The problem lies in the existing security strategies that customers are employing to protect data center endpoints. We are specifically referring to the endpoints within the data center where applications are hosted, not end user endpoints, like laptops or phones.
The legacy approach to protecting applications is to monitor endpoints for known threat signatures. Think of antivirus software. AV software has a massive database of known malware signatures, which it uses to identify threats on an endpoint.
The problem with this approach is that if the security solution hasn't seen the threat before, there is no signature to match, and therefore, the threat will be missed. This means that any brand new (or zero-day) threats will go undetected.
ML approaches to endpoint threat detection have become more prominent in recent years, in order to address this problem of identifying unknown threats. The idea is that by aggregating data from as many parts of the...