Today’s organizations have the challenge of managing several different applications and software within their technology stack. The more public-facing platforms an organization utilizes, the greater their public attack surface risks. Without proper protection, they and their community can become an easy target for malicious actors.

Compared to even just a few years ago, the tools available for data scientists and machine learning engineers today are of remarkable variety and ease of use. However, the availability and sophistication of such tools belies the ongoing challenges in implementing end-to-end data analytics use cases in the enterprise and in production.

Customers regularly ask me what types of data sources they should be sending to their SIEMs to get the most value out of the solution. The driver for these conversations is often because the customers have been locked into a SIEM product where they have to pay more for consumption. More log data equals more money and, as a result, enterprises have to make a difficult choice around what log sources and data are what they guess is the most important.

Ever since JASK was founded, we have heavily integrated with threat intelligence platforms to gain context into attacker activity through indicators of compromise (IOCs). Now that we have joined Sumo Logic, our customers have the ability to pull in more data than ever making this feature even more powerful. One of our tightest integrations is with the Anomali (formerly ThreatStream) platform.

As more and more enterprises shift to the cloud, the pressure on SOC teams to protect them against threats rises exponentially. They are the very first line of defense against data breaches and cyber threats that become more frequent and more sophisticated.

Unless you’ve been living under a rock you are probably familiar with the recent Shadow Brokers data dump of the Equation Group tools. In that release a precision SMB backdoor was included called Double Pulsar. This backdoor is implemented by exploiting the recently patched Windows vulnerability: CVE-2017-0143. For detection, we are going to first focus on the backdoor portion of the implant, hunting for traces left behind on the network.

A number of domain “forgeries” or tricky, translated look-alikes have been observed recently. These attack campaigns cleverly abuse International Domain Names (IDN) which, once translated into ASCII in a standard browser, result in the appearance of a corporate or organization name that allows the targeting of such organization’s domains for impersonation or hijacking. This attack has been researched and defined in past campaigns as an IDN homograph attack.

The ability of an actor to remain undiscovered or obfuscating its doings when driving a malicious campaign usually affects the gains of such campaigns. These gains can be measured in different items such as time to allow completion of operations (exfiltration, movement of compromised data), ability to remain operative before take down notices are issued, or ability to obtain gains based on for-profit driven crimeware (DDoS for hire, Crypto mining).

Our digital surface is expanding rapidly and threats are becoming more sophisticated day by day. This is putting enormous strain on security teams, which have already been stretched to the limits. Nonetheless, organizations are skeptical of relieving this cybersecurity strain with AI and automation. Why does this situation persist when it’s simply against the logic?