What gets measured gets managed, the old saying goes. But it’s hard to measure and manage what you can’t see. For IT executives and the internal teams tasked with protecting and securing the company’s sensitive data, simply understanding what data exists within their universe can seem like an insurmountable challenge.
That’s because organizations are creating and consuming more data than ever. Collectively, they generated more than 44 zettabytes per day in 2020, a figure expected to grow to 175 zettabytes daily by 2025. But the surge in data volume isn’t the biggest data security challenge.
Rather, it’s the fact that data is dispersed across numerous on-premises and cloud environments while taking on a range of forms and formats that make it more difficult for CISOs, CIOs, COOs, and database or IT administrators to protect it. Today, it’s simply harder to accurately and completely identify every byte of data than ever, and any of it left undefined or outside the scope of a company’s data security and privacy efforts increases the risk and likelihood of unauthorized access or use of it.
In response, IT leaders are increasingly recognizing the importance not just of having robust data discovery capabilities, but also the imperative of ensuring they’re discovering, classifying, and remediating data properly and thoroughly. They’re actively seeking solutions and workflows that will give them the clearest, most complete, and accurate picture of their entire data operation.
False positives cost time, money, and productivity
As much as 90% of today’s enterprise data is unstructured — productivity documents, emails, photos, and social media posts — making it harder than ever to discover and index, due in large part to the diverse locations where it lives and the relatively little context traditional discovery tools have to find it.
These days, sensitive data can be anything from credit card data, financial, or other personally identifiable information (PII) to location data, genetic, and even biometric information. The strings of numbers and characters comprising this data all have the potential to match sensitive data formats, dramatically increasing the likelihood of a false positive alert.
False-positive data discovery means a data element is incorrectly identified as meaning one thing when in fact, it means something else. On the surface, false positives may not seem like such a big deal. But for already overwhelmed IT and security teams, wading through a flood of as many as 4,000 alerts per week can result in spending as much as 25% of their time chasing down false positives. That’s hundreds, if not thousands, of hours each year they spend investigating alerts that turn out to be dead ends that can distract them from real security threats and take them away from working on more important, higher-value IT initiatives and efforts.
Though, technology may never completely eliminate false-positive (and false-negative) identification, next-generation discovery solutions like Spirion Sensitive Data Platform brings a new and innovative approach to sensitive data discovery that consistently delivers an industry-leading 98.5% data discovery accuracy rate.
The platform provides a flexible hybrid approach to data discovery and classification with both software-based agents for on-premises servers or endpoints and agentless scanning in the cloud for simplicity, scalability, and performance. Specifically, the solution goes beyond simple pattern matching common to most of today’s discovery tools and applies more advanced techniques like branching algorithms, vector analysis, and regressions analysis to increase detection accuracy — and do it all automatically.
Discovery accuracy drives security effectiveness
Data privacy and protection has never been more important — or more difficult than it is right now. Comprehensive data protection strategies are only effective and impactful when organizations have complete visibility and transparency into their entire data universe.
But with the exponential growth in enterprise data, legacy data discovery tools and methodologies are putting organizations at higher risk of letting hidden bits of sensitive data slip through the cracks undetected and unprotected. Worse, it increases the likelihood of falling out of compliance with a range of data governance standards including GDPR, HIPAA, HITECH, CCPA, PCI DSS, and others that can have disastrous consequences for the company’s bottom line and their brand’s reputation.
Enhancing data discovery accuracy through automated and scalable tools that scour every operating system, cloud, or network-attached storage system is vital for improving your company’s overall data security posture. Not only will it cut down on wasteful false positive alerts and eliminate crucial visibility gaps, but it will also help to completely reshape your entire approach to data privacy and protection and provide unrivaled end-to-end data lifecycle coverage that stands up to the evolving challenges of business in the Digital Age.