When businesses trust providers to address needs like cybersecurity, they are entrusting vital intellectual property, the private data of employees, consumers, and others, and often, the very infrastructure the business requires to operate.
The SANS Institute recently released an analyst First Look Report on MixMode titled, “Self-Supervised Learning Cybersecurity Platform for Threat Detection.” Matt Bromiley, Senior Security Analyst at SANS and author of the report, explores the barriers SOCs face to conquering the vast amounts of data generated by modern enterprises and the solutions that MixMode provides for detecting and investigating threats including our utilization of self-supervised AI for cybersecurity.
It’s the open secret no one’s talking about — too many cybersecurity solutions in the marketplace stand no chance of providing comprehensive coverage because they are incapable of handling data arising from all sources. Many available solutions are effectively legacy platforms hiding within fresh marketing packages.
The much-anticipated fifth generation (5G) of broadband cellular technology has arrived, ushering in unprecedented network speed and connectivity. The tech is also spurring innovation into new tech solutions to meet an ever-growing appetite for instant, reliable connectivity, often, faster than most enterprise Cybersecurity teams can handle. If there was ever a time for AI to deliver on the promises made by Cybersecurity platform vendors, it’s now.
MixMode’s Head of Sales and Alliances, Geoff Coulehan, understands first-hand the challenges current and incoming MixMode customers are facing with their unwieldy cybersecurity tool stacks.
espite the availability of new technology, companies still have to depend on extracted, aggregated, and normalized historical data to operate. The inherent architecture of legacy solutions diverts focus away from fundamental business problems companies need to address.
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.
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.
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.
