Barkly delivers the strongest protection with the fewest false positives and simplest management. Barkly uses Responsive Machine Learning™ to uniquely block exploits, fileless, and file-based attacks at runtime.
of businesses targeted by ransomware were infected
of successful infections come from fileless attacks
Our agent, Rapidvisor®, monitors activity across multiple levels of the system in real-time.
We don’t stop malware based on isolated behaviors - we recognize sequences of activity that form the DNA of an attack.
When we recognize ransomware and other malware, we stop the process from executing.
Fileless attacks represent 70% of successful endpoint infections, and are becoming a popular delivery vector for ransomware payloads. Traditional endpoint protection focuses on files only and leaves businesses exposed to fileless attacks and exploits. Businesses need stronger protection to prevent revenue and data loss, operating downtime, and reputation damage.
Barkly uses Responsive Machine Learning to uniquely block exploits, fileless, and file-based attacks at runtime. We deliver the strongest protection with the fewest false positives and simplest management.
“Installation and rollout were straightforward. The product is working great, and post implementation communication and follow up excellent.” Read more
“Smooth installation, lightweight agents, no negative impacts operationally, and increased defense posture.” Read more
“We were hit 3 times in late 2015 and early 2016 with ransomware before we went to Barkly...I have only had one alert since we started using Barkly and it stopped the suspicious file immediately before any harm was done” Read more
“Fantastic product, company vision, and technical support.” Read more
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“Installation and use of Barkly couldn’t be any more painless. From our trial to the creation of an account, things have gone smoothly and almost effortlessly. Barkly performed well in testing, and does not impact system performance like other solutions do. Support from Barkly is beyond anything I expected. ”