Enterprise Data Lake

Exponential growth in data volumes, and rapidly increasing attack surfaces are challenging security teams to control cyber threats and data leaks. Security data lakes can help address the key security challenges facing organizations.

The massive data generated from Endpoint Detection and Response (EDR) and Extended Detection and Response (XDR) solutions can take days to analyze and its cost prohibitive to store all the collected data which can cause key signals to be missed during an investigation or breaches to go unnoticed.

To overcome the challenges of scale, cost, structure, and detection capabilities, SafePaaS provides enterprise security data lake to separate storage from compute.

Enterprise security data lake is a centralized repository designed to store, process, and analyze all security meta-data from data sources that represent security threats to an organization.  Security meta-data can be consumed using industry standard message formats such as JSON, XML, CSV/Flat-file etc and transfer methods including SOAP, REST, JDBC, sFTP, etc.  The housed data can be cross-linked, parsed,  searched, classified, masked, and encrypted for enterprise wide security management to prevent cyber threats and regulatory compliance penalties. 

Traditional SIEMs are ineffective to handle the scale of the data that organizations generate today - resulting in unmitigated threats to the business systems. 

SafePaaS provides insights into security threats by centralizing security data across on-premise, cloud, and SaaS environments and enabling advanced analytics to detect and respond to sophisticated attackers.

SafePaaS enables security teams to collect and analyze security data holistically by making a wide variety of data types searchable once the data is extracted, transformed, and loaded - a one-time setup.  Once this data is normalized, it can be used for performing effective threat detection and investigations. You can collect and analyze logs from all the data sources including applications, databases, server and SIEM event logs.   Security events can be enriched by adding event and non-event contextual information such as identity context (user, host, IP addresses), vulnerability context (scan reports), business context, and more. Context plays an important role in eliminating False Positives, which in turn helps prioritize higher risk threats.

Data lake technology is based on ETL pipelines for coalescing data into proper formats. Teams can also utilize these same mechanisms to derive intelligence in the form of “fact tables”, which saves compute time during an investigation. For example, a “dimension table” may contain all API calls into an AWS account, but a job can pull in all of the Admin calls into a specific “fact table”, saving a huge amount of time during an investigation.