Geoffrey is a cybersecurity business executive and leader with over 25 years of experience and a proven track record in sales and solutions across a wide variety of information security technologies, risk management, and regulatory compliance solutions. Geoffrey currently runs Strategic Alliances at MixMode and before coming to MixMode he ran large solutions teams at Splunk, Palo Alto Networks, and SAP.
As any growing company can attest, data is at the center of virtually every decision and impacts nearly every facet of day-to-day operations. Protecting data as it evolves alongside a successful venture must remain a top-level priority, but succeeding at this challenge can be easier said than done.
It’s only natural that organizations gravitate toward popular third party solutions like Security Information and Event Management (SIEM) software, as these products take up the majority of the vendor offerings in the cybersecurity solution marketplace. And in some ways, these products can provide a basic level of security support. From a wider angle, however, SIEM and other log-based security software can become more of a hindrance than a help.
Managing a relatively small, stable data store is one thing, but dynamic companies face immense challenges when those circumstances change. Data growth can become unwieldy to safeguard when it has to be carefully prepared through a series of time-consuming, manual processes before the security software can evaluate it.
Ultimately, an initial cybersecurity investment evolves into an ongoing expense that becomes harder to justify with every passing year.
Why Do Log Data Security Approaches Incur So Many Ongoing Costs?
Enterprises sift through the same data many times over to find and understand details. This work represents a remarkable financial and human resources investment. It’s not a winning approach when all that work and investment leads to a security “solution” that isn’t a solution at all.
Traditional cybersecurity approaches rely on log data. To operate, these systems require SOC teams to massage, extract, transform, normalize, and consolidate log data into a central repository. It’s the only way to get the data into the proprietary format required by the third party security solution.
Companies must consider not only their initial investment into SIEM software, but the ongoing costs of licensing and data retention. These are costs that will exponentially grow over time as the volume of data required for accuracy increases.
Why is Normalizing Data so Expensive?
In part, the normalization process is baked into the contracts organizations sign when they bring on third party security solutions like SIEM. As data is accessed, enterprises see charges start to mount at each stage of the normalization process:
● Consolidation with other extracted data
● Standardization for querying
● Reporting and analytic optimization
Along the way, organizations can spend millions on warehousing structured application data via programs like Snowflake, to store machine-generated data via cloud based databases, to send some data to longer term storage to meet regulatory compliance obligations, and for the utility costs necessary to move all that data around. To put it simply, data handling is expensive.
In many cases, the investment doesn’t even pay off. Despite immense cost outlays, a great deal of enterprise data remains inaccessible, unusable, and valueless because it is effectively unsearchable.
MixMode is the Modern Solution
MixMode is fundamentally different. The MixMode platform uses third-wave AI at the raw signal level and allows customers to store data in its raw, proprietary format without additional levels of normalization and extraction.
MixMode looks beyond data aggregation logs and applies third-wave AI intelligence to the raw signal, examining the indicators provided by that signal. The result is full coverage for the entirety of a network environment, free from the constraints and expenses involved with proprietary data movement and data retention.
With MixMode, organizations can truly invest in functional outcomes and the desired outcomes of the deployment of security platforms rather than needlessly creating expansive, siloed data lakes.