Human Error, Complex Global Regulations and Deluge of Data Continue to Challenge Data Protection and Security Initiatives Worldwide
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.
5 Reasons #dataprotection fails without #machinelearning (via @TITUS)
TITUS, 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 Protection Strategy
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 significant challenge.
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.
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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 data.
- 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.