The Hidden Costs and Challenges of Log Data Storage Using a SIEM
Ultimately, MixMode found, the log-based SIEM approach resulted in five times the amount of data that needed to be stored, a cost that was passed along to the government entity.
Ultimately, MixMode found, the log-based SIEM approach resulted in five times the amount of data that needed to be stored, a cost that was passed along to the government entity.
Data is the beating heart of every modern organization, but it’s only valuable when it’s accessible, understandable, and most importantly, protected.
What are companies really gaining when they take on SOAR? At a high level, SOAR and legacy platforms are falling far short of their promises. SOCs are left with several pivotal questions.
Staying on top of cybersecurity risk can feel like a losing battle in today’s modern, hyperconnected reality. The influx of IoT devices and increased reliance of BYOD devices has created a diverse, complex threatscape rife with overlapping vulnerabilities across physical and cyber assets.
Most customers are surprised to learn that SOAR platforms rely on invoking 3rd party technologies, including next-generation firewalls and endpoint protection platforms via traditional API calls to isolate and quarantine malicious threats and users.
Every network vulnerability opened new opportunities for hackers to infiltrate systems, steal data and wreak havoc. Several notable security incidents have left governments, private organizations, medical systems and large enterprise networks reeling. Many of these entities have discovered that their security plans are simply not up to the task of mitigating modern cybersecurity threats.
MixMode has been recognized as one of the best tech startups in Santa Barbara in 2021. Thank you to the staff at The Tech Tribune for this honor.
The latest in an ever-increasing bag of supplemental platforms to address the shortcomings of legacy cyber threat platforms is SOAR (Security Orchestration Automation and Response).
MixMode CTO and Chief Scientist, Igor Mezic, recently contributed an article for CPO Magazine that examines the evolution of Machine Learning (ML) and Artificial Intelligence (AI) within cybersecurity, the three waves of AI, and the modern-day application of predictive AI in cybersecurity to protect against adversaries who are also utilizing AI technology.
The only truly workable network solution must bridge the inherent gaps that exist throughout [infrastructure] systems. It must also be capable enough to root out hidden vulnerabilities ripe for hacking.
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.
In what the New York Times is calling, “One of the most sophisticated and perhaps largest hacks in more than five years,” malicious adversaries acting on behalf of a foreign government, likely Russian, broke into the email systems of multiple U.S. Federal agencies including the Treasury and Commerce Departments.
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.
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”.
A number of recent high profile ransomware attacks on U.S. hospitals have demonstrated the urgency for organizations, municipalities, and critical services to take a proactive approach to protecting networks with a predictive AI solution.
Despite a three-year SIEM deployment and a two-year UBA deployment, government personnel needed an alternative to better detect and manage threats in real-time, as well as an improved platform for gathering comprehensive data.
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.
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.
MixMode teamed up with Ravenii to host a webinar focused on the history and evolution of SIEM platforms, their ideal role in a SOC today, and how they fall short as a threat detection tool in today’s modern cybersecurity environment.
It should be noted that SIEM platforms are exceptionally effective at what they initially were intended for: providing enterprise teams with a central repository of log information that would allow them to conduct search and investigation activities against machine-generated data. If this was all an enterprise cybersecurity team needed in 2020 to thwart attacks and stop bad actors from infiltrating their systems, SIEM would truly be the cybersecurity silver bullet that it claims to be.