Metadata Sampling for Policy Violation Detection

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Solution Overview

Problem

Large enterprises face challenges in efficiently identifying and remediating policy violations within vast unstructured data sources, as traditional methods require analyzing every document, which is impractical due to the volume, variety, and complexity of unstructured data, leading to high costs and inefficiencies.

Innovation Solution

A method that uses metadata sampling and analysis to predict potential policy violations, reducing the need to examine all documents by initializing random buckets, computing a metadata sample size, collecting metadata, and scoring documents against a policy compliance catalog, thereby identifying hotspots for deeper analysis and remediation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional methods analyze every document to identify policy violations, then measurement precision is improved, but productivity deteriorates due to the vast volume of unstructured data

Engineering Contradiction:

Improvepolicy violation detection accuracyVSAvoiddocument processing speed

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent segments the large unstructured data set into manageable portions by using metadata to identify and isolate high-risk document subsets. Instead of processing all documents uniformly, the system divides the data into segments based on metadata characteristics that correlate with policy violation risk, allowing focused analysis on relevant portions while maintaining detection accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces metadata as an intermediary layer between the raw unstructured data and the policy violation detection process. This metadata intermediary enables the system to predict which documents are likely to contain violations without examining every document's full content, thus improving productivity while maintaining measurement precision through the metadata-based prediction model.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If metadata sampling is used to predict policy violations, then productivity is improved by reducing analysis scope, but measurement precision deteriorates due to sampling limitations

Engineering Contradiction:

Improvecompliance monitoring efficiencyVSAvoidpolicy violation prediction accuracy

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The patent applies preliminary action by collecting and analyzing metadata from a sample of documents before conducting full-scale policy violation detection. This preliminary metadata analysis establishes baseline characteristics and risk patterns that guide subsequent full-document analysis, improving overall efficiency while maintaining precision through the informed sampling approach.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates feedback mechanisms where the results from metadata sampling and initial document analyses are used to refine the prediction model. The feedback loop continuously improves measurement precision by adjusting the sampling strategy and metadata features based on actual violation detection outcomes, ensuring that productivity gains do not compromise accuracy.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If random bucket sampling is implemented, then ease of operation is improved through simplified sampling, but loss of information increases due to potential violation misses

Engineering Contradiction:

Improvesampling process simplicityVSAvoidpolicy violation data completeness

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The patent applies parameter changes by dynamically adjusting the sampling parameters (such as sample size, confidence levels, and risk thresholds) based on the specific data set and compliance requirements. This allows the system to maintain ease of operation through automated parameter selection while minimizing information loss by adapting the sampling intensity to the risk profile of the documents being analyzed.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12124490B2Predicting policy violations in a document with an enterprise data source

Publication Date: 2024.10.22 INTERNATIONAL BUSINESS MACHINE CORPORATION

AI summary

Predicting potential policy violations in a given data source (such as a set of documents) so that a deeper analysis can be performed on the set of documents to gain additional insights into the potential policy violation(s) that may be contained therein. In some instances, this prediction is done by performing a random sampling operation on the set of documents and collecting the metadata from these documents in order to determine whether or not the set of documents that are randomly sampled include a policy violation that can be remediated.