Deploying effective data security & innovating for a frictionless future
Recorded on Monday, October 28, 2019 at the ISF Annual World Congress webinar, Joel Greenwell and Mark Cassetta discuss the importance of data protection and how machine learning and classification plays a role both today and tomorrow.
Growing repositories of unsecured data and the proliferation of data sharing applications, mobile device and remote access present serious security risks and can leave your organization open to lost intellectual property, significant fines, loss of investor trust, loss of clients, and lawsuits.
No longer can the task of securing data be the sole responsibility of an overworked IT department.
To be successful, everyone in your organization needs to take on a data stewardship role. Data classification remains the first step in getting your teams involved.
Classification was once thought of as a “nice to have” in addition to perimeter defenses like DLP but is finally now seen as foundational to any data security strategy.
This is evidenced by the growing number of data classification solutions on the market today compared to even only 5 years ago.
This proliferation of these tools is, however, a double-edged sword.
While it’s encouraging that organizations are being offered more choice when it comes to classification solutions, the unfortunate fact is that many of these solutions are built as a one-size-fits-all product that do not offer a protection scheme that is tailored to the needs of the organization.
It is with this in mind that Joel Greenwell, Seasoned Enterprise CISO and consultant will discuss challenges around data protection today, while offering real-world advice on how to build a robust data protection plan that goes beyond mere best practices.
Then, Mark Cassetta, (former) VP Strategy at Titus will discuss how machine learning can make implementation frictionless, and almost invisible, to end users while creating even stronger protection. He’ll separate machine learning buzz from reality and explain why and how automated personal data detection can be used as the next starting point to drive classification.