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Introduction:
Detection engineering is no longer about writing a single Sigma rule or tuning a Splunk alert. Modern enterprise environments demand reasoning across seven different SIEM platforms, 120+ log sources, and real-world constraints like broken parsers, schema drift, and incorrect threat intelligence. DetectBench, a new benchmark from Spectrum Security, challenges AI systems and human engineers alike with 1,000+ tasks spanning easy to expert difficulty—measuring not just whether a detection works, but whether it works under the messy, noisy conditions of actual production.
Learning Objectives:
- Understand the four difficulty levels and core skills tested by DetectBench (threat research, log analysis, detection strategy, SIEM nuances, tuning, validation, statistics, evidence collection).
- Learn how to query and analyze logs across Elastic, Splunk, Microsoft Sentinel, and Google SecOps using platform-specific commands and KQL/SPL.
- Build and test detection rules using Sigma, RSigma, and Atomic Red Team, including CI/CD secret-harvesting scenarios with token-type differentiation.
You Should Know:
- Deconstructing DetectBench: From Easy to Expert—What Each Level Tests
DetectBench organizes detection engineering into four difficulty tiers. Easy tasks validate basic log presence and simple correlation. Medium tasks introduce false positives and log structure variations. Hard tasks require understanding license tiers, SaaS vs. self-managed, and token types (user access token, job token, deploy token, runner token). Expert tasks involve broken telemetry, parser failures, or detection impossibilities where the answer is “you cannot detect this with current logs.”
Step‑by‑step guide to assessing your own detection coverage:
- Inventory your SIEM and log sources: Run the following commands to list all ingested log types in a Linux-based SIEM forwarder:
List all log directories and their sizes ls -lah /var/log/ | awk '{print $9, $5}' Check syslog for ingestion errors journalctl -u rsyslog --since "24 hours ago" | grep -i error -
Map a realistic attack scenario (e.g., CI/CD secret harvesting) to required logs:
Windows: Check for Git credential access events Get-WinEvent -LogName "Security" | Where-Object { $_.Id -in 4663,4656 } | Select-Object TimeCreated, Message -
Identify coverage gaps using DetectBench’s logic: If your environment uses GitLab SaaS with audit logs disabled, certain token-based attacks become invisible. Document these as “deployment blockers.”
-
Querying Across Seven SIEMs: Essential Commands for Detection Engineers
Each SIEM in DetectBench requires distinct query syntax. Mastering these is non‑negotiable for passing hard‑level tasks.
Elastic (KQL-based):
// Find failed SSH logins with malformed username event.dataset: "ssh.login" and event.outcome: "failure" and user.name: "" | where strlen(user.name) > 30 | stats count by host.name, user.name
Splunk (SPL):
index=linux_secure fail sshd | rex "Failed password for (?<invalid_user>[^ ]+)" | where len(invalid_user) > 30 | table _time, host, invalid_user
Microsoft Sentinel (KQL):
// Detect CI/CD job token misuse from runner logs GitLabAuditLog | where OperationName contains "job_token" | extend TokenType = tostring(parse_json(Properties).token_type) | where TokenType == "job_token" and Result == "success" | project TimeGenerated, ProjectName, UserAgent
Google SecOps (YARA-L 2.0):
rule CICD_SecretHarvest {
meta:
author = "Detection Engineer"
events:
$git.event_type = "git.fetch"
$audit.token_type in ("job_token", "deploy_token")
condition:
$git and $audit
}
Testing your queries: Use `curl` to send test events to Elastic’s bulk API:
curl -X POST "localhost:9200/_bulk" -H "Content-Type: application/json" --data-binary @test_events.json
- Log Analysis with Linux/Windows Command Line: Parsing Broken Telemetry
DetectBench’s “hard” tasks include malformed logs and schema drift. Use these commands to validate log structure before writing detection rules.
