prompt

Free

Expert-level prompt for AI-powered threat detection engineering.

FreeFree tier

Type

Open Source

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About prompt

This prompt from the ai-boost/awesome-prompts repository defines a detailed role for a Threat Detection Engineer, designed to be used with AI assistants. It instructs the AI to act as a specialist who builds detection layers to catch attackers after preventive controls fail. The prompt covers core missions including building high-fidelity detections using Sigma rules compiled to SIEM formats (Splunk SPL, Azure Sentinel KQL, Elastic EQL, Chronicle YARA-L), mapping and expanding MITRE ATT&CK coverage, conducting threat hunts, and tuning the detection pipeline to reduce false positives. It emphasizes detection-as-code, adversary-informed design, and quality over quantity in rule deployment. The prompt is structured with actionable methodologies and critical rules for effective security operations.

Key Features

Build high-fidelity detections with Sigma rules compiled to Splunk, Sentinel, Elastic, and Chronicle

Map and expand MITRE ATT&CK coverage across Windows, Linux, Cloud, and Containers

Conduct hypothesis-driven threat hunts using SIEM queries, EDR telemetry, and network metadata

Tune detection pipeline via allowlisting, thresholds, contextual enrichment, and false positive reduction

Detection-as-code workflow: rules versioned in Git, tested in CI, deployed automatically

Adversary-informed design: every detection mapped to at least one ATT&CK technique

Validate detections with atomic red team tests and purple team exercises

Document playbooks for reproducible hunts

Monitor log completeness and normalize new log sources

Prioritize behavioral detections over static IOC matching

Pros & Cons

Pros

Provides a comprehensive, structured methodology for detection engineering
Covers the full detection lifecycle: writing rules, mapping coverage, hunting, and tuning
Actionable details including specific SIEM formats and testing approaches
Emphasizes quality over quantity, reducing alert fatigue
Adversary-informed design ensures relevance to real threats

Cons

Requires an AI model capable of following complex role instructions
Not a standalone tool; needs integration into a chatbot or prompt execution platform
May need customization for organization-specific log sources and environments
Effectiveness depends on the underlying AI model's security knowledge

Best For

Simulating a threat detection engineering role in AI conversationsTraining SOC analysts on detection rule development and tuningAutomating threat hunting workflows with AI assistantsBuilding and iterating on detection rules for enterprise SIEM systemsConducting purple team exercises to validate detection coverageMapping security controls to MITRE ATT&CK for compliance reporting

FAQ

What is detection-as-code?

Detection-as-code means treating detection rules like software: version them in Git, test them in CI pipelines, and deploy them automatically. The prompt specifies that every detection should include a description, ATT&CK mapping, false positive scenarios, and a validation test case.

How does this prompt help with MITRE ATT&CK coverage?

The prompt instructs the AI to assess current coverage against the ATT&CK matrix per platform, identify gaps using threat intelligence, build detection roadmaps to close high-risk technique gaps first, and validate detections via atomic red team tests or purple team exercises.

What is the recommended approach to false positives?

The prompt advises reducing false positive rates through allowlisting, thresholds, and contextual enrichment. It stresses that noisy rules erode SOC trust and suggests removing rules that consistently produce untuned false positives.

Which SIEM formats does this prompt support?

The prompt explicitly mentions writing rules in Sigma (vendor-agnostic) and compiling to Splunk SPL, Microsoft Sentinel KQL, Elastic EQL, and Chronicle YARA-L.