Published: March 2026 | Last reviewed: March 22, 2026

Why Detection Engineering is Harder Than It Should Be

Detection engineers know their environment, but intelligence is fragmented across vendor reports, blog posts, and internal tickets. Turning that intelligence into tested, deployable rules is slow and error-prone.

Problem 01

Intelligence-to-Detection Gap

A new threat report drops. You read 15 pages, extract IOCs manually, research the MITRE techniques, then hand-write SPL or KQL. Hours of work for a single detection that may already exist elsewhere.

Problem 02

No Visibility into Coverage Gaps

You have hundreds of rules in production, but which MITRE techniques are actually covered? Which high-severity threats have zero detection? Without a coverage map, you are flying blind on defensive posture.

Problem 03

Format Fragmentation

Your team runs Splunk in one environment and Sentinel in another. Sigma promises portability but conversion is imperfect. You end up maintaining parallel rule sets that drift apart over time.

Problem 04

Detection Debt Accumulates Silently

Every threat without a corresponding detection is debt. Every rule with outdated logic is debt. Without a system to track and score this debt, critical gaps persist for months unnoticed.

Three Rule Formats, One Platform

Every detection in Threadlinqs is available in all three major formats. Filter, compare, and export from a single interface.

SPL
900+

Splunk Processing Language. Ready for savedsearches.conf or the search bar. Includes index, sourcetype, and macro references.

KQL
900+

Kusto Query Language for Microsoft Sentinel. Analytics rule format with table references and time windows.

Sigma
900+

Vendor-agnostic YAML format. Convert to any SIEM backend via sigma-cli. Includes logsource and detection sections.

How Threadlinqs Accelerates Detection Engineering

Detection Library with Advanced Filtering

The detection library is not a dump of rules. It is a structured, searchable collection with multi-select filters across type, severity, confidence, MITRE tactic, index, sourcetype, author, and threat actor. Find exactly the rules you need in seconds.

Filter Sidebar

Multi-Select Filters with Live Count Maps

Each filter group shows real-time counts. Toggle multiple values simultaneously. Results update instantly. AND/OR logic between filter groups for precise queries.

MITRE ATT&CK Coverage Mapping

Every detection is mapped to one or more MITRE ATT&CK techniques. The coverage map visualizes your detection posture across all 14 tactics, highlighting gaps where threats exist but detections do not.

Coverage Analysis

465 Techniques Tracked Across 14 Tactics

Heatmap visualization by tactic density. Expandable technique details showing linked threats and detections. Export coverage reports for compliance and leadership briefings.

Detection Debt Scoring

The advanced correlation engine calculates a detection debt score for every threat. High-severity threats with zero detections surface at the top. You see exactly where to invest your next sprint.

Detection Debt

Prioritized Backlog from Intelligence Data

Split view: uncovered threats sorted by debt score on the left, covered threats on the right. Each entry shows severity, MITRE technique count, IOC count, and days since publication.

Cross-Correlation Engine

Seven correlation engines run in parallel to surface connections invisible in isolation: MITRE heatmap overlap, adversary infrastructure sharing, IOC consensus scoring, CVE velocity tracking, attribution networks, and enrichment health.

Detection Engineering Workflow

From gap identification to rule deployment, all in one session.

det-engineer@threadlinqs
$ tl detection-debt --sort debt_score --limit 5 RANK THREAT_ID SEVERITY MITRE DETECTIONS DEBT 1 TL-2026-0192 CRITICAL 12 0 9.8 2 TL-2026-0188 HIGH 8 1 7.2 3 TL-2026-0175 CRITICAL 6 0 6.9 4 TL-2026-0190 HIGH 9 2 5.4 5 TL-2026-0183 MEDIUM 5 1 4.1   $ tl detections --threat TL-2026-0192 --format sigma No detections found. Generating from threat intelligence...   $ tl export --threat TL-2026-0192 --format all Exported: 3 SPL + 3 KQL + 3 Sigma rules MITRE mapped: T1059.001, T1071.001, T1486 Saved to: ./exports/TL-2026-0192/
2,700+ Detection Rules
3 Rule Formats
465 MITRE Techniques
7 Correlation Engines

Features for Detection Engineers

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SPL rules include index and sourcetype references for Splunk Enterprise and Splunk Cloud. KQL rules reference Microsoft Sentinel tables (SecurityEvent, DeviceProcessEvents, etc.). Sigma rules follow the standard specification and convert via sigma-cli to 30+ backends including QRadar, Elastic, Chronicle, and CrowdStrike LogScale.

Splunk Microsoft Sentinel Elastic SIEM CrowdStrike LogScale QRadar
// author
Threadlinqs Intel Team
Security Engineer at Threadlinqs Intelligence. Researching active threats, building detection rules, and mapping adversary tradecraft across SPL, KQL, and Sigma.
medium.com/@hatim.bakkali10

Close Detection Gaps Faster

Free tier includes the full detection library and MITRE coverage map. Upgrade to copy, export, and access the correlation engine.

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