Page 35 - Security Today, November/December 2020

to store and how to secure their files. Files get copied, modified, emailed and link- shared. Unstructured data is wild and wooly, and it doesn’t lend itself to careful construction of micro segments.
Privileges. Modifying your team’s access privileges for those easy-to-find and static resources is also not a big problem. But the least-privilege imperative gets way more complicated when the target resource is an individual file. Is it realistic to ask an IT staffer to figure out access control for a specific legal contract or price list, for example? Probably not.
Sound like a tough problem? It is, but don’t despair. Protecting unstructured data is a worthy goal and there are emerging solutions that’ll help us join the zero-trust/least-privilege movement. There are two problems to be solved, and both are unique to unstructured data.
KNOWING WHAT YOU HAVE
“Like we’ve mentioned, traditional zero-trust focuses on resources that are pretty easy to get your arms around.” Unstructured data, on the other hand, is fantastically complex and diverse (see details in this study).
Specialized data, such as a contract or a sales strategy, might be both strategically valuable and difficult for outsiders to understand. To date, pattern matching and end-user file markup techniques have been used to find business-critical data. Neither option is working very well.
KNOWING WHAT TO DO
Developing policies for networked resources, while not easy, is at least manageable. Unstructured data is different. It’s diverse and dynamic, changing with time and business imperatives. Data loss prevention (DLP) technologies take a stab at the unstructured data policy problem, but DLP implementations are highly complex beasts bordering on unmanageable. Knowing what policies to apply to each file is a very tough problem.
ZERO TRUST/LEAST PRIVILEGE WITH DEEP LEARNING
At this point, you might be wondering if there’s any hope for zero-trust/least-privilege approaches. Fortunately, over the last few years deep learning technologies, specifically natural language processing have matured and now offer some exciting new capabilities. The two problems we’ve identified, discovering/ categorizing your data and defining appropriate access policies, are now solvable with automated deep learning solutions.
Deep learning reveals document
meaning and context to provide accurate, granular categories that reflect business criticality. These categories are essential for zero trust security solutions. Deep learning, being far more accurate than pattern matching and far easier to implement than end user classification programs, is the answer.
Once categorized, deep learning can establish a security baseline for each category. That baseline encompasses how files are permissioned, shared, stored, and managed, and it reflects the policy decisions made by the people who know those files best, the owners and end users. From here it is an easy step to find and fix
at-risk files, automatically and accurately. Zero Trust/least-privilege security is possible for unstructured data. By categorizing data and discovering the most appropriate security policies for each file, we’ve kicked away the barriers to effective, efficient and focused security at the file level. We’re finally ready to apply one of the decade’s most powerful security frameworks to the millions
of files and documents our users create and manage every day.
Karthik Krishna is the CEO and co-founder of Concentric AI.
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