One of the key components of solving issues with IT is to examine constantly the logs of applications, infrastructure, or security devices. The output of these logs is used by security researchers or site reliability engineers to investigate the problems and come up with a solution. Loom’s solution mimics the cognitive ability of DevOps engineers and researchers to proactively find the anomaly by investigating the logs constantly in real-time and deliver alerts before an escalation occurs. To augment their value proposition, Loom’s team developed the Tribal Knowledge Bank TriKB, a multi-sourced knowledge management bank that bridges the skill requirements to securely analyze the root problems by providing recommended resolution.
Gabby Menachem, CEO of Loom Systems with over fifteen years of experience in technology innovation was instrumental in developing an AI-powered log analysis solution that can be deployed either as software-as-a-service on AWS and Azure or as a virtual machine that can be integrated to run in the customer’s infrastructure in the cloud. “The logs initially are integrated within Loom’s application after which the software system uses cognitive ability to learn the logs automatically in under 90 minutes,” states Menachem. At this point, the system proactively generates alerts about the issues with virtualization tools and customer experience. The software also provides solutions to all of the active problems without intervention of the IT personnel. “We have built a log monitoring solution that has cognitive ability and understands data in a way similar to humans,” states Menachem.
Initially, the logs are integrated within Loom’s application after which the software system uses cognitive ability to learn the logs automatically in under 90 minutes
This immense improvement in swift problem resolution has proven to be time-effective and valuable for any organization's CIO.
The aforementioned solutions maintain two elemental factors. Foremost, due to AIP (Adaptive Internet Protocol), most of the IPs are integrated into the loom system for auto-detection of problems around a specific use-case. Consequently, the solutions produced are in line with the customer’s logs, and a baseline, as well as an understanding of the environmental requirement, is generated without IT support. Secondly, the Loom platform as a learning machine constantly expands its insight knowledge utilizing crowdsourcing from all its customers and learning from the customer’s IT team, therefore it always has an available solution to each detected problem.
Menachem shares an instance wherein KPMG, a professional service company that has terabytes of information inflow from multiple sources took several months to process it in order to find the pattern of cyber-attacks on their client’s environment. On implementing Loom’s self-learning AI-powered log analyzer, the processing time gap was reduced from several months to a few hours to figure out the anomaly leads in the environment. Additionally, data is constantly analyzed to find out the trends in real-time and an overall forensics and cyber defense were achieved.
Contemplating the future prospects of Loom, Menachem expresses his desire to expand their solution within the enterprise sector. Furthermore, they are set to venture into the managed security service (MSS) arena next year and minimize the persisting skill gap in that sector.