Facing a growing cascade of regulations and public pressure,
organizations know that having a strong, end-to-end data protection
strategy is a critical priority. That said, as organizations continue to
invest heavily in data protection solutions, many still struggle to
achieve that goal. The challenges organizations commonly face become
more pressing and complex, yet the resources and time available to solve
them remain static. The answer is something many organizations have done
to address similarly complex challenges in data management and data
analysis – the adoption of machine learning capabilities.
a leading provider of data protection solutions and a Blackstone
portfolio company, has identified five common reasons data protection
strategies fail without implementing machine learning capabilities.
Five Reasons Machine Learning is Critical to a Successful Data
1. Human beings make mistakes. As end users create the data an
organization seeks to protect, the belief is they are the best source to
analyze how valuable their data is and the best security to apply.
However, this isn’t always true. End users can make mistakes. Many
times, this means they may not apply stringent enough protection to
their data or, more commonly, apply strict protections to data that
isn’t critical to the organization.
2. More global regulations create complexity and confusion. The
introduction of the General Data Protection Regulation (GDPR) sparked a
worldwide movement to address growing public concern as to how
businesses treat sensitive and/or personal data. While this is a
positive step in ensuring businesses become good data stewards, it also
creates complexity, as these businesses must understand what sensitive
data they have, where it resides, and how it is protected to ensure they
are compliant with a growing list of regulations. As each regulation has
unique attributes, ensuring compliance on a continuous basis remains a
3. Explosion of data is difficult to identify and manage. Multiple
sources indicate that the amount of data created and consumed daily will
continue to increase exponentially for the foreseeable future.
Organizations continue to heavily invest in technology to manage and
analyze this data, but protecting this data remains challenging.
4. Traditional solutions are often inaccurate. Existing
traditional methods to identify and apply context to data include
Regular Expressions for data like SSNs or credit card numbers. Though
these are widely used, organizations regularly report issues with
accuracy and false positives. These methods are limited in terms of what
data can be reported against, , creating gaps in organizational
knowledge as to what data is truly sensitive.
5. Fewer resources and less time. Organizations worldwide grapple
with finding skilled security professionals, which hinders the ability
to deploy new strategies and technologies. Additionally, security and IT
professionals are responsible for a myriad of projects and activities,
leaving little time to ensure end users are consistently applying and
adhering to data protection and security policies.
Machine learning offers a new way of thinking about data protection
Deploying machine learning as a part of an organization’s overall data
protection strategy can provide the critical assistance users need to
apply the proper safeguards to data they’ve created without adding
friction to their day-to-day activities. For organizations ready to
adopt a more mature machine learning posture, end users could be
removed from the equation while increasing confidence in the
organization’s ability to identify, contextualize and protect critical
TITUS’ award-winning TITUS
Intelligent Protection enables organizations using the company’s
industry-leading TITUS Classification Suite and TITUS Illuminate
solutions the ability to build and deploy machine learning
capabilities based on company-specific data protection needs while
providing additional consistency and accuracy to data security efforts.
“It’s common to hear people refer to data as the ‘new oil,’ and they
aren’t wrong. Data is such a critically important asset for any
organization, yet most continue to struggle with data protection,” said
Mark Cassetta, senior vice president of strategy, TITUS. “In the past,
vendors including TITUS have championed the user as a critical component
of a successful data protection strategy. Users can continue to play a
central role in an organization’s data protection strategy, but they
need help. Leveraging machine learning represents a new way to improve
and further automate the user experience while increasing accuracy.
Organizations that don’t believe machine learning will change the way
they protect sensitive data will miss a critical opportunity to
accelerate their adoption of a successful data protection strategy.”
Learn more about TITUS
Blog Post: “Machine
Learning: Data Protection’s Next Frontier”
Blog Post: “When
it Comes to Data Protection, Should You Crowdsource Your Security?”
Blog Post: “Machine
Learning and Its Impact on How We Work”
5 Reasons #dataprotection fails without
#machinelearning (via @TITUS): http://bit.ly/mltopad42319
TITUS is a leader in providing solutions that
enable businesses to accelerate their adoption of data protection. The
company’s products enable organizations to discover, classify, protect,
analyze and share information. With an open, intelligent policy manager,
TITUS customers are also able to address regulatory compliance
initiatives and get more out of their existing security investments,
including data loss prevention (DLP), cloud access broker (CASB),
encryption, and next-generation firewall (NGFW) solutions. Millions of
users in over 120 countries trust TITUS to keep their data compliant and
secure, including some of the largest financial institutions and
manufacturing companies in the world, government and military
organizations across the G-7 and Australia, and Fortune 2000 companies.
More information is available at www.titus.com.