Unified DPaaS Platform for Predictive Data Protection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data protection practices within organizations often result in redundant systems and infrastructure, leading to inefficiencies and higher costs, while also making organizations vulnerable to internal and external data breaches due to the lack of a standardized, secure framework.
Innovation Solution
A data protection framework provided as a software as a service (DPaaS) that utilizes a data protection policy determination machine learning model to generate dynamic data protection policies, integrating predictive data protection capabilities with uniform interfaces to ensure consistent security and governance across various data uses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If organizations implement multiple separate data protection systems, then data protection coverage is improved, but system complexity and cost increase
Solution Approach 1:
The patent combines multiple data protection functions (backup, recovery, security, compliance) into a single unified DPaaS platform. The system integrates diverse data protection capabilities through common infrastructure, shared management interfaces, and coordinated processing pipelines, eliminating the need for separate standalone systems while maintaining comprehensive protection coverage.
Solution Approach 2:
The DPaaS system is designed as a multi-functional platform that can perform various data protection operations (backup, restore, security scanning, policy enforcement) across different data types and storage locations. The system provides universal data protection services through standardized interfaces and adaptive processing that works across heterogeneous environments.
2Reliability
If organizations implement multiple separate data protection systems, then data protection coverage is improved, but operational cost increases
Solution Approach 1:
The patent consolidates multiple data protection systems into a single unified platform, reducing operational costs by eliminating redundant infrastructure, shared software licenses, and multiple management overheads. The system achieves comprehensive data protection coverage while lowering total cost of ownership through resource consolidation and efficient utilization.
3Reliability
If data protection policies are updated frequently to address new threats, then security effectiveness is improved, but system stability deteriorates
Solution Approach 1:
The patent implements dynamic data protection policies that can be updated and adapted in response to emerging threats and changing requirements. The system allows policy modifications without complete system reconfiguration, enabling flexible security adjustments while maintaining operational stability through gradual updates and version control mechanisms.
Solution Approach 2:
The system incorporates feedback mechanisms that monitor security threats, policy effectiveness, and system performance. Based on this feedback, the system automatically adjusts and refines data protection policies, enabling continuous improvement of security effectiveness while maintaining system stability through controlled, evidence-based policy evolution.
4Measurement precision
If manual review of data protection instructions is performed, then policy accuracy is improved, but processing time increases
Solution Approach 1:
The patent implements automated systems that self-generate, self-review, and self-validate data protection policies using machine learning models, rule engines, and automated testing. The system performs autonomous policy analysis and validation without requiring extensive manual intervention, achieving high policy accuracy through automated verification while maintaining rapid processing speeds.
Solution Approach 2:
The system incorporates automated feedback loops that validate policy correctness, check for conflicts, and verify compliance requirements. This automated feedback mechanism provides rapid policy verification with high accuracy, eliminating the time-consuming nature of manual review while maintaining or improving policy quality through systematic automated checking.
Data Source
AI summary
Embodiments of the present disclosure provide methods, apparatus, systems, computing devices, and computing entities for predictive data protection using a data protection policy determination machine learning model. In one embodiment, a method is provided comprising: processing a historical data corpus using the data protection policy determination machine learning model to generate a dynamic data protection policy update describing inferred data protection instructions; determining an attestation subset of the inferred data protection instructions by comparing the instructions and prior data protection instructions described by an existing data protection policy; for each inferred data protection instruction in the attestation subset, determining a per-instruction attestation determination based on end-user feedback; generating an updated data protection policy by updating the existing policy in accordance with each inferred instruction in the attestation subset whose per-instruction attestation determination describes an affirmative attestation determination; and performing the predictive data protection using the updated data protection policy.