Security Query Languages: KQL, SPL, and YARA
Part of the Cybersecurity Skills Guide — This article is one deep-dive in our complete guide series.
By HADESS Team | February 28, 2026 | Updated: February 28, 2026 | 5 min read
Security analysts spend most of their time querying data — searching logs for indicators, building detection rules, and hunting for threats. The query language depends on the platform: KQL for Microsoft Sentinel and Defender, SPL for Splunk, and YARA for file and malware analysis. Proficiency in at least one log query language and YARA is expected in any detection-focused role.
KQL (Kusto Query Language)
KQL is the query language for Microsoft Sentinel, Defender for Endpoint, Azure Monitor, and Azure Data Explorer. Its pipe-based syntax reads left to right, making queries relatively intuitive.
Basic structure: start with a table, then pipe through operators.
Find failed logins from external IPs in the last 24 hours: “ SigninLogs | where TimeGenerated > ago(24h) | where ResultType != "0" | where IPAddress !startswith "10." | summarize FailedAttempts = count() by UserPrincipalName, IPAddress | where FailedAttempts > 10 | sort by FailedAttempts desc `
Key KQL operators for security work:
- where
-- filter rows - summarize
-- aggregate data (count, sum, dcount for distinct count) - join
-- combine tables (correlate sign-in events with alert data) - make-series
-- create time series for trend analysis and anomaly detection - parse
-- extract fields from unstructured text - externaldata
-- query external threat intel feeds in CSV/JSON format
KQL has built-in anomaly detection functions like series_decompose_anomalies() that flag statistical outliers in time-series data. This is useful for detecting unusual traffic volumes, login frequency spikes, or data transfer anomalies without manually defining thresholds.
SPL (Search Processing Language)
SPL is Splunk's query language. If your organization runs Splunk Enterprise or Splunk Cloud, SPL is the primary tool for search, analysis, and detection rule creation.
Find processes spawned by Office applications (potential macro execution): ` index=sysmon EventCode=1 | search ParentImage IN ("\\WINWORD.EXE", "\\EXCEL.EXE", "*\\POWERPNT.EXE") | where NOT Image IN ("\\splwow64.exe", "\\DllHost.exe") | stats count by ParentImage, Image, CommandLine, Computer | sort -count `
Key SPL commands:
- search
/where-- filter events - stats
-- aggregate (count, values, dc for distinct count) - eval
-- create calculated fields - transaction
-- group related events by shared fields and time window - lookup
-- enrich events with reference data (threat intel feeds, asset inventory) - tstats
-- high-performance aggregation against indexed fields (much faster than stats for large datasets)
Splunk's datamodel and tstats combination is how you build detections that scale. Instead of searching raw events (slow at terabyte scale), accelerated data models pre-index common fields for fast aggregation.
YARA Rules
YARA identifies and classifies malware based on textual or binary patterns. YARA rules describe patterns that match against file contents, process memory, or network streams. Where KQL and SPL query log data, YARA queries the files and memory themselves.
A basic YARA rule: ` rule Cobalt_Strike_Beacon { meta: author = "Security Team" description = "Detects Cobalt Strike beacon in memory or on disk" severity = "high"
strings: $config = { 00 01 00 01 00 02 ?? ?? 00 02 00 01 00 02 ?? ?? } $sleep_mask = "Sleep mask" ascii wide $beacon_dll = "beacon.dll" ascii $reflective = "ReflectiveLoader" ascii
condition: 2 of them } `
YARA rule components:
- meta: descriptive information (author, description, reference) -- does not affect matching
- strings: text strings, hex patterns (with wildcards ??
and jumps[4-6]`), or regular expressions to match - condition: logical expression defining how strings combine to make a positive match
Use YARA for:
- Scanning files submitted to sandbox analysis
- Scanning endpoint memory through EDR integration (CrowdStrike, Defender support YARA scans)
- Scanning file shares or repositories for sensitive data patterns (PII, credentials)
- Retroactive hunting across collected malware samples
Write rules that match on behavioral patterns rather than easily changed strings. A rule matching on a specific mutex name breaks when the attacker changes one character. A rule matching on a combination of API import patterns, file structure anomalies, and encoded string characteristics is more durable.
Next Steps
- Test your detection engineering skills with the skills assessment
- Explore SIEM, EDR, and threat hunting topics in the skills library
- Use the coaching tool to practice writing detection queries across platforms
Related Guides in This Series
- EDR: Endpoint Detection, Response, and Threat Hunting — HADESS | 2026
- Firewall Management: Rules, Zones, and Change Control — HADESS | 2026
- Hardware Security Modules: Key Management and Compliance — HADESS | 2026
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