Transform your SOC with the APEX AI Copilot — autonomous threat triage, MITRE ATT&CK-mapped detection engineering, real-time IOC enrichment, and case management designed for high-velocity security operations.
The APEX AI Copilot orchestrates 19 specialized security tools via natural language — from threat hunting to SIEM query generation.
Describe a security event in plain English. The APEX AI returns MITRE ATT&CK TTP mapping, threat actor attribution, kill chain analysis, and recommended detection rules.
Submit IPs, domains, file hashes, or URLs. The copilot automatically enriches via VirusTotal, AbuseIPDB, and Shodan, then provides a unified threat verdict with confidence score.
Generate Sigma detection rules, Splunk SPL queries, Elastic EQL, and KQL from natural language descriptions of threats or MITRE ATT&CK technique IDs.
Paste raw log data, alert JSON, or network traffic patterns. The AI triage assistant scores severity, identifies false positive indicators, and generates an initial incident report.
Map observed TTPs to MITRE ATT&CK Enterprise, ICS, or Mobile matrices. Generate attack chain narratives and identify detection coverage gaps in your security stack.
Generate board-ready security briefings from raw threat data — business impact analysis, risk scoring, remediation priorities, and KPI summaries in plain business language.
From alert ingestion to case closure — AI acceleration at every step.
SIEM alerts, threat intel feeds, and IOC hits ingested via webhook or API pull. Auto-normalized to a common schema for consistent AI analysis regardless of source format.
APEX AI Copilot automatically enriches every alert — IOC reputation, CVE context, threat actor attribution, MITRE TTP mapping — and scores severity from 1–10.
High-confidence alerts auto-escalate to cases with severity, type, and analyst assignment. Full audit trail, evidence collection, and timeline reconstruction built in.
Analysts work with full AI context — pre-enriched IOCs, suggested queries, MITRE ATT&CK mapping, and related historical cases — reducing investigation time by up to 80%.
Close cases with detection rule updates (Sigma/Splunk/Elastic auto-generated), false positive tagging, and threat intelligence enrichment fed back into the platform.
The traditional SOC model — analysts watching a SIEM dashboard and triaging thousands of alerts manually — is broken. Security teams face alert fatigue (the average SOC receives 11,000+ alerts per day), analyst burnout (53% SOC analyst turnover annually), and ever-increasing attacker dwell times (average 204 days before detection).
AI-native SOC operations address these challenges at the root cause: automation of repetitive, low-value analyst tasks (alert triage, IOC enrichment, report writing) so analysts can focus on high-judgment investigative work that requires human expertise.
The MITRE ATT&CK framework documents real-world adversary tactics, techniques, and procedures (TTPs) observed in actual attacks. For SOC teams, ATT&CK serves three critical functions: detection engineering (writing detection rules aligned to specific techniques), threat hunting (proactively searching for ATT&CK technique evidence in telemetry), and coverage assessment (identifying which techniques your defenses can detect vs. gaps).
CYBERDUDEBIVASH integrates ATT&CK throughout the SOC workflow — every alert and case is automatically mapped to relevant ATT&CK techniques, detection rules are generated with ATT&CK technique IDs, and coverage reports show your defensive posture across the ATT&CK matrix.
Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) are the primary metrics for SOC operational effectiveness. Industry averages are sobering: MTTD averages 204 days (IBM Cost of a Data Breach 2024), MTTR averages 73 days for containment. AI-augmented SOCs demonstrably reduce both metrics — automated enrichment cuts investigation time by 60–80%, and pre-built playbooks eliminate decision latency in the response phase.
CYBERDUDEBIVASH integrates with all major SIEMs via SIEM export API (POST /api/export/siem). Supported formats: Sigma (universal), Splunk SPL, Elastic EQL, Microsoft Sentinel KQL, QRadar LEEF, CEF, JSON, STIX 2.1. Scheduled export via cron or webhook-triggered on new CVE additions.
Generate production-ready Sigma/KQL/Splunk rules from any CVE — powered by the AI Security Engine
Start with the AI Security Copilot free — APEX God Mode available on Enterprise and MSSP tiers.