However, in practice, distributions frequently shift due to factors that cannot be explored or controlled in the training data. Convolutional neural networks trained on millions of photos, for example, might fail when seeing things in new lighting conditions, from slightly altered angles, or against new backdrops. Attempts to resolve these issues include training machine learning models on more samples. However, as the environment becomes more complicated, adding additional training instances becomes impractical to cover the entire distribution.

Why Is Deep Learning Called Deep? You must have asked that question.

From insider threats to abuse of privileges and management to hackers, humans are important and diverse carriers of cyber risks. Therefore, Machine Learning help detect changes in the way users interact in the IT environment and describe their behavioral characteristics in the attack environment. Despite high marketing requirements, the reality is that the corporate security environment is a huge and dynamic network. And managers must constantly monitor, audit, and update based on continuous, unpredictable, internal, and external threat vectors. ML introduces various enhancements in the ability to detect, investigate, and respond to threats. But it is a combination of personnel and technology that can manage a full range of threats in the ever-evolving security environment.