Cloud-Based Data Collection Tool for Secure E-Discovery
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Solution Overview
Problem
Current electronic data collection systems face issues such as manual installation requirements, security concerns, inefficiencies due to specialized knowledge needs, excessive data collection beyond what is requested, and limited accessibility and portability of non-cloud storage systems, leading to disproportionate costs and accessibility challenges in eDiscovery processes.
Innovation Solution
A cloud-based data collection system generates a customized collector based on specific requirements, allowing for secure, autonomous data collection from target systems, storing data in cloud storage, and providing a user-friendly interface for determining data types, resources, and reporting, while ensuring data integrity and security through encryption and unique deployment keys.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual installation is required for data collection systems, then system control and security management are improved, but installation complexity and time consumption increase
Solution Approach 1:
The data collection system performs self-installation by automatically deploying collection agents to target devices without requiring manual intervention. The system autonomously configures data collection parameters, establishes communication channels, and begins data gathering operations, thereby eliminating the need for specialized administrators while maintaining security through automated authentication and authorization protocols.
Solution Approach 2:
A cloud-based deployment server acts as an intermediary between the data collection system and target devices. This intermediary automatically manages the installation process by transmitting collection agents to target devices, handling authentication, and coordinating data flow, thereby reducing both installation complexity and potential security risks associated with manual installation.
2Loss of information
If all data is collected from target systems, then data completeness is improved, but data storage costs and processing time increase
Solution Approach 1:
The system implements selective data collection by configuring different collection parameters for different data sources and types. Collection agents are customized to gather only relevant data based on local requirements at each target device, such as specific file types, time ranges, or data categories, thereby maintaining necessary data completeness while significantly reducing overall data volume and associated storage and processing costs.
Solution Approach 2:
The system employs partial data collection by gathering only the subset of data that is actually needed for the specific eDiscovery case at hand. Rather than collecting all possible data from target systems, the configuration allows for targeted collection based on relevance criteria, legal requirements, and case priorities, thus avoiding the excessive action of collecting unnecessary data that would increase storage and processing burdens.
3Reliability
If non-cloud-based storage is used, then data security and control are improved, but accessibility and portability decrease
Solution Approach 1:
The system implements a hybrid storage architecture that combines cloud-based storage with centralized control mechanisms. Collection agents operate autonomously at local devices while reporting to a cloud-based repository, enabling multiple functions including secure data storage, remote accessibility, automated backup, and centralized management. This universal approach allows the system to maintain data control through encryption and access policies while simultaneously providing enhanced accessibility and portability through cloud infrastructure.
Data Source
AI summary
An approach is provided for generating a customized, cloud-based data collection tool for collecting data from computer resources of a target system. In an embodiment, the method comprises: receiving a request to perform a data collection from one or more target computer resources; wherein the request includes one or more requirements that are specific to the data collection; based on, at least in part, the requirements, generating a customization specification for generating a customized collector that is specific to the data collection to be performed on the target computer resources; and transmitting the customization specification to a deployment engine to cause the deployment engine to: based on, at least in part, the customization specification, generate the customized collector that is specific to the data collection to be performed on the target computer resources; and transmit the customized collector, for generating the customized collector, to a cloud storage for storing.