Linux – Extract and validate JSON logs:
Check for malformed JSON in application logs cat /var/log/app.log | jq -c 'select(. == null)' 2>/dev/null || echo "Malformed JSON found" Count unique field names across 10,000 log lines head -10000 /var/log/app.log | jq 'keys' | sort | uniq -c | sort -nr
Windows – Parse XML event logs for missing attributes:
Find events where the 'TargetUserName' attribute is empty (schema drift)
Get-WinEvent -FilterHashtable @{LogName='Security'; ID=4624} | ForEach-Object {
$xml = [bash]$<em>.ToXml()
$user = $xml.Event.EventData.Data | Where-Object {$</em>.Name -eq 'TargetUserName'} | Select-Object -ExpandProperty 'text'
if ([bash]::IsNullOrWhiteSpace($user)) { $_ }
}
Simulating parser failure (for testing): Use `sed` to remove a critical field from a log sample:
sed 's/"user_name":"[^"]"/"user_name":null/' clean.log > broken.log
- Writing and Tuning Detection Rules with Sigma and RSigma
Sigma is the lingua franca of detection engineering. DetectBench’s rubric penalizes false negatives caused by improper tuning.
Step‑by‑step guide to creating a Sigma rule for CI/CD token abuse:
1. Write the rule (`cicd_token_abuse.yml`):
title: CI/CD Job Token Used from Unusual IP status: experimental logsource: product: gitlab service: audit detection: selection: token_type: "job_token" geoip.country_code: - "RU" - "CN" - "KP" condition: selection falsepositives: - Legitimate cross-region runners level: medium
2. Convert to SIEM-specific formats using `sigmac`:
Install sigma-cli pip install sigma-cli Convert to Splunk sigma convert -t splunk cicd_token_abuse.yml -o rule_splunk.conf Convert to Elastic Lucene sigma convert -t lucene cicd_token_abuse.yml
3. Validate with RSigma (Rust-native Sigma compiler):
git clone https://github.com/timescale/rsigma cd rsigma cargo run -- convert -t splunk ../cicd_token_abuse.yml
Tuning to avoid false positives: Use `stats` to find the baseline frequency of job token usage across your environment:
index=gitlab token_type="job_token" | timechart count by user_id span=1h | eventstats avg(count) as avg, stdev(count) as stdev | where count > avg + (3stdev)
5. CI/CD Secret Harvesting: The Hardest DetectBench Category
One of DetectBench’s most challenging tasks asks: “Given SaaS vs. self-managed, license tier, and token types, is secret harvesting observable?” Here’s how to build a detection strategy that won’t alert every developer.
Step‑by‑step guide to token‑aware detection:
- Identify all token types in your CI/CD logs (GitLab example):
-- PostgreSQL query on audit log table SELECT DISTINCT token_type, COUNT() FROM gitlab_audit_logs WHERE created_at > NOW() - INTERVAL '7 days' GROUP BY token_type;
-
Create a detection matrix for each token’s observability:
– User access token: Logs user_id, IP, scopes → high observability
– Job token: Short-lived, often missing source IP → medium
– Deploy token: No user association → low observability (coverage gap)
- Implement a multi-stage detection rule in Microsoft Sentinel (KQL):
// Stage 1: Detect multiple token types from same runner let suspicious_runners = GitLabAuditLog | where TimeGenerated > ago(1h) | summarize TokenTypes = make_set(token_type) by runner_id | where array_length(TokenTypes) > 2; // Stage 2: Cross-check with artifact downloads GitLabAuditLog | where runner_id in (suspicious_runners) | where OperationName contains "artifact" | extend IsHarvesting = iif(ResponseCode == 200, true, false) | project TimeGenerated, runner_id, IsHarvesting
-
If detection is impossible (e.g., deploy tokens in free tier SaaS), implement a compensating control: enforce mandatory token prefix scanning via `grep` in pre-receive hooks:
Server-side Git hook to block secrets if grep -rE "(glpat-|glptt-|gldt-)" "$PWD"; then echo "ERROR: CI/CD token detected in commit" >&2 exit 1 fi
-
Benchmarking AI for Detection Engineering: Lessons from Harvey & DetectBench
The Harvey Legal Agent Benchmark (https://www.harvey.ai/blog/introducing-harveys-legal-agent-benchmark) inspired DetectBench’s outcome‑based scoring. Unlike general AI benchmarks, these measure whether an agent can achieve a specific detection outcome under real constraints (time, cost, noisy data).
