The Economic Value of Prevention in the Cybersecurity Lifecycle report was recently released by the Ponemon Institute and sponsored by cybersecurity provider Deep Instinct. The report delineates the savings that organizations could actualize per incident if effective preventative measures are implemented, which range from around $300,000 to over a million USD depending on the nature of the attack.
The study uncovered an interesting discrepancy: although a whopping 70% of cybersecurity professionals believe preventative security measures improve cybersecurity posture and reduce overall security costs, only 21% of their organizations’ budgets are allocated to attack prevention.
Steve Salinas joins me on the podcast to address how deep learning is making preventative cybersecurity a reality. I learn more about how Deep Learning is a fully autonomous system that can learn from all the available raw data, as an expert’s technological knowledge does not limit it. It provides the best unknown cyber threat prevention, detection, and response with the highest detection rates of unknown malware; while generating a near-zero false-positive rate.
Since Deep Learning is input-agnostic, I also explore how it can protect any new type of device, endpoint, mobile or server, and type of operating system, against a broad range of file-based or fileless attacks, with a low impact on performance. The Deep Learning brain handles most of the security, in almost zero-time (milliseconds). It also provides administrators a very user-friendly management console (deployed in the cloud or on-prem) to view all of the relevant information.
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