A recent IBM study estimates the cost of a data breach to have elevated 12% over the past 5 years (2019), and now averages $3.92 million. These are devastating indications for most organizations who operate with small budgets. While that IBM study blames novel malicious cyberattacks as the cause of the most substantial breaches, it evaluates that nearly half (49%) of the data breaches were inhouse, inadvertent breaches from human error and system glitches. In the following, Infolob showcases how to overcome these inhouse faults with our Data Science solution.
Infolob aspires to reimagine the concept of data security considering the insider threat analytics and the significant role it can play ahead. The maturity of Deep Neural Networks now allows data scientists to leverage them in order to build a knowledge base itself by learning what a usual behavior is, and thereby predicting the threat behavior, even though there is no fundamental definition of such behavior. And doing this much ahead of its occurrence in any meaningful way.
The adjacent implementation of LSTM Encoder, a flavor of Deep Recurrent Neural Networks, has been modified at its core to understand, profile the user base, and flag the anomalous behavior of a user associated in the process of accessing sensitive data.
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Infolob’s Data Science Solution Framework
The proposed Infolob’s data science solution framework can be leveraged by any industry or firm to avoid potential insider threats, of any domain, which comprises sensitive data as one of its proprietary in a continuous fashion.
In the following, without the loss of generality, the valid user behavior is unsurprisingly dynamic i.e. a new behavior can be anomalous and/or non-threatening. As this behavior has to be learned by the algorithm in a timely fashion, we have extended the algorithm capability with a continuous learning mechanism, so as to keep the current behavior and flag the rightfully anomalous nature.