Let’s take a closer look at the false narrative being propagated in the cybersecurity market and explore some more appropriate alternatives. An example of a legacy cyber tool vendor “feature” being touted as an ideal solution to the problem of automatic remediation is reliant on a standard (though rarely used for reasons we will explore in this article) function known as TCP Reset.
Mature cyber security teams understand a harsh reality often ignored by legacy cyber tools vendors: correlation must not be confused with causation.
Cybersecurity tool vendors understand that historically, the determining factors for a successful Security Operations Center (SOC) have been dependent entirely on 2 operational factors rather than the underlying technology.
Geoffrey Coulehan, Head of Sales for MixMode, pens our latest whitepaper on the misleading “one-click remediation” claims that many cybersecurity tool vendors are using in the marketplace.
Many vendor claims are unclear as to what they’re promising in this space, but they are quite insistent that without Zero Trust network architectures, organizations are at great risk for data breaches and other network sabotage.
How sure are you that log files represent the best source of information to base your entire Cybersecurity program upon? Log data is the cornerstone of every traditional cybersecurity platform including SIEM (Security Information and Event Management), UEBA (User and Entity Behavior Analytics), and xDR (Detection and Response).
As SIEM evolved, vendors began bolting on NDR (network detection and response) and NTA (network traffic analysis) to their base SIEM offerings. The hope (and promise) was that these tools would add the real-time security solution that was lacking with SIEM technology.
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.
A jumble of ineffective, incomplete, extremely expensive platforms that don’t meet the fundamental challenges faced by enterprise SOC teams in the early 2020s is not sustainable.
The following is an excerpt from our recent whitepaper, “Why Traditional Cybersecurity Tools Cannot Defend Against Zero-Day and No Signature Attacks,” in which we dive into how traditional cybersecurity tools work, why this fundamentally limits them from being able to detect zero-day or previously unknown attacks, why the industry standard for breach detection is around …
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.
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).
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.
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.
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.
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.
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?
