///Products

Who are the key technology partners for LogPoint?

LogPoint has a number of strategic technology partnerships with companies such as VERINT, Cyberark, Onapsis, AgileSI, Dflabs, TrendMicro, LogBinder, Nxlog and Recorded Future

More information: https://www.logpoint.com/en/partners/strategic-partners/

Is LogPoint EAL (Common Criteria) certified?

Yes. LogPoint holds a EAL 3+ certification, which is the highest certification level of any vendor in the SIEM industry.

More information: https://www.logpoint.com/en/product/eal3-certified/

What are the average server requirements for a mid-sized LogPoint deployment?

For a midsize LogPoint deployment we would work with the enterprise to understand the existing log sources and their locations before deploying. An all-in-one appliance (virtual or physical) would be deployed centrally, with the following specifications:
24 CPU Cores, 256 GB RAM, 8 TB Storage

Additional collector/backend modules would then be deployed in key locations with the following specifications
14 CPU Cores , 32 GB RAM, 2 TB Storage

Always consult with a LogPoint partner before finalizing deployment sizing

How is data acquired/ingested in LogPoint?

LogPoint enables customers to deploy collection instances in different areas of the networks. These collection instances parse, normalizes, enriches, filters, routes, compresses, and buffers event data. The collection instances offer full high availability through failover and load balancing between multiple backends. The LogPoint collection instances scale to more than 50.000 EPS and are widely regarded as the highest throughput collection architecture on the market.

What does the LogPoint development program look like?

We use open innovation methods internally as well as in close collaboration with customers and partners. We also work with different research institutes and universities for exploring areas of Big data, IoT, and Machine learning. The focus area of the research revolves around the challenges around security and efficient data handling that we have observed in the industry. Some of these are behavior analytics and unsupervised learning, search and query optimization, latent topic modeling, intelligent data parsing, expert systems for incident handling, profiling based anomaly detection, etc.