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 Alternatively, organizations can replace their outdated SIEM with a modern single platform self-learning AI solution.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.
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.
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.
The Security Operations Center (SOC) of today is fundamentally flawed. Currently enterprise cybersecurity spend is higher than ever, but despite multi-million dollar cybersecurity investments, organizations remain vulnerable to attacks. One of the major reasons for this is legacy SIEM deployments. More spend does not equal more security.
Handling and managing data today has become unwieldy for IT teams on multiple fronts, but the security impact is especially troubling.
Financial institutions spend five to ten million dollars each year managing data. A recent Computer Services Inc (CSI) study reveals that most banks expect to spend up to 40 percent of their budgets on regulatory compliance cybersecurity, often adopting expensive data normalization strategies.
The very nature of data is its infinite capacity for growth. For security teams at large, highly integrated and complex enterprises like financial services institutions, that growth can quickly become unwieldy when the approach is to store, normalize and prepare all of this data in order to extract value.
Geoff Coulehan, MixMode’s Head of Strategic Alliances, joined Secrutiny’s “Magnify Podcast,” to discuss the priorities CISOs should focus on to better protect their now-remote team of employees.
Most cybersecurity vendors today tout some form of “Artificial Intelligence” as an underlying mechanism for the differentiation of their product among the market. But if everyone is saying they have AI, and everyone is also claiming theirs is the “best,” how can they all be telling the truth?
One thing is clear: more spend does not equal more security and the next generation of cybersecurity tools will route out these inefficiencies.
Many CISOs and SecOps teams were faced with a gut-wrenching choice: addressing the operational challenges of keeping workers connected, or shoring up vulnerabilities before hackers exploited them. Both options involved time-consuming, repetitive, manual work.
MixMode creates a generative baseline. Unlike the historically-based baselines provided by add-on NTA solutions, a generative baseline is predictive, real-time, and accurate. MixMode provides anomaly detection and behavioral analytics and the ability to suppress false positives and surface true positives.
Since we determine everything on data here at MixMode, we went into our website data to see which of our Q2 articles got the most traffic over the past few months. Not surprisingly, the majority of our top articles covered topics on the advancement of AI in cybersecurity and network traffic analysis (NTA).
Most SIEM vendors acknowledge the value of network traffic data for leading indicators of attacks, anomaly detection, and user behavior analysis as being far more useful than log data. Ironically, network traffic data is often expressly excluded from SIEM deployments, because the data ingest significantly increases the required data aggregation and storage costs typically 3-5x.
