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Introduction:
The modern Security Operations Center (SOC) is drowning in alerts but starving for actionable intelligence. Detection engineers often waste countless hours translating rules between platforms like Microsoft Sentinel, Splunk, and CrowdStrike, while simultaneously struggling to maintain visibility across the MITRE ATT&CK framework. Enter Detections.ai—a community-driven ecosystem that leverages AI to automatically generate, translate, and enrich detection rules, enabling security teams to discover the latest threat hunting queries based on language (KQL, Sigma, SPL, YARA), platform, threat actor, or CVE.
Learning Objectives:
- Understand how to leverage the Detections.ai platform to discover and share detection rules across multiple SIEM, XDR, and EDR environments.
- Master the AI-powered translation and enrichment features that map rules to the MITRE ATT&CK framework and identify CVEs.
- Learn to operationalize community-driven detections for threat hunting, incident response, and proactive defense hardening.
You Should Know:
- The Detections.ai Ecosystem: More Than Just a Rule Repository
Detections.ai is not merely a library; it is a living, breathing community where thousands of detection engineers collaborate to outpace adversaries. The platform aggregates content from GitHub, vendors, and individual contributors, organizing it for seamless searchability. What sets it apart is the AI-powered detection creation feature, which allows users to upload threat intelligence documents (PDF, JSON, Markdown) and instantly generate actionable detection rules.
Step‑by‑Step Guide: Getting Started with Detections.ai
- Access the Platform: Navigate to https://detections.ai/.
- Join the Community: Use an invite code to register. Codes like “KQLWizard” (shared by Steven Lim), “MITRE”, “Network”, or “YARA” provide access to specific groups and content libraries.
- Navigate the Library: Use the search filters to query by Detection Language (e.g., KQL, Sigma, SPL, YARA, S1QL) or Platform (e.g., Microsoft Sentinel, CrowdStrike, Splunk).
- Explore TTP Coverage: Drill down into the MITRE ATT&CK mapping feature to identify gaps in your current coverage. The platform highlights which TTPs are covered by community detections and which are missing.
- Leverage AI Translation: If you find a high-fidelity Sigma rule but use Splunk, use the built-in AI to translate it into SPL instantly.
2. AI-Powered Rule Enrichment and Translation
The core value of Detections.ai lies in its intelligent enrichment layer. Every rule uploaded to the platform undergoes automated analysis. The system performs Automatic MITRE ATT&CK mappings, CVE identification, False positive analysis, and Threat classification & severity scoring. This transforms raw detection logic into contextualized intelligence.
Step‑by‑Step Guide: Using AI to Translate and Enrich Rules
1. Upload a Rule: Navigate to the upload section and paste a detection rule (e.g., a Suricata signature or a YARA rule).
2. Initiate Analysis: The AI engine parses the syntax and enriches it with threat intelligence context.
3. Review the Output: The platform returns the rule with mapped MITRE techniques, relevant CVEs, and a severity score.
4. Translate for Your Stack: Select your target platform (e.g., Microsoft Sentinel) and let the AI convert the rule into the appropriate language (KQL).
5. Export and Deploy: Copy the translated rule directly into your SIEM or EDR console.
Linux Command Example (Suricata Rule Upload):
For network detection engineers, you can upload your Suricata rules directly. Ensure your `.rules` file or ZIP archive is ready for seamless migration.
Example: Packaging Suricata rules for upload zip -r suricata_rules.zip /etc/suricata/rules/.rules Upload the ZIP file via the Detections.ai interface
3. Operationalizing Community Detections for Threat Hunting
The platform excels at translating threat intelligence into huntable hypotheses. For instance, when Microsoft released a blog on “ClickFixing”—a social engineering technique using fake CAPTCHAs to execute PowerShell via the Windows Run dialog—the community rapidly produced detections.
Step‑by‑Step Guide: Hunting for ClickFixing Artifacts
- Search the Platform: Use the search bar for “ClickFixing” to find community-sourced rules across KQL, Sigma, and Splunk SPL.
- Analyze the KQL Rule: Review the Microsoft Defender Advanced Hunting query that looks for suspicious entries in the `RunMRU` registry key (
HKCU\Software\Microsoft\Windows\CurrentVersion\Explorer\RunMRU). - Refine the Query: Exclude noise by filtering out the `MRUList` value, which only tracks order, not the actual malicious command.
- Execute the Hunt: Run the query in your Microsoft 365 Defender portal.
- Investigate Results: Look for encoded PowerShell commands (
powershell.exe -eC) in the registry data strings.
Windows Command Example (Registry Check):
To manually check for this persistence or execution artifact on a Windows endpoint:
Query the RunMRU key for potential malicious entries reg query "HKCU\Software\Microsoft\Windows\CurrentVersion\Explorer\RunMRU"
4. MITRE ATT&CK Coverage Mapping and Gap Analysis
One of the most powerful features is the ability to map your entire detection library against the MITRE ATT&CK framework. This allows security teams to visualize their coverage and identify blind spots in real-time.
Step‑by‑Step Guide: Performing a Coverage Gap Analysis
- Access Library Management: Navigate to the “Library” section of Detections.ai.
