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 …
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.
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.
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.
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.
The following is an excerpt from our recently published whitepaper, “Self-Supervised Learning – AI for Complex Network Security.” The author, Dr. Peter Stephenson, is a cybersecurity and digital forensics expert having practiced in the security, forensics and digital investigation fields for over 55 years. Section 4 – Why Training Matters – And How The Adversary …
Deep learning makes decisions based upon the data it sees and the data that it doesn’t see but infers from what it does see. This became useful in the AV industry when the adversary introduced polymorphic viruses. These are viruses that change their appearance on the fly and not always in the same way.
Artificial Intelligence – or AI – has become a buzzword since it emerged in the 1950s. However, all AI systems are not created equal. In our white paper, “Self-Supervised Learning – AI For Complex Network Security,” Dr. Peter Stephenson explains the different “waves” of artificial intelligence. He uses the DARPA definitions for each of these …
As organizations began to rely more heavily on networking to carry out their operations over the past decade, IT teams added security analyst positions. These professionals focused on network security and providing regulatory compliance oversight. Over time, the role of the security analyst has expanded to include threat hunting tasks. That is, evaluating security platform …
The world’s reliance on fast, reliable, secure networks has likely never been as apparent as it became in early 2020, when the world responded to the Coronavirus pandemic. Suddenly, vast swaths of the global workforce needed to access and send enormous stores of data from home. In some ways, it couldn’t have happened at a worse time.
Our newest whitepaper, “How Predictive AI is Disrupting the Cybersecurity Industry,” evaluates several common SecOps issues around Network Traffic Analysis, explaining why typical solutions are wholly ineffective and represent sunk costs versus added value. We examine how self-supervised learning AI is poised to overcome the SecOps challenges of protecting today’s distributed networks.