Unstructured data is projected to account for approximately
80% of the data that enterprises will process on a daily basis by 2025. Data breaches and
other security issues get a lot of attention in the media, but all businesses working with data,
especially data in the cloud, are at risk of data loss. Preventing data loss can be difficult for a
number of reasons.
IDG projects that by 2026, there will be 163 zettabytes of data in the world. To put that in
context, one zettabyte is equal to a thousand exabytes, a billion terabytes, or a trillion gigabytes.
The astronomical amount of data transmitting, living, and working in the cloud is just one of
the complications that make securing data a tough task for businesses to manage. Of all the
unstructured data in the world, most of it goes completely unused. According to industry
analysts IDC, more than 90% of unstructured data is never examined. This means large
portions of data float around unsecured and underutilized for many businesses.
That’s why it’s important to understand where unstructured data comes from, why it’s so hard
to pin down, the risks of not securing unstructured data, and the rewards of bringing that data
into a structured environment.
Hiding in plain sight
Unstructured data can come from almost any source. Nearly every asset or piece of content
created or shared by a device in the cloud carries unstructured data. This can include:
Product demo videos on your website
QR codes for discounts and deals on an e-commerce app
Podcasts and other audio blogging files hosted on your website’s blog page
Social media messages on platforms like Facebook, Twitter, and LinkedIn
Internal communications and collaboration platforms are major sources of unstructured data.
Think Slack, Confluence, and other SaaS applications where many people do their daily
work and communicate with colleagues. Most cloud-based applications like these allow
unstructured data to pass through massive networks to be shared, copied, accessed and
stored unprotected.
IDG Communications published an article written by then-Pitney Bowes Software Vice
President Andy Berry in 2018. Berry commented on how the modern workplace approaches
data and why these norms contribute to the data loss problem, citing one study that found
enterprises using almost 500 unique business applications. SaaS applications generate data
that can quickly become obsolete, unusable, and eventually inaccessible.
Data powers everything we do in our professional and personal lives, but with little to no
oversight on data hygiene, we often miss out on key opportunities to improve security
blindspots and maximize data performance.
A complex problem
The various sources of unstructured data show how complex data loss can be. Many
problems with DLP start with the three V’s of data — volume, velocity, and variety. It’s hard for
humans and manual review to keep up with the staggering amount of data, speed of data
proliferation, and the many different sources of data.
Adding to the problem is the fact that unstructured data is very difficult to organize. It’s
impossible to dump every piece of unstructured information into a database or spreadsheet,
because that data comes from myriad different sources and likely doesn’t follow similar
formatting rules. On top of that, finding unstructured data through manual processes would
take more time than there are hours in the day. It’s not a job for humans.
Other roadblocks to unstructured data collection include increasingly stringent privacy regimes,
laws that protect intellectual property (IP) and other confidential or proprietary information like
trade secrets, and businesses communicating across different security domains between the
cloud and traditional hard-drive based storage systems. Information security is evolving at
lightning speeds, but some schools of thought are still based on older priorities that focus on
preventing outsider threats. It’s important to protect an organization from malicious actors, but
what about good-natured, everyday workers who don’t know what they don’t know? That can
still hurt an organization in tremendous ways.
Unstructured data isn’t all bad news. It can also be an opportunity for organizations that can
recognize two main ideas. First, that this data must be gathered, protected, and understood.
Second, that there’s value in all the data that is currently going unused. Computer Weekly
cited sources that estimate modern businesses are
utilizing as little as 1% of their unstructured data.
Our world runs on data, and each person interacting with apps, platforms, and devices
contributes to the growing data reserves. When organizations think about gathering data to
help with marketing, business intelligence, and other key functions, they must also factor in
the impact of unstructured data. Unstructured data presents equal risk and opportunity for
business leaders. When that data lives in the darkness, its only impacts are negative.
But when data is brought into the light, we can use that data to be smarter and better at work.
Solving the unstructured data problem
Unstructured data is a major concern for organizations using cloud-based collaboration and
communications platforms. Productivity relies on environments where co-workers can share
ideas and messages quickly, without fear of exposing sensitive data. Nightfall, a data loss
prevention (DLP) solution, provides much-needed security for today’s most used
communications and collaboration platforms like Slack, Confluence, and many other popular
SaaS & data infrastructure products.
Since these applications lack an internal DLP function, and each allows for the lightning-fast
transmission of massive amounts of data, Nightfall’s machine learning based platform is an
essential partner for many organizations handling sensitive information like PII (personally
}identifiable information), PHI (protected health information), and other business-critical secrets.
Nightfall’s three step approach allows businesses to discover, classify, and protect
unstructured data through artificial intelligence (AI) and machine learning (ML). Our solution
makes sense of unstructured data, while traditional security solutions solely rely on users to
help categorize data through methods like regular expressions (regex), which have limited
accuracy in unstructured environments.
Each step of Nightfall’s ML solution is critical to the process of DLP. Discover means a
continuous monitor of sensitive data that is flowing into and out of all the services you use.
Classify means ML classifies your sensitive data & PII automatically, so nothing gets missed.
Protect means businesses can set up automated workflows for quarantines, deletions, alerts,
and more. These three arms of DLP save you time and keep your business safe — all with
minimal manual process or review oversight from you or your staff.
Helping businesses identify and access unstructured data
Data is a part of life, especially as remote work becomes an essential function for productivity
and collaboration. Business leaders must understand the risk of ignoring unstructured data and
the value of making that data work for the business. It’s a tall order to identify and bring in a
mass of unknown data to the cloud, but the rewards come with a better understanding of your
organization, your industry, and your customers. Good things can come from unstructured
data — as long as you’re ready to approach the issue with a solid data strategy and a
knowledgeable DLP partner like Nightfall.
About Nightfall
Nightfall is the industry’s first cloud-native DLP platform that discovers, classifies, and protects data
via machine learning. Nightfall is designed to work with popular SaaS applications like Slack & GitHub
as well as IaaS platforms like AWS. You can schedule a demo with us below to see the Nightfall platform
in action.
“This article is originally posted on Nightfall.ai”
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