Sensitive data discovery is the process of identifying, classifying, and masking PII, conducted by companies to ensure compliance with data privacy laws.
Sensitive data discovery is the process of automatically locating, selecting, and obscuring Personally Identifiable Information (PII), performed by enterprises to ensure compliance with data protection legislation.
If your organization's structured and unstructured data stores represent a vast digital haystack, then sensitive data is the needle. From financial records to health information, from Social Security Numbers to credit card details – any type of PII or other sensitive data could be hidden anywhere within this haystack.
Sensitive data discovery is essential to finding those needles.
Sensitive data discovery is crucial because it helps ensure compliance with regulations like GDPR, CPRA, HIPAA, and more. Each law stipulates strict guidelines for obfuscating sensitive data by a variety of different methods, including PII masking, anonymization vs encryption, data tokenization, and synthetic data generation.
Moreover, by identifying and protecting sensitive data, companies can mitigate the risks of data breaches and leaks, which can have devastating financial and reputational consequences.
Finally, sensitive data discovery reduces the risk of potential insider threats by uncovering PII that unauthorized users shouldn't see.
The process of discovering sensitive data goes beyond simply locating data. Discovery also involves classifying its sensitivity level – which empowers organizations to prioritize their protection efforts. Yet, with data volumes pushing the Zettabyte range, traditional methods of sensitive data discovery and classification are falling short.
In addition to the vast amount of data that organizations store, data can be structured (in linear tables) or unstructured (in files or emails). It can reside in multiple enterprise systems spread across different geographies or business units. And it’s subject to a series of data regulations defining what information is considered sensitive and how it needs to be handled.
Additionally, accurately identifying sensitive data can be tricky – at scale, it’s hard to tell whether a 9-digit figure is a Social Security Number or just a random sequence. And user behavior also adds complexity. For example, employees might share or move sensitive data without proper authorization, making tracking more difficult.
Despite these challenges, ongoing PII discovery is vital for robust data security. That’s why organizations are increasingly looking into leveraging automation and AI technologies to search through massive datasets faster and more effectively.
Sensitive data discovery lets you:
Map your data landscape
You get a clear picture of your vulnerabilities by pinpointing where all sensitive data resides, from enterprise systems to vector databases. You also eliminate blind spots and ensure no critical data is overlooked.
Comply with regulations more easily
With all the different regulations, keeping up with what constitutes sensitive data can be challenging. Discovery tools can automate this process, ensuring all sensitive data is classified and protected correctly.
Strengthen your data security
By understanding what data is considered sensitive, you can prioritize its protection. This proactive approach minimizes the chances of unauthorized access, data breaches, and potential financial and reputational damage.
Respond more quickly to incidents
If a breach occurs, you can quickly identify the location and scope of the compromised data, allowing for faster containment and remediation – minimizing its impact on customers and stakeholders.
Reveal hidden insights
By uncovering sensitive data that may have been forgotten or misplaced, you can streamline the data management process and identify opportunities for data minimization.
Finding sensitive data within an organization was once a slow and error-prone manual process.
It involved interviewing various departments to identify data sources, collecting data from them, and manually compiling an inventory. Next, access permissions for each dataset had to be documented. Finally, the entire process needed to be reviewed.
Innovative approaches to sensitive data discovery and masking are looking to AI for answers.
Unlike traditional methods, AI-based data masking tools would operate continuously and would be more accurate and context-aware. They’d also be able to raise immediate alerts when identifying data stored improperly or used in a way that deviates from data masking best practices.
However, even with the latest automation and AI technologies, sensitive data discovery tools don’t easily understand the context of what constitutes PII. Having a human in the loop remains crucial in confirming sensitive data based on context and in connecting all the pieces.
Data masking is one of the most effective methods for protecting PII.
Data masking solutions allow companies to maintain referential integrity while maximizing data usability. And advanced techniques like dynamic data masking help strike the right balance between data protection and access.
The best approach to sensitive data discovery and compliance leverages data masking technology based on business entities.
Entity-based discovery tools identify PII within the context of each business entity (an individual customer, product, order, etc.) allowing for exceptional accuracy in pinpointing sensitive data and preventing accidental exposure.
Entity-based data masking software discovers, ingests, organizes, and masks sensitive data on the fly. This real-time capability empowers authorized users to work with masked entity data while safeguarding sensitive information and maintaining compliance.
Learn more about K2view data masking tools with PII discovery built in.