ThreatWatch® Advanced Log Analytics (ALA)
What is ThreatWatch® Advanced Log Analysis?
Security On-Demand’s ThreatWatch® ALA – Advanced Log Analysis service is based on SOD’s ground-breaking AQ Technology that acts as the data analytics engine between your data and the application. The AQ Technology in ThreatWatch® ALA provides analysis of indicators up to 100x faster than querying the database directly.
THREAT DETECTION ON A MASSIVE SCALE
When it comes to finding threats, speed to detection is key, that’s where AQ Technology leads the way.
A NEW, PATENTED APPROACH TO THREAT ANALYTICS
Patented AQ Technology powers AI breakthroughs in advanced threat detection
RAPID ANALYSIS OF THREAT DATA
We can find advanced threats 10 to 100 times faster and more accurately than a SIEM or 3rd Generation system
Increase Speed and Performance
AQ Technology used in ALA can analyze all of the log data without data reduction.
AQ Technology used in ALA can analyze all of the log data without data reduction, normalization, or elimination of potential threat indicators. Security On-Demand is able to find threats and indicators that would otherwise be dropped from the data set based on the overwhelming volume of data.
Use Patented Technology
Security On-Demand is the only MSSP/MDR in the world that utilizes AQ Technology
With over 7 patents, Security On-Demand raises the bar when it comes to efficient and effective threat detection. SOD is the most innovative MDR platform with regular capability additions and valuable upgrades.
BENEFITS OF ADVANCED LOG ANALYTICS
The benefits of Security On-Demand’s AQ Technology are disruptive compared with current industry approaches. AQ Technology provides the following benefits:
With over 7 patents, Security On-Demand raises the bar when it comes to efficient and effective threat detection. SOD is the most innovative MDR platform with regular capability additions and valuable upgrades.
- Enhances Machine Learning Models
- Eliminates the need to perform data reduction
- Significantly enhances the time to detection
- Lowers the labor cost of false positives
- Increases the percentage of threats detected
- Eliminate the need to sample the data to know what’s in it
- Eliminate the need to extract data sets into elastic search to hunt for data
- Conduct broad searches for threat indicators across large data sets not previously possible
- Conduct a series of Machine Learning Automated database queries to hunt for threat indicators
- Know what is in the data set (or data lake) before we search for it