Node Sanitation Copilot (NSC) – A Front-End Approach to Comprehensive Data Observability

 Abstract

Data observability solutions are traditionally built to monitor backend systems, ensuring compliance, performance, and data integrity within a predefined tech stack. However, these solutions overlook the critical aspect of data transfer and duplication at the front end, where data travels between user nodes and endpoints. Node Sanitation Copilot (NSC) leverages task mining and remote capture technologies to provide real-time, UI-layer visibility into data classification, redundancy management, and access control, making it a pioneering solution in front-end data observability. This paper discusses the architecture of NSC, contrasts its functionality with traditional backend-focused observability tools, and explores novel applications where NSC can offer superior infrastructure support.

1. Introduction

As digital data grows exponentially within organizations, tracking its journey from creation to deletion has become more complex. Data observability tools have evolved, primarily focusing on backend systems and offering robust solutions for predefined technology stacks. However, these tools fall short in providing insights into front-end user activities where data leakage risks are more prevalent. The NSC aims to fill this gap by integrating task mining and remote capture into its framework, offering seamless data observability across the front-end ecosystem.

 

 

2. Challenges in Traditional Backend Data Observability

Traditional data observability systems are focused on backend infrastructure, offering limited coverage over the entire data lifecycle. Some key limitations include:

2.1 Limited Front-End Visibility

Traditional systems are constrained to the backend, making it difficult to track data activities such as duplication or unintentional leakage via user actions at the front end (e.g., emails, cloud storage transfers).

2.2 Dependency on a Fixed Tech Stack

Most backend observability tools are designed for specific databases, frameworks, or programming environments, which makes them hard to adapt across diverse platforms and endpoints in modern organizations.

2.3 Inability to Detect Unauthorized Transfers

Backend-focused tools typically do not monitor or flag risky data transfers originating from the user interface, such as emails sent to external domains, cloud sharing, or data copied into unauthorized media.

2.4 Delayed Incident Response

Since backend observability tools rely on logs and data from servers, detection of improper access and data breaches is often delayed, impacting real-time incident response.

3. NSC: Architecture and Key Features

3.1 Task Mining for Organizational Data Observability

Task mining is deployed across the entire organization, enabling comprehensive tracking of user interactions with data. This process ensures that every touchpoint between users and datasets is captured in real time, enhancing data observability on the front-end UI layer.

3.1.1 Data Collection and Trigger Mechanisms

Sensitive datasets within the organization are defined and prioritized based on policies established by the Data Loss Prevention (DLP) system. For each of these datasets, NSC sets up trigger mechanisms. These triggers are designed to activate whenever specific actions are taken on these datasets, such as viewing, editing, or sharing.

Once a dataset trigger is activated, task mining captures the subsequent steps taken by the user. This can include whether the dataset is saved to a local or remote location, shared through email, WhatsApp, or Teams, or copied to an external device. NSC does not rely on external APIs from these communication platforms; instead, it leverages the depth of task mining tools (such as those provided by Epiplex), capturing the names of recipients and other metadata. This allows for precise monitoring without requiring direct access to the APIs of communication tools, enhancing security and privacy while still delivering detailed insights.

3.1.2 Collation and Analysis of Interaction Data

The data gathered through task mining is collated into a centralized system where NSC analyzes interactions at the organizational level. This analysis provides insights into data flows between users and nodes, identifying any abnormal patterns that might indicate potential data breaches or non-compliance with DLP policies. The system's comprehensive nature allows for real-time reporting, ensuring that unauthorized data transfers are flagged immediately and escalated for review by system administrators.

3.2 Remote Capture Client for Continuous Monitoring

NSC's remote capture component serves as the client-side tool that resides on each user's machine. This client acts as the key interface for data monitoring, collecting critical front-end activity data and relaying it to the central observability system.

3.2.1 Client Deployment and Trigger-Based Activation

The remote capture client can be deployed across an organization in various ways. It can be configured to start automatically when the system is powered on, ensuring continuous data capture from the beginning of a user session. Alternatively, administrators can schedule the client to run during specific time windows or based on certain triggers (e.g., when a sensitive dataset is accessed).

3.2.2 Interaction with Task Mining

While task mining handles the logic of identifying sensitive datasets and monitoring interaction steps, the remote capture client handles data collection and reporting from the user’s system. It records the sequence of actions and supports task mining’s deeper analysis by relaying this data in near real-time to a centralized server. This allows the system to monitor and capture even transient data interactions, such as copying files to USB drives or pasting sensitive content into an unmonitored application.

3.3 Data Redundancy Management and Review Process

Data redundancy in organizations often leads to wasted storage and higher risks of data exposure. NSC tackles redundancy through periodic reviews, ensuring that redundant or outdated data is appropriately managed without violating DLP policies.

3.3.1 Scheduled Redundancy Audits

Organizations can schedule audits on a monthly, quarterly, or custom basis to check for redundant or duplicated data across employee machines. Rules are defined within NSC’s central console to prioritize data retention based on user roles. For instance, data on higher-ranking employees' systems may be deemed more critical and retained, while redundant data on lower-ranked employee machines is flagged for deletion.

3.3.2 Review Process and Local Deletion

Once data is flagged for deletion, NSC’s workflow temporarily stores the data in a frozen state on a secure server. This frozen database retains the files for a set period, during which users may request to keep or restore the data. This process adheres to DLP policies by enforcing a formal approval process before allowing any data to be kept locally.

