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 various data policies due to data sprawl, attack surface expansion, and retention policy violations, leading to increased risks of data breaches and non-compliance.
Innovation Solution
A content classification system that classifies and modifies digital content items to satisfy data policies by generating classifications, mappings, and implementing downstream operations to remediate violations, providing efficient and flexible management of computing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic classification of digital content items is implemented, then compliance with data policies is improved, but device complexity increases
Solution Approach 1:
The system segments the classification process into multiple stages: accessing digital content items, generating classifications based on data elements, generating mappings between content items and data policies, and detecting violations. This segmentation allows complex compliance checking to be broken down into manageable operations that can be performed systematically across large datasets.
Solution Approach 2:
The system dynamically generates classifications and mappings based on the actual content items and data policies encountered. Rather than using static pre-defined categories, the system adapts its classification scheme to the specific data elements and policy requirements, enabling flexible compliance checking across diverse digital environments without requiring exhaustive pre-programming of all possible scenarios.
2Measurement precision
If comprehensive monitoring of data locations and usage is implemented, then detection of policy violations is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary classification of digital content items and generation of mappings between content items and data policies before violation detection occurs. By pre-processing the data to establish classifications and mappings in advance, the system can quickly identify violations without needing to perform comprehensive analysis every time a policy check is required, thus reducing real-time processing time while maintaining detection accuracy.
Solution Approach 2:
The system continuously maintains updated classifications and mappings as data policies and content items change, ensuring that compliance monitoring remains effective without requiring repeated comprehensive reanalysis. This continuous maintenance approach allows the system to detect violations efficiently using pre-established frameworks that are constantly refined, rather than performing time-consuming full re-evaluations for each policy check.
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.