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Despite AI’s potential to help defend against cyber attacks, AI adoption in cybersecurity practices remains in its early stages. Why is this the case, and how can organizations overcome these hurdles to pave the way for a secure future?
The star of the moment is Large Language Models (aka LLMs), the foundational model that powers ChatGPT. There are plenty of documented examples of truly impressive feats built on this technology: writing reports or outputting code in seconds. At its core, LLMs basically ingest A LOT of text (e.g., think Internet) as a corpus of training data and rely on human feedback in a type of supervised training called reinforcement learning.
At MixMode, we will look you in the eye when we explain that we know the marketplace is crowded with failed promises of cyber platforms that claim to be “AI-based, self-learning, offering real-time detection and prevention at scale.
If it’s AI, it’s probably written in PowerPoint… Read More →
Zero-day attacks are considered the number one cybersecurity threat to company networks large and small. Understand what they are, why it’s so hard to detect them, and how artificial intelligence (AI) is helping to solve this modern problem.
A Proven Strategy For Defending Against Zero-Day Exploits And Attacks — Updated for 2023 Read More →
In a world where bad actors are capable of building sophisticated AI capable of sidestepping traditional cybersecurity platforms, it has become critically important to onboard tools that work in real-time, are deadly accurate, and can predict an incident before it happens.
Getting Ahead of the Adversary with Third-Wave AI Read More →
Shannon Lawson, CISO for the City of Phoenix, and Geoffrey Coulehan, Head of Sales for MixMode recently joined forces for a fireside chat at the Evanta CISO Summit in Phoenix, AZ. Lawson and Coulehan have been partnering together for almost three years to transform the City’s cybersecurity initiatives and Security Operations Center (SOC).
Video: Modernizing Phoenix’s Cybersecurity to Combat Nation-State Attacks Read More →
Unsupervised learning actually draws inferences from datasets without labels. It is best used if you want to find patterns but don’t know exactly what you’re looking for.
Unsupervised Machine Learning: Benefits for the Financial Services Industry Read More →
In the report, 451 Research explains why security analytics needs to include advanced Third-Wave AI, which autonomously learns normal behavior and adapts to constantly changing network environments, to address the next generation of cyberthreats and increase SOC productivity.
MixMode’s unsupervised, third-wave AI computes patterns of interaction over many different timescales, contrasting it over the next 5-minute interval with what was seen previously. Should patterns deviate, the platform performs an assessment of the security risk implied in that deviation and presents it to the user.
Understanding the Evolution and Impact of AI on Cybersecurity Read More →
MixMode Inc., a leading global provider of Artificial Intelligence-powered Cybersecurity, announced today that the company was recognized as a supply side innovator in the November 2021 Gartner report: Emerging Technologies: Tech Innovators in AI in Attack Detection — Supply Side.
Anomaly detection, the “identification of rare occurrences, items, or events of concern due to their differing characteristics from the majority of the processed data,” allows organizations to track “security errors, structural defects and even bank fraud,” according to DeepAI and described in three main forms of anomaly detection as: unsupervised, supervised and semi-supervised. Security Operations Center (SOC) analysts use each of these approaches to varying degrees of effectiveness in Cybersecurity applications.
What is Anomaly Detection in Cybersecurity? Read More →
Organizations are exclusively depending on selective information forwarded to the SIEM. The information that inevitably exists outside the system of record — information relevant for zero-day attacks — is ignored.
SIEM Cannot Detect (and Ignores) Zero-Day Attacks Read More →
On the surface, an “incremental stacking” approach to correlative analysis platforms like SIEM, XDR and UEBA is logical. Organizations can overcome some of the inherent limitations present in their security solutions by adding a network traffic analysis (NTA), for example. Industry analysts have been touting this approach for some time now as necessary for full coverage enterprise security.
Within the first 24 hours after deployment, MixMode had enabled the government entity to regain control over the security environment and network data infrastructure. No longer limited to log data analysis, they were able to identify and address real-time threats as well as network and operational configuration challenges.
Maximize ROI with Greater Efficacy Using Unsupervised AI Read More →
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.
The Hidden Costs and Challenges of Log Data Storage Using a SIEM Read More →
The cybersecurity market has, simply put, been cobbled together. A tangled web of non-integrated systems and alerts from siloed systems. Enterprises are now being forced to utilize a “Frankenstein” of stitched together tools to create a platform that might cover their security bases.
The Case Against Using a Frankenstein Cybersecurity Platform Read More →
The evidence indicates that these attackers are traditionally specialized in hijacking social media accounts via SIM Swapping.
Phishing for Bitcoin: The Twitter Hack Masterminded by a 17 Year Old Read More →
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?
Guide: How to Choose an AI-Based Cybersecurity Platform Read More →
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.
Deep Dive: How much time do security teams spend labeling with Supervised Learning? Read More →
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.
Why The Future of Cybersecurity Needs Both Humans and AI Working Together Read More →