AQ TechnologyTM allows searches of massive data sets such as log data to be queried for threat indicators 10-100+ times faster than conventional databases. This drastically improves time to detection of cyber-security threats with the ability to apply both supervised and unsupervised machine learning approaches to automate the detection of advanced threats.
SOD’s ground-breaking AQ TechnologyTM is based on Rough Set mathematical theory using the concept known as “Approximate Query”. With only a small trade off in query accuracy (less than 0.4%), this allows you to look at all the data, all the time. Only then can you find the indicators of threat and remediate the vulnerability.
Security is a Data Problem
Data access is the main inhibiting factor in performing threat analysis. No matter which other threat detection model is used, there are always more questions to ask the data. The security analyst will always have additional questions to ask the data to validate assumptions. That takes time and there’s room for error. This is the premise for the SOD threat analytics model and why AQ Technology is the critical element used to solve the data access problem.