Aegis-Mind: An Automated SOC Alert-Triage and Threat-Intelligence Enrichment Architecture with Cryptographic Webhook Verification

Modern Security Operations Centers (SOCs) face severe alert fatigue caused by high volumes of repetitive internal network telemetry and unverified external indicators of compromise (IoCs). Manual triage of IP addresses, domains, and binary hashes across threat-intelligence feeds increases Mean Time to Respond (MTTR) and analyst cognitive load. This paper presents Aegis-Mind, an automated SOC alert-triage and threat-intelligence enrichment system built in Python and Flask. The architecture enforces cryptographic webhook authenticity via HMAC-SHA256 signature verification, performs deterministic pre-filtering of RFC 1918 private IPv4 address spaces to eliminate internal non-routable noise, and integrates the VirusTotal v3 API with a local caching mechanism to enrich suspicious IP addresses, domains, and SHA-256 file hashes without redundant network lookups or API rate-limit exhaustion. Finally, Aegis-Mind automatically maps enriched security events to MITRE ATT&CK tactics and techniques and dispatches structured, real-time incident notifications to Slack and Discord channels. Experimental evaluation demonstrates significant reductions in false-positive alert volume, external API query overhead, and tier-1 incident triage latency.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-06
DOI
https://doi.org/10.5281/zenodo.23996109
Primary Topic
Network Security and Intrusion Detection
Type
preprint
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preprint

Aegis-Mind: An Automated SOC Alert-Triage and Threat-Intelligence Enrichment Architecture with Cryptographic Webhook Verification

Kishan Ambaliya
3 links
Zenodo (CERN European Organization for Nuclear Research)
Network Security and Intrusion Detection
preprint

Aegis-Mind: An Automated SOC Alert-Triage and Threat-Intelligence Enrichment Architecture with Cryptographic Webhook Verification

Kishan Ambaliya
preprint en

Abstract

Modern Security Operations Centers (SOCs) face severe alert fatigue caused by high volumes of repetitive internal network telemetry and unverified external indicators of compromise (IoCs). Manual triage of IP addresses, domains, and binary hashes across threat-intelligence feeds increases Mean Time to Respond (MTTR) and analyst cognitive load. This paper presents Aegis-Mind, an automated SOC alert-triage and threat-intelligence enrichment system built in Python and Flask. The architecture enforces cryptographic webhook authenticity via HMAC-SHA256 signature verification, performs deterministic pre-filtering of RFC 1918 private IPv4 address spaces to eliminate internal non-routable noise, and integrates the VirusTotal v3 API with a local caching mechanism to enrich suspicious IP addresses, domains, and SHA-256 file hashes without redundant network lookups or API rate-limit exhaustion. Finally, Aegis-Mind automatically maps enriched security events to MITRE ATT&CK tactics and techniques and dispatches structured, real-time incident notifications to Slack and Discord channels. Experimental evaluation demonstrates significant reductions in false-positive alert volume, external API query overhead, and tier-1 incident triage latency.

Zenodo (CERN European Organization for Nuclear Research)
Parul University (IN)
Network Security and Intrusion Detection
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