Sigma

Security Vision SIEM Tackles Alert Overload with Data Quality Monitoring and MITRE ATT&CK Coverage

The article examines persistent SIEM challenges illustrated by the 2013 Target breach, where FireEye detected the compromise but response processes failed. It cites recent statistics showing organizations receive nearly 3,000 daily alerts with 46 percent false positives and 63 percent left uninvestigated. Security Vision SIEM, built on the Low-Code Security Vision 5 platform, integrates event collection, normalization, correlation, asset context, and basic response. Key July 2026 updates add collection stability monitoring, SLA dashboards, statistical anomaly detection, Sigma rule exchange, and retrospective process chain reconstruction. The product offers more than 1,200 correlation rules covering over 70 percent of MITRE ATT&CK techniques along with FSTEC BDU mappings and expert response recommendations. It supports remote and agent-based collection across 150 integrations while providing visual No-Code tools for connectors, rules, and dashboards.

Security Vision SIEM Adds Monitoring for Missing Logs, Correlation Quality, and SOC SLA Compliance

Security Vision has released a major update to its SIEM platform that extends monitoring beyond external threats to the health of the data collection pipeline itself. The new release introduces continuous checks for source stability, allowing administrators to define acceptable event flow deviations and receive alerts when logs suddenly stop arriving. A dedicated dashboard now evaluates correlation rule performance through testing on simulated events and supports import/export in Sigma format for easier detection sharing across platforms. The StatAnalyser service applies statistical models to flag atypical behavior with special markers, while the incident card gains automated retrospective process-chain reconstruction that links parent processes, user sessions, and host movements. Additional oversight features track analyst SLA adherence and let managers drill from team-wide statistics into individual performance metrics. Overall, the platform aims to close the loop from data ingestion through detection, investigation, and response within a single managed workflow.

LangGraph Architecture Combines Hybrid RAG with YARA and Sigma Engines for Streaming Log Analysis

A new architectural pattern integrates LangGraph with a hybrid RAG system and deterministic signature engines to process large volumes of unstructured cybersecurity logs efficiently. The pipeline uses Vector for chunking logs into 250-line segments with 20-line overlap, Kafka for streaming, and an 8-node asynchronous DAG that runs AI and rule-based branches in parallel. Agent 1 groups events and generates hypotheses, while a hybrid RAG module performs query reformulation, vector plus BM25 search with 0.6/0.4 weighting, and LLM re-ranking against a translated MITRE ATT&CK knowledge base stored in ChromaDB. Parallel YARA and Sigma engines scan parsed logs using custom text-based rule implementations, with automatic YARA rule generation triggered when coverage gaps are detected. Final aggregation occurs in Agent 3, which validates findings, deduplicates confirmed incidents, and routes unconfirmed events for manual review while storing reports in PostgreSQL. Tested on a 43-minute synthetic dataset containing 38 MITRE techniques, the system achieved 85.7% precision and 78.9% recall at 3.5 lines per second using Gemini 2.5 Flash.