Shimon Noam Oren, VP Research & Deep-Learning at Deep Instinct & Steve Salinas, Head of Product Marketing at Deep Instinct
It is an accepted fact that increasing prevention controls results in a higher rate of false positives. If the goal is to prevent as many threats as possible, with most solutions in the market today it makes sense that as thresholds tighten, some wrong categorization can occur. Given this fact, many security teams continuously work to balance their prevention settings with their capacity to investigate false positives; we call this the Prevention Trade-off Dilemma.
However, there is an emerging technology that eliminates this trade-off dilemma - Deep learning-based prevention. Deep learning prevention by its very nature ensures security teams do not have to make these problematic trade-off decisions required by other security products, as it analyzes all available data to make its malicious/benign decisions.
By attending this webinar, you will:
•See why the cyber trade-off decision is unavoidable with legacy and most next-gen prevention tools
•Gain a servicable understanding of the differences between machine-learning and deep-learning prevention
•Real world example of how this trade-off was significantly minimized with a deep learning-based solution
•Live Q&A with Deep Instinct’s Security experts