SOC

Our Top 2020 Cybersecurity Insights

The transition from office to remote environments was abrupt and one of the most defining moments that the cybersecurity industry and professionals faced in 2020. We wrote about the top issues CISOs were facing throughout the year but also doubled down on sharing insights about the evolution of next-generation SOCs, the failure of SIEM platforms as organizations are experiencing them today, and how self-supervised AI fits into the equation.

MixMode in the Real World: Customers Turn to MixMode Frustrated and in Search of a Viable SIEM Alternative

SIEM has failed to meet the needs of enterprises in the modern threatscape. One huge reason for this is that over time, most organizations will come to the sad realization that they will never achieve a full enterprise deployment of their SIEM. By its very nature, SIEM is always “in process.” It’s not unusual for an organization to have an SIEM in process for a full decade.

Featured Use Case: Why a Large US Utility Company Turned to MixMode to Address Utility Grid Vulnerabilities

A large utility company approached MixMode with the following scenario: The enterprise SOC was utilizing a shared SIEM application that was being utilized by several stakeholders: the networking team, the SCADA team, the dev-ops team, the compliance team and cybersecurity teams for “basic search and investigation of log files to meet regulatory compliance requirements”.

How Vendors Capitalize on SIEM’s Fundamental Flaws

Because the fundamental nature of SIEM requires infinite amounts of data, security teams are forced to constantly wrangle their network data and faced with an unmanageable number of false positive alerts. This means they have to devise efficient ways to collect, organize and store data, resulting in an incredible investment in human and financial resources.

The Case Against Using a Frankenstein Cybersecurity Platform

The cybersecurity market has, simply put, been cobbled together. A tangled web of non-integrated systems and alerts from siloed systems. Enterprises are now being forced to utilize a “Frankenstein” of stitched together tools to create a platform that might cover their security bases.

Improving on the Typical SIEM Model

Despite its inherent flaws, today’s SIEM software solutions still shine when it comes to searching and investigating log data. One effective, comprehensive approach to network security pairs the best parts of SIEM with modern, AI-driven predictive analysis tools. Alternatively, organizations can replace their outdated SIEM with a modern single platform self-learning AI solution.

Whitepaper: The Failed Promises of SIEM

The fundamental SIEM flaws lie in the platform’s need for continual adjustment, endless data stores, and a tendency to create an overwhelming number of false positives. When organizations instead turn to a next-generation cybersecurity solution, which predicts behavior with an unsupervised (zero tuning) system, they are poised to save on both financial and human resources.

How Data Normalization in Cybersecurity Impacts Regulatory Compliance

Complying with privacy regulations requires all organizations to have access to data on demand, wherever it lives on a network. With the unfathomable amount of data managed by most organizations operating in the finance space today, it can become a significant challenge to locate specific data across legacy systems and networks with countless connections online and off.

Why The Future of Cybersecurity Needs Both Humans and AI Working Together

A recent WhiteHat Security survey revealed that more than 70 percent of respondents cited AI-based tools as contributing to more efficiency. More than 55 percent of mundane tasks have been replaced by AI, freeing up analysts for other departmental tasks.