ChatGPT has recently gained attention for its impressive results and ease of use in creating human-like text results from simple prompts. While many discussions center around its potential impact on various jobs, it’s crucial to also consider the potential consequences for cybersecurity.
Cybersecurity using AI
Hosted by Mark Ehr, Senior Consulting Analyst for 451 Research Advisors and Igor Mezic, Chief Scientist and CTO for MixMode on Tuesday, November 1st at 1pm EST / 10am PST, they will discuss 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.
It’s no surprise that organizations are pouring resources into their security approaches, from investments into hardware and software and significant increases in Cybersecurity professional hiring. In fact, industry watchers expect organizations globally to contribute to $1.75 trillion in cumulative spending on Cybersecurity between 2021 and 2025.
The much-anticipated fifth generation (5G) of broadband cellular technology has arrived, ushering in unprecedented network speed and connectivity. The tech is also spurring innovation into new tech solutions to meet an ever-growing appetite for instant, reliable connectivity, often, faster than most enterprise Cybersecurity teams can handle. If there was ever a time for AI to deliver on the promises made by Cybersecurity platform vendors, it’s now.
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.
In October, 2019 a MixMode customer experienced an incident where an external entity attacked a web server located in their DMZ, compromised it, and then pivoted internally through the DMZ to attempt access of a customer database. While the attacker was successful in penetrating the customer’s network, MixMode was able to detect the event before they were successful in penetrating the customer database.