Dynamic Data Protection Strategy Based on Risk Scores
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Solution Overview
Problem
Current data protection strategies lack dynamic adaptation to changing situational factors, such as natural disasters and security threats, which can lead to inadequate data backup and recovery plans, potentially resulting in data loss and prolonged recovery times.
Innovation Solution
A computer-implemented method that assesses the criticality of data, computes a risk score based on situational factors from external modules, and dynamically selects and adjusts data protection plans accordingly, allowing for real-time changes in backup frequency and retention periods in response to detected changes in risk levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static data protection plan is used, then operational simplicity is maintained, but adaptability to changing risk conditions deteriorates
Solution Approach 1:
The patent implements dynamic data protection by continuously monitoring risk factors (natural disasters, security threats, system health) and automatically adjusting backup frequency, retention periods, and protection levels based on real-time risk scores. This transforms a static protection plan into a dynamic system that adapts to changing conditions without requiring manual intervention.
Solution Approach 2:
The system incorporates feedback mechanisms by continuously monitoring external and internal risk factors, computing risk scores based on this feedback, and using the scores to adjust protection strategies. This closed-loop feedback system enables automatic adaptation to changing risk conditions while maintaining operational simplicity through automation.
2Reliability
If backup frequency is increased to improve data protection, then data safety is improved, but resource consumption increases
Solution Approach 1:
The patent dynamically adjusts backup frequency based on real-time risk scores. When risk factors are low, backup frequency is reduced to conserve resources. When risk factors increase (natural disasters, security threats), backup frequency automatically increases to enhance data safety. This dynamic adjustment resolves the contradiction by making resource consumption proportional to actual risk levels.
Solution Approach 2:
The system changes key parameters (backup frequency, retention period, protection level) based on computed risk scores. Instead of maintaining constant high-frequency backups regardless of conditions, the system adjusts these parameters dynamically, ensuring data safety when needed while optimizing resource consumption during low-risk periods.
3Measurement precision
If comprehensive risk monitoring is implemented, then risk detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments risk monitoring into distinct categories: external risk factors (natural disasters, security threats, political instability) and internal risk factors (system health, data integrity, performance metrics). Each category is monitored independently with appropriate sensors and data sources, then integrated into a unified risk score. This segmentation improves detection accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The system employs a universal risk scoring mechanism that processes multiple types of risk factors through a common evaluation framework. The same computational engine handles diverse inputs (weather data, security feeds, system logs) and produces a unified risk score that drives protection decisions. This multi-functional approach improves comprehensive risk detection without proportionally increasing complexity.
Data Source
AI summary
Techniques are disclosed for dynamically changing a data protection plan based on a risk score. The risk score is continuously or periodically recalculated based on situational factors that are detected from external modules. The situational factors can include natural phenomena such as weather, fire, and seismic activity. The situational factors can include manmade phenomena such as financial conditions, political stability in the region where the data resides, war, terrorist attacks, and the like. The situational factors are retrieved from one or more external modules. The external modules may be IoT (Internet of Things) modules. The external modules are monitored, and as new data from the external modules becomes available, a risk score for stored data is computed. The risk score is then used to select an appropriate data protection plan.