Data breaches have become a part of life. They impact hospitals, universities, government agencies, charitable organizations and commercial enterprises. In healthcare alone, 2020 saw 640 breaches, exposing 30 million personal records, a 25% increase over 2019 that equates to roughly two breaches per day, according to the U.S. Department of Health and Human Services. On a global basis, 2.3 billion records were breached in February 2021.
It’s painfully clear that existing data loss prevention (DLP) tools are struggling to deal with the data sprawl, ubiquitous cloud services, device diversity and human behaviors that constitute our virtual world.
Conventional DLP solutions are built on a castle-and-moat framework in which data centers and cloud platforms are the castles holding sensitive data. They’re surrounded by networks, endpoint devices and human beings that serve as moats, defining the defensive security perimeters of every organization. Conventional solutions assign sensitivity ratings to individual data assets and monitor these perimeters to detect the unauthorized movement of sensitive data.
It’s painfully clear that existing data loss prevention (DLP) tools are struggling to deal with the data sprawl, ubiquitous cloud services, device diversity and human behaviors that constitute our virtual world.
Unfortunately, these historical security boundaries are becoming increasingly ambiguous and somewhat irrelevant as bots, APIs and collaboration tools become the primary conduits for sharing and exchanging data.
In reality, data loss is only half the problem confronting a modern enterprise. Corporations are routinely exposed to financial, legal and ethical risks associated with the mishandling or misuse of sensitive information within the corporation itself. The risks associated with the misuse of personally identifiable information have been widely publicized.
However, risks of similar or greater severity can result from the mishandling of intellectual property, material nonpublic information, or any type of data that was obtained through a formal agreement that placed explicit restrictions on its use.
Conventional DLP frameworks are incapable of addressing these challenges. We believe they need to be replaced by a new data misuse protection (DMP) framework that safeguards data from unauthorized or inappropriate use within a corporate environment in addition to its outright theft or inadvertent loss. DMP solutions will provide data assets with more sophisticated self-defense mechanisms instead of relying on the surveillance of traditional security perimeters.
PlatoAi. Web3 Reimagined. Data Intelligence Amplified.
