The world is changing in front of our eyes. Where facts, truth and honesty were once our most valuable assets, nowadays, alternative-facts, post-truths and outright lies reign. Unfortunately, the cybersecurity industry is no exception to this trend.
While the idea of artificial intelligence (AI) and the real applications of machine learning (ML) have been influencing various fields for years now, their full transformative potential is yet to be realized. ML-based technologies increasingly help fight large-scale fraud, evaluate and optimize business processes, improve testing procedures and develop new solutions to existing problems. Like
Machine learning (ML) is routinely cited by post-truth vendors as their biggest selling point, their main advantage. But ML – if it’s done properly – comes with problems and limitations.
From competitive salaries to ever-evolving job descriptions, there are myriad reasons why a cybersecurity career could be right for you
And how would you know if the algorithm was tampered with?
With 2019 ending, ESET experts offer their insights into how new innovations will impact our privacy, security and lives in the not so distant future
As the use of this technology grows so does the risk that attackers may hijack it
In December 2013 news broke that Target suffered a breach that forced consumers and the cybersecurity community to question the security practices of retailers
With just days left in 2018, ESET experts offer their reflections in ‘Cybersecurity Trends 2019’ on themes that are set to figure prominently in the upcoming year
To help the reader navigate through the maze of such threats, ESET’s thought leaders have zeroed in on several areas that top the priority list in our exercise in looking forward.
Unless companies processing citizens’ personal data fully understand the reasoning behind the decisions made based on their machine-learning models, they will find themselves between a rock and a hard place.
Machine learning (ML) in eight blogposts!? In our last post, let’s take a peek under the hood of ESET’s cybersecurity engine and its ML gears.
Machine learning alone is not enough to protect endpoints and predicting an attacker's next moves. Other security solutions and human input are needed.
There is no magic in machine learning. It's a field of computer science that gives computers the ability to find patterns in huge amounts of data.
Welcome to the beginning of a new series of short articles focused on the currents state of AI, all the ins and outs of machine learning, and how it affects cybersecurity.
As deepfakes become indistinguishable from reality and the potential for the misuse of synthetic content is virtually endless, what can you do to avoid falling victim to deepfake fraud?
Without ever setting foot in the lab, a threat actor could dupe DNA researchers into creating pathogens, according to a study describing "an end-to-end cyber-biological attack"
…and why are they selling it to other security vendors and product testers?
As the pandemic forces many employees to work from home, can your organization stay productive – and safe?
Footage defined as parody or satire will be permitted, as the social network isn’t slamming the door on all types of manipulated media
