Improve Data Security through Data Aggregation Purge Process

“Data is money”, in today's digital economy, data is often equated to money due to its immense value in informing decisions, driving strategies, and personalizing customer experiences. Organizations harness data to gain competitive advantages, improve operations, and create new revenue streams. However, there are challenges also in keeping this data safe, even after its use. The Identity Theft Resource Center (ITRC) reported that the number of data breaches in 2022 increased by 17% compared to 2021. This trend indicates a rising number of incidents each year. Expecting data left on the shelf to continually yield more valuable insights after every reprocessing, risks organizational resources unnecessarily. The costs and risks associated with safeguarding dormant data often outweigh the potential benefits, suggesting a need for a more strategic approach to data management and analysis.
This innovative futuristic model suggests a mutually beneficial solution for both data consumers and providers. It proposes that after data is analyzed and insights are generated, the original source data can be deleted to ensure privacy and security. This allows for the retention of analytical results for future reference or analysis, without the need to store the original data, addressing concerns about data security and privacy while maintaining the utility of the analysis.
This out of the box solution evolves in step-by-step process to eliminate the need of original data after its re-processing and valuable insight creation, the dormant data is suppressed from data retention process. This solution is simply based on the fundamental principle “less data less risk”. By significantly reducing the need of original data after its use, tremendously improves data security and lowers the overall data breach possibilities.
The effectiveness of this innovative model is critically dependent on the effectiveness of “Insight Registry” and “Aggregated Metadata” components. They are strategically built on the type and classification of data.
By eliminating redundant data, we reduce the 'attack surface' that could potentially be exploited in breaches, thereby protecting the integrity of the system and, in broader applications, safeguarding human lives by preventing sensitive information from falling into the wrong hands.