Dynamic Data Classification for Policy Violation Remediation
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Solution Overview
Problem
Large organizations face challenges in managing digital data to comply with diverse data policies due to data sprawl, attack surface expansion, and retention policy violations, leading to increased security risks and compliance issues, particularly in complex computing systems with varied data assets and locations.
Innovation Solution
A content classification system that classifies and modifies digital content items to ensure compliance with data policies by generating mappings between content items and policies, detecting violations, and implementing corrective operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dynamic classification of digital content items is performed to ensure compliance with data policies, then compliance accuracy is improved, but computational load increases
Solution Approach 1:
The system segments the classification task by processing digital content items in batches rather than individually, and by dividing the classification into stages: initial filtering based on metadata, followed by deeper analysis only for items that pass the first stage. This segmentation reduces overall computational load while maintaining compliance accuracy.
Solution Approach 2:
The system applies partial action by performing comprehensive classification only on a subset of digital content items that are most likely to violate policies, rather than exhaustively analyzing all items. The system uses heuristics and risk scoring to identify high-risk items for deep analysis, reducing computational resources spent on low-risk items while maintaining detection accuracy.
2Measurement precision
If comprehensive scanning of digital content items is performed to detect policy violations, then detection accuracy is improved, but processing time increases
Solution Approach 1:
The system performs preliminary action by conducting rapid metadata-based filtering and risk assessment before performing comprehensive content analysis. Items are pre-sorted by risk level, and only high-risk items undergo full scanning, significantly reducing processing time for the majority of low-risk items while maintaining high detection accuracy for violations.
Solution Approach 2:
The system implements periodic scanning at different intervals for different types of digital content items, rather than continuous scanning. High-risk items are scanned continuously or at frequent intervals, while low-risk items are scanned periodically or only when changes are detected, reducing overall processing time while maintaining effective violation detection.
3Adaptability or versatility
If multiple data policies are applied to diverse digital content items, then compliance coverage is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by implementing a single multi-functional classification framework that can handle multiple data policies and diverse digital content types through a unified architecture. The same core algorithms and processing pipeline adapt to different policies and content types, eliminating the need for separate specialized systems for each policy or content type, thus reducing overall system complexity.
Solution Approach 2:
The system manages complexity by dynamically changing classification parameters and thresholds based on the specific data policy and content type being processed. Rather than maintaining fixed complex rules for each policy, the system adjusts its analysis depth, metadata fields to examine, and risk criteria based on input parameters, simplifying the underlying system architecture while maintaining broad compliance coverage.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for managing computing systems to classify and modify digital content items to satisfy digital data requirements of data policies. For example, the content classification system validates, enforces, and remediates digital data content corresponding to digital data requirements of a data policy based on data types covered by the data policy. The disclosed systems generate classifications for digital content items by accessing digital content items and generating mappings between the digital content items and a data policy. The disclosed systems utilize the mappings and digital data requirements of the data policy to determine whether the digital content items violate one or more elements of the data policy. The disclosed systems can perform various downstream operations to remediate the data policy violations, such as by causing various computing devices to modify the violating digital content items.