There has been a great deal of attention paid to the advantages of machine learning in security tools lately. And while it shows a great deal of promise, the reality is alone, machine learning is not enough to consistently and accurately detect, prevent or predict threats and is prone to false-positives. When considering how to reduce the overall threat exposure window, organizations need to understand how, only when combined with additional technologies, machine learning can be effective.
Enterprises are struggling to find secure ways to allow trusted users access sensitive data. Traditional security models designed to protect limited entry points to the data are no longer viable. These best practices, presented by Gurucul’s CEO, Saryu Nayyar, can help address the challenges.
There’s tremendous excitement about Machine Learning and its Artificial Intelligence applications for cybersecurity. There’s a lot of confusion and vendor technobabble, too, that must be sorted out.
From Big Data to Behavioral Analytics to Machine Learning, Artificial Intelligence presents a confusing landscape, in large part because the terms are vague and defined inconsistently (and vendors like it this way).
At the end of June 2016, bad actors published 10,000,000 stolen record for sale. Experts in cybersecurity, Andrew Komarov, Balázs Scheidler and Adam Laub, discuss findings uncovered in a recent InfoArmor report: Healthcare Under Attack: Cybercriminals Target Medical Institutions.