When it comes to advancements in cybersecurity, rule-based systems are holding the industry back. Relying on humans to constantly input and label rules in order to detect and stay ahead of threats is a bottleneck process that is setting security teams up for failure, especially with tools like SIEM, NDR, and NTA.
Handling and managing data today has become unwieldy for IT teams on multiple fronts, but the security impact is especially troubling.
The time required for data processing, transition, aggregation, and the normalization does not allow real-time threat detection using today’s SIEM solutions. The only beneficiary of security through log aggregation is the SIEM vendor.
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 predictive AI field of machine learning collects, analyzes, and tests data to predict future possibilities. AI’s neurological network is patterned on the human brain. But AI works on a scale that goes far beyond what is humanly possible. The top uses for predictive AI technologies to protect sensitive data and systems are in network detection and response (NDR), threat detection, and cybercrime prevention.
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.
The evidence indicates that these attackers are traditionally specialized in hijacking social media accounts via SIM Swapping.
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.
A recent WhiteHat Security survey revealed that more than 70 percent of respondents cited AI-based tools as contributing to more efficiency. More than 55 percent of mundane tasks have been replaced by AI, freeing up analysts for other departmental tasks.
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).
One of the most prevalent issues impacting the effectiveness of security teams who use SIEM as their primary means of threat detection and remediation is the fact that data logs are an attractive medium for modern hackers to exploit.
Real unsupervised AI spots security issues sooner and predicts future behavior more accurately than older first- and second-wave solutions. Self-supervised AI technology draws on an understanding of the fundamental nature of the network where it lives, an understanding that isn’t possible with supervised-AI.
Yann LeCun and Yoshua Bengio were recently interviewed by VentureBeat Magazine on the topics of self-supervised learning and human-level intelligence for AI. Our CTO Dr. Igor Mezic sat down with our team to discuss some of the most interesting pieces of the LeCun article, and offer a potential solution to a search for truly self-supervised …
MixMode CTO Responds to Self-Supervised AI Hopes Read More →
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 …
Why Training Matters – And How Adversarial AI Takes Advantage of It Read More →
With MixMode in the center of a program, we will make all the other security investments that you’ve made, better. So when you send data to your SIEM, when you send data to your SOAR, you don’t want those products to be overwhelmed with false positive alerts, with data you don’t need.
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.
While SIEM is undoubtedly a step up from unmonitored network environments, the inherent nature of today’s SIEM software often falls short in several important ways. SIEM is an outdated solution for adequately protecting networks within the modern threatscape.
