The 2015 Red List report is out and I’m excited to see that Niara got a special call out in the report. According to the report, the number one problem faced by security practitioners is logging and analytics. And, per the report, the headway that Niara has made into true machine learning and advanced algorithms will drive overall efficacy.
First, let me provide some context on the Red List. The Red List, created by well-known security practitioner Justin Somaini, reports on the results of a survey regarding early-stage security startups. It was first released in 2013 and provides a ranking of security startups based on their value in solving critical problems.
The survey for this edition of the Red List was sent to approximately 40,000 security practitioners in April 2015, a quarter before Niara came out of stealth. Getting included in an independent report such as this one means that we were already on security practitioners’ radar well before information on our solution was publicly available. To me, that is just as exciting as being included in the report. It shows that the problems Niara addresses are real enough for organizations to actively seek out information on how we can help them.
It’s also heartening to read about the transition that’s happening in this space where, according to the Red List, an approach based on regular expression and correlation rules is being replaced with machine learning algorithms. Anticipating that transition was one of the drivers for our vision (and a discussion point in my last blog) about security intelligence. We knew that traditional approaches would simply add to the alert white noise when faced with 1) the volume of security data being produced by IT systems in modern organizations and 2) the sophistication of advanced threats.
Niara’s big data security analytics (BDSA) provides the machine assist for security analysts to make better decisions, even when faced with overwhelming volumes of data. And all the innovations built into the Niara solution – big data, advanced analytics and forensics converged into a single solution; machine learning-based behavioral analytics (including entity and user behavior analytics or UBA) modules that automatically surface hidden threats; layered forensics that provide analysts with context when they conduct incident investigations or explore a hypothesis; the ability to embed into existing infrastructure – is what customers (e.g., a leading energy company, an F500 financial services company) say they need to handle the increasing amounts of security data and efficiently identify advanced threats that get past perimeter security systems.
The transition is just beginning. Security is an ever-changing cat-and-mouse game, with the bad guys trying their hardest to stay ahead. But Niara, built on a big data architecture and using advanced statistical modeling, is well-positioned thwart them by providing customers with much needed capabilities for effective threat detection and incident response, even in the face of changing requirements.