Wednesday, October 27, 2021
AI is a term that has been thrown around in the cybersecurity industry for quite some time. The common components typically referenced when talking about AI are Machine learning and Deep Learning, but what are the differences? When it comes to cybersecurity, AI can be a huge leap forward in combating cyberattacks, but not all solutions are the same. If AI could be the silver bullet, why are today's AI solutions not working? Many of the traditional Machine Learning cybersecurity solutions currently available are causing massive operational challenges as they are not adequately combating the ever-evolving and sophisticated threats. Detection and response-based solutions (EDR) are insufficient because they typically can take 10 minutes or more to identify a threat detected in the environment. It takes sub 3 seconds to infect and start encrypting a system; that is why time is of the essence. You have to prevent the infection and damage it can inflict before it takes root, executes, and spreads. One important item of note is the emerging trend of adversarial machine learning being leveraged by cybercriminals; how can this be combated? Executives and security leaders need to start adopting a preventative approach to cybersecurity utilizing the latest in cutting-edge security solutions, which is only made possible through the use of AI and, more importantly, the use of Deep Learning.The great news is that AI technologies are advancing. Deep learning is proven to be the most effective prevention cybersecurity solution to date, resulting in unmatched prevention rates with proven lowest false positive rates. As organizations evaluate new technologies, a firm understanding of the differences, challenges, and benefits of all AI solutions is a must. Therefore, educational advancements in machine learning and deep learning are well warranted.