Quad Miners Puts Context Data at the Center of AI Security

Quad Miners says AI security systems need context data as a basis for decisions before model performance can support threat detection and response. An alert signaling a possible cyber threat does not show what information was exchanged or whether behavior differed from normal patterns. The company says expanding automation without sufficient evidence can turn analysis errors into incorrect blocking, so data must be available to support and validate accurate detection and reliable autonomous response.

The approach centers on a context-data-based AI security observability platform. Quad Miners’ Network Blackbox, a Network Detection and Response (NDR) solution, generates data from full-packet collection through session and file reconstruction, behavior analysis and threat detection. The company says it connects network activity with the basis for AI decisions through three elements: original transaction and session data, behavioral baselines, and explanations for detections. This lets analysts inspect actual communications, compare normal and abnormal behavior, and review why an activity was classified as a threat.

Network Blackbox compares behavior in each session with asset-specific baselines and traces attack patterns through scenario hunting, which combines signs such as repeated requests and parameter tampering. Full-packet analysis can reveal the contents of communications and suspected exfiltrated data, as well as connection information. AI functions are used for asset behavior anomaly detection, email context anomaly detection, encrypted traffic classification, and explainable threat-context analysis. The company says natural-language explanations of detection evidence help reduce the effort required to review false positives.

Quad Miners is conducting a Proof of Value (PoV) with a global search and analytics platform company to assess the operational value of analyzing Large Language Model (LLM)-based attack scenarios in a telecom security monitoring environment. Context data and incident information from Security Information and Event Management (SIEM) correlation analysis are sent to the analytics platform. The customer’s internal language model then performs correlation analysis on alerts generated through normalization and machine-learning analysis. The company says it is connecting this data foundation to customers’ existing security systems to advance the process from decisions to response.

Network Blackbox sends detection results and related network information to customers’ Security Information and Event Management (SIEM), Endpoint Detection and Response (EDR), and Security Orchestration, Automation and Response (SOAR) tools. It provides endpoint security tools with details of suspected devices and data-exfiltration routes to narrow investigations, and supplies confirmed attack communication details for firewall response. Quad Miners says linking different tools’ analyses and actions through network evidence lets customers connect detection, analysis and response in stages suited to their environments, while strengthening existing security investments and the evidence behind analysis. Quad Miners Chief Technology Officer Yongho Kim said data quality, as the basis for AI decisions, is the standard for countering AI hacking. He said organizations that secure context data first can improve AI detection and automation on top of existing SIEM, EDR and SOAR investments.

Quad Miners says AI security systems need context data as a basis for decisions before model performance can support threat detection and response. An alert signaling a possible cyber threat does not show what information was exchanged or whether behavior differed from normal patterns. The company says expanding automation without sufficient evidence can turn analysis errors into incorrect blocking, so data must be available to support and validate accurate detection and reliable autonomous response.

The approach centers on a context-data-based AI security observability platform. Quad Miners’ Network Blackbox, a Network Detection and Response (NDR) solution, generates data from full-packet collection through session and file reconstruction, behavior analysis and threat detection. The company says it connects network activity with the basis for AI decisions through three elements: original transaction and session data, behavioral baselines, and explanations for detections. This lets analysts inspect actual communications, compare normal and abnormal behavior, and review why an activity was classified as a threat.

Network Blackbox compares behavior in each session with asset-specific baselines and traces attack patterns through scenario hunting, which combines signs such as repeated requests and parameter tampering. Full-packet analysis can reveal the contents of communications and suspected exfiltrated data, as well as connection information. AI functions are used for asset behavior anomaly detection, email context anomaly detection, encrypted traffic classification, and explainable threat-context analysis. The company says natural-language explanations of detection evidence help reduce the effort required to review false positives.

Quad Miners is conducting a Proof of Value (PoV) with a global search and analytics platform company to assess the operational value of analyzing Large Language Model (LLM)-based attack scenarios in a telecom security monitoring environment. Context data and incident information from Security Information and Event Management (SIEM) correlation analysis are sent to the analytics platform. The customer’s internal language model then performs correlation analysis on alerts generated through normalization and machine-learning analysis. The company says it is connecting this data foundation to customers’ existing security systems to advance the process from decisions to response.

Network Blackbox sends detection results and related network information to customers’ Security Information and Event Management (SIEM), Endpoint Detection and Response (EDR), and Security Orchestration, Automation and Response (SOAR) tools. It provides endpoint security tools with details of suspected devices and data-exfiltration routes to narrow investigations, and supplies confirmed attack communication details for firewall response. Quad Miners says linking different tools’ analyses and actions through network evidence lets customers connect detection, analysis and response in stages suited to their environments, while strengthening existing security investments and the evidence behind analysis. Quad Miners Chief Technology Officer Yongho Kim said data quality, as the basis for AI decisions, is the standard for countering AI hacking. He said organizations that secure context data first can improve AI detection and automation on top of existing SIEM, EDR and SOAR investments.