Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Frost & Sullivan analysts investigated 70 market participants and recognized Trustwave as one of 15 Innovators in the field. The report noted that Trustwave’s Fusion platform allows clients to manage and view their cybersecurity status, and elite SpiderLabs are driving factors behind being honored. “Trustwave Fusion, a security operations platform that integrates and enriches data from over 750 third-party sources, including cloud, network, endpoints, and email.

As businesses continue to adopt cloud integration and remote work increases, security teams are facing more visibility challenges as well as an influx of security event data. There is more need to understand the threats than ever before, as the threat surface area increases, and tactics increase. Cyber threats are becoming more sophisticated and occurring more frequently, forcing organizations to rely on quality threat detection to protect their data, employees, and reputation.

The saying “data is king” has been around for quite a while and we all know that the world operates and makes decisions on digital data 24x7x365. But, is data king in the field of cybersecurity? I believe that evidence - not data - is what is needed to speed defenders’ knowledge and response capabilities, so let's talk about both.

The Domain Name System (DNS) is responsible for mapping client-facing domain names to their corresponding IP addresses, making it a fundamental element of the internet. DNS-level events provide valuable information about network traffic that can be used to identify malicious activity. For instance, monitoring DNS lookups can help you see whether a host on your network attempted to connect to a site known to contain malware.

At CrowdStrike, we combine cloud scale with machine learning expertise to improve the efficacy of our machine learning models. One method for achieving that involves scanning massive numbers of files that we may not even have in our sample collections before we release our machine learning models. This prerelease scan allows us to maximize the efficacy of our machine learning models while minimizing negative impact of new or updated model releases.