Predictive AI

Building a Better SOC Based on What We Learned in 2020

Every network vulnerability opened new opportunities for hackers to infiltrate systems, steal data and wreak havoc. Several notable security incidents have left governments, private organizations, medical systems and large enterprise networks reeling. Many of these entities have discovered that their security plans are simply not up to the task of mitigating modern cybersecurity threats.

Our Top 2020 Cybersecurity Insights

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.

Russian Hack of U.S. Federal Agencies Shine Spotlight on SIEM Failures in Cybersecurity

In what the New York Times is calling, “One of the most sophisticated and perhaps largest hacks in more than five years,” malicious adversaries acting on behalf of a foreign government, likely Russian, broke into the email systems of multiple U.S. Federal agencies including the Treasury and Commerce Departments.

Techiexpert: How Predictive AI Protects Against Ransomware, GANs and More

MixMode CTO and Chief Scientist, Igor Mezic, recently contributed an article for Techiexpert that examines three modern AI adversarial attacks, the financial toll they are having on some of our most important systems (including healthcare), and how predictive, third-wave AI is the only future-proof cybersecurity solution to protect organizations from these intelligent attacks.

Recent Ransomware Attacks on U.S. Hospitals Highlight the Inefficiency of Rules-Based Cybersecurity Solutions

A number of recent high profile ransomware attacks on U.S. hospitals have demonstrated the urgency for organizations, municipalities, and critical services to take a proactive approach to protecting networks with a predictive AI solution.

The Case Against Using a Frankenstein Cybersecurity Platform

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.

Improving on the Typical SIEM Model

Despite its inherent flaws, today’s SIEM software solutions still shine when it comes to searching and investigating log data. One effective, comprehensive approach to network security pairs the best parts of SIEM with modern, AI-driven predictive analysis tools. Alternatively, organizations can replace their outdated SIEM with a modern single platform self-learning AI solution.

What is Predictive AI and How is it Being Used in Cybersecurity?

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.

One Thing All Cybersecurity teams Should Have During COVID-19

COVID-19 has caused most corporate businesses that remain open to shift to a work from home, remote workplace. Because of this, the cybersecurity industry has been turned on its head. Security teams went from monitoring and protecting established network environments to quickly pivoting their tools, resources, and oversight to manage a distributed workforce. This has …

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New Whitepaper: How Predictive AI is Disrupting the Cybersecurity Industry

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.