Step‑by‑step guide to evaluating an AI detection agent:
- Define a pass/fail rubric for a hard‑level task:
– Pass: Agent identifies the log source mismatch and recommends a configuration change.
– Fail: Agent writes a detection rule that doesn’t account for missing verbosity.
- Use a controlled test environment with pre‑broken telemetry (e.g., Cribl or Vector):
Simulate schema drift with Vector remap [transforms.drift] type = "remap" inputs = ["raw_logs"] source = ''' if random(0.2) < 0.05 { .user = null Simulate missing field } ''' -
Run the AI agent against a subset of DetectBench tasks using the provided environment (contact Spectrum Security for access). Score each attempt against the published ground truth rubric.
-
Mitigation and Hardening Strategies Based on DetectBench Gaps
When a detection task reveals a coverage gap (e.g., “you cannot detect this with current logs”), the correct answer is not a rule—it’s a deployment fix.
Step‑by‑step hardening guide:
1. Enable verbose logging on critical SaaS applications:
Google Workspace: Enable audit logs for all token events via API gcloud services enable logging.googleapis.com gcloud logging sinks create gsuite-audit storage.googleapis.com/gsuite-bucket --include-children
- Fix parser breaks by validating log schemas daily:
Python script to validate log fields against expected schema import json, sys expected_fields = {"user", "action", "src_ip", "token_type"} with open(sys.argv[bash]) as f: for line in f: obj = json.loads(line) missing = expected_fields - set(obj.keys()) if missing: print(f"Schema drift: missing {missing}", file=sys.stderr) -
Implement detection engineering CI/CD with rsyslog or Fluentd to test rules before production:
Test a new Sigma rule against historical logs docker run -v $(pwd):/rules sigma/sigma-cli check /rules/cicd_token_abuse.yml Deploy only if no false positives exceed 2% on 7‑day sample
What Undercode Say:
- Key Takeaway 1: DetectBench proves that real‑world detection engineering requires far more than alert writing—it demands deep understanding of log provenance, parser health, token semantics, and deployment architecture. The hardest tasks aren’t about exploits; they’re about knowing when detection is mathematically impossible with available data.
- Key Takeaway 2: AI agents that pass DetectBench will need to reason across seven SIEM dialects, handle malformed telemetry, and make trade‑offs between false positives and coverage—skills that currently separate junior analysts from senior engineers. The benchmark’s pass/fail rubric, inspired by legal AI benchmarks, is a template for how to evaluate agentic security tools.
Analysis: The LinkedIn discussion highlights a growing consensus: general AI benchmarks are useless for cybersecurity. DetectBench’s focus on “noisy, drifting enterprise environments” mirrors the daily reality of SOC teams. The mention of RSigma (Rust Sigma compiler) and the Harvey legal benchmark shows cross‑industry convergence on outcome‑based, environment‑aware testing. For detection engineers, this means your skillset is shifting from rule authoring to log system design. For AI security startups, passing DetectBench will become a market differentiator—similar to how SABSA or MITRE ATT&CK evaluations are used today.
Prediction:
Within 18 months, organizations will begin requiring SIEM vendors to publish DetectBench scores as part of procurement RFPs. Detection engineering roles will split into two tracks: “Detection Rule Writers” (junior) and “Detection Architects” (senior) who can diagnose log source gaps, tune parsers, and choose between SaaS vs. self-managed based on observability. AI agents that achieve “Expert” level on DetectBench will be deployed as autonomous pre‑filtering layers in SOCs, but human engineers will still be needed for the tasks where the correct answer is “you cannot detect this”—a conclusion that requires judgment no current LLM reliably possesses. Expect the RSigma project to evolve into a full benchmark harness, and watch for DetectBench‑compatible training courses within 12 months.
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