- View TTP Coverage: The platform visualizes which techniques (e.g., T1059 – Command and Scripting Interpreter) are covered by your current rules.
- Identify Gaps: See which TTPs have zero coverage.
- Discover Community Rules: Click on a missing TTP to view community-contributed detections that you can import.
- Import and Test: Import the community rule, test it in your environment, and deploy it to close the gap.
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Cross-Platform Rule Translation (Sigma to KQL to SPL)
Sigma is a generic signature format for SIEM systems. Detections.ai simplifies the arduous task of converting Sigma rules into vendor-specific languages.
Step‑by‑Step Guide: Translating a Sigma Rule
- Find a Sigma Rule: Search the Detections.ai library for a Sigma rule targeting, for example, lateral movement (T1021).
- Select Target Format: Choose your target platform—Microsoft Sentinel (KQL) or Splunk (SPL).
- Initiate Translation: Use the “Translate” function to let the AI handle the syntax conversion.
- Review the Translation: Verify the logic and field mappings (e.g., mapping `EventID` to `EventID` in Windows logs, or `Image` to
Process.CommandLine). - Deploy: Copy the resulting KQL or SPL query into your SIEM.
KQL Example (Lateral Movement Detection):
Here is an example of a KQL query that might be generated to detect lateral movement via WMI:
// Detect WMI Process Creation for Lateral Movement (T1047) DeviceProcessEvents | where ProcessCommandLine contains "wmic" and ProcessCommandLine contains "process call create" | where InitiatingProcessAccountName != "SYSTEM" | project Timestamp, DeviceName, InitiatingProcessAccountName, ProcessCommandLine, InitiatingProcessId
6. Cloud Hardening and API Security Detections
Beyond endpoint and network detections, the community focuses heavily on cloud security. Detections.ai hosts rules for identifying misconfigurations and API abuse in cloud environments.
Step‑by‑Step Guide: Hunting for Cloud Misconfigurations
- Filter by Cloud Tags: Use the platform filters to search for “AWS”, “Azure”, or “GCP” specific detections.
- Review IAM Rules: Look for rules that detect privilege escalation or unusual API calls (e.g.,
iam:CreateAccessKey). - Analyze the Logic: Understand the detection logic, which often correlates CloudTrail or Azure Activity logs.
- Implement in SIEM: Translate the rule to your cloud-1ative SIEM (e.g., Azure Sentinel) and enable automated alerts.
- Remediate: Use the rule’s guidance to enforce least-privilege access and remediate misconfigurations.
Linux Command Example (AWS CLI Check for Suspicious Keys):
To manually audit for recently created access keys in your AWS environment:
List IAM users and check for key age aws iam list-users --query 'Users[].UserName' --output text | while read user; do aws iam list-access-keys --user-1ame $user --query 'AccessKeyMetadata[?CreateDate>=<code>2026-01-01</code>]' done
7. Vulnerability Exploitation and Mitigation (CVE Correlation)
The platform’s AI enrichment automatically identifies CVEs associated with detection rules. This allows SOC teams to prioritize detections based on the severity of the vulnerability being targeted.
Step‑by‑Step Guide: Prioritizing CVEs with Detections
- Search by CVE: Use the search bar to look for a specific CVE (e.g., CVE-2024-12345).
- View Associated Detections: The platform returns a list of detection rules that cover the exploitation attempt.
- Assess Severity: Review the AI-generated severity scoring to prioritize response.
- Deploy Mitigation: Implement the detection rule in your EDR/SIEM.
- Hunt for Activity: Use the rule to hunt for evidence of exploitation in your historical logs.
What Undercode Say:
- Key Takeaway 1: The fusion of community intelligence and AI automation is the future of detection engineering. Platforms like Detections.ai eliminate the manual, error-prone process of rule translation, allowing analysts to focus on hunting rather than formatting.
- Key Takeaway 2: The MITRE ATT&CK mapping and gap analysis feature is a game-changer for SOC maturity. It enables data-driven decisions on where to invest detection engineering resources, ensuring coverage aligns with the current threat landscape.
Analysis: The Detections.ai model addresses a critical pain point in cybersecurity: the fragmentation of detection languages. By providing a unified interface for KQL, Sigma, SPL, and YARA, it breaks down silos between teams using different vendors. The platform’s reliance on community contributions ensures that the library remains current with emerging threats, while the AI layer ensures that these contributions are immediately actionable and contextually rich. The recent partnership with Suricata highlights the platform’s commitment to bridging the network detection gap, often overlooked in favor of endpoint-focused tools. Ultimately, this approach democratizes high-quality detection engineering, making enterprise-grade threat hunting accessible to organizations of all sizes.
Prediction:
- +1 The democratization of detection engineering through AI and community sharing will significantly reduce the average time-to-detect (TTD) for emerging threats, as rules can be shared and deployed globally within hours of a new attack being discovered.
- -1 The reliance on community-contributed rules introduces a risk of false positives or incomplete logic if not properly vetted. Organizations must maintain rigorous testing and validation pipelines before deploying community-sourced detections into production environments.
- +1 As the platform scales and integrates with more vendor-specific APIs, we will likely see the emergence of automated response playbooks that trigger based on community-validated detections, moving towards true autonomous security operations.
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