their SPL without manual effort. However, organizations must address data leakage risks, validate result equivalence, and retain human oversight. This approach scales from small teams to large enterprises, especially when combined with scheduled automation. The future will see LLM agents not only optimizing but also proactively rewriting queries based on index changes or data volume shifts.
Prediction: Within 18 months, major SIEM vendors will embed LLM‑based query optimizers directly into their interfaces, moving from “suggestions” to automatic refactoring during execution. Blue teams will shift from writing efficient queries to describing intent in natural language, while AI handles performance tuning. This will democratize advanced threat hunting, but also create new attack surfaces—adversaries may craft queries that poison optimization feedback loops. The arms race will extend to LLM prompt injection within log data, forcing SOCs to sanitize both incoming data and outgoing analysis prompts.
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