3.3.3 User Interaction with Data Deletion Alerts

Whenever local data is deleted—whether through redundancy management or triggered data loss prevention—users are presented with a startup splash screen the next time they log into their machine. This splash screen provides a detailed breakdown of the deleted files, including file paths, names, and other metadata. Users can initiate a data retrieval request directly from this screen, which automatically triggers the approval workflow required by DLP policies.

3.4 Dynamic RPA Script Generation

NSC dynamically generates RPA scripts for automated data handling tasks. These scripts are particularly useful in automating data deletion or hiding, reducing the manual effort needed to ensure DLP compliance.

3.4.1 Sensitivity-Based Data Handling

NSC structures organizational data into databases that assign a sensitivity index to each file. This index determines the action that will be taken by the RPA script:

• Immediate Deletion: For files marked as highly sensitive, RPA scripts execute real-time deletion commands (e.g., using the Windows Terminal).
• Hidden State: Moderately sensitive files can be temporarily hidden, limiting access while preserving the file for organizational use.
• Frozen Database Transfer: Files with low sensitivity or user requests for retention are sent to the frozen database. Users must formally request access to these files, ensuring that data retrieval is controlled and adheres to organizational policies.

3.5 Real-Time Alerts and Admin Panel Control

NSC integrates seamlessly with real-time alerts and provides a comprehensive admin panel for monitoring and managing policy violations.

3.5.1 Real-Time Policy Violation Alerts

Triggers can be set to automatically alert system administrators when DLP policies are violated or sensitive data interactions occur. These alerts are configurable based on the organization’s business logic and the level of sensitivity attached to the dataset.

3.5.2 Admin Actions for Policy Enforcement

The admin panel offers a range of actionable features, including the ability to suspend user machines when a critical violation is detected. Warnings can also be issued directly to users when certain policy thresholds are crossed, helping to enforce proper data handling behavior in real-time.

Shown below in the mermaid chart is the logic flow for a use case where in all above mentioned featured are combined to work simultaneously. 

4. Integration with Existing Backend Data Observability Solutions

NSC's architecture is designed to seamlessly integrate with existing backend data observability tools, creating a unified and comprehensive data security infrastructure.

4.1 SIEM (Security Information and Event Management) Integration

NSC can be integrated with SIEM platforms to offer a complete view of both front-end and back-end data interactions. SIEM systems, traditionally focused on backend logs and server-side activities, can enrich their datasets by receiving real-time data and alerts from NSC’s task mining and remote capture components. This integration allows for correlating front-end user actions with backend anomalies or security events, enabling better incident response strategies.

4.2 API Hooks for Backend Monitoring Systems

Although NSC avoids relying on external APIs for tracking purposes, it can use API hooks to communicate with backend monitoring systems. For instance, when task mining detects a sensitive dataset interaction on the front end, this information can be relayed to backend observability tools, which can then cross-reference server logs to identify any backend security risks. This ensures a continuous loop of observability across the entire data lifecycle, from user access to server storage.

4.3 Data Lakes and Centralized Logging

NSC's captured data—such as file interactions, task mining logs, and redundancy audits—can be stored in data lakes or centralized logging systems. These logs are then accessible to traditional backend observability tools for deeper analytics, machine learning models, or predictive maintenance algorithms. By combining front-end and back-end data, organizations can detect emerging data patterns that would otherwise remain hidden with a single-layer observability solution.

4.4 Incident Management System (IMS) Integration

NSC integrates with IMS platforms, automatically generating tickets for any policy violations or data breaches flagged by the task mining or remote capture client. IMS systems track these incidents from identification through to resolution, improving operational efficiency by automating the workflow of incident management and policy enforcement.

4.5 Cloud-Based Observability Integration

As organizations shift towards hybrid and cloud-native infrastructures, NSC can extend its task mining and remote capture functionality to cloud environments. NSC can integrate with cloud-based observability platforms to monitor and flag sensitive data interactions within cloud apps, ensuring a full visibility loop that spans on-premise and cloud ecosystems.

4.6 Orchestration with RPA Tools

NSC's dynamically generated RPA scripts can work alongside traditional RPA orchestration tools. Backend systems that trigger specific tasks (e.g., data retention workflows) can leverage NSC's front-end automation to carry out related tasks at the user level, such as real-time data deletions or approvals for file retention. This level of integration ensures that data observability and compliance processes remain tightly synchronized across the organization.

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5. Advantages of NSC Over Traditional Backend Data Observability Solutions

Traditional backend observability solutions, while useful for monitoring server-side activities, often fail to address the full data lifecycle, particularly when it comes to front-end user interactions. The Node Sanitation Copilot (NSC) introduces significant advantages by shifting the focus to front-end data observability, complementing and expanding the capabilities of existing backend solutions.

5.1 Full Data Lifecycle Coverage

5.1.1 Traditional Backend Focus

Backend observability tools such as SIEM and log management systems primarily focus on monitoring server-side events, application logs, network traffic, and system performance. These tools are effective at tracking data once it reaches a server but lack visibility into how data is interacted with on individual user machines. This creates a blind spot in terms of how data is accessed, transferred, or copied by employees.

5.1.2 NSC’s Comprehensive Coverage

NSC fills this gap by extending data observability to the front-end, covering every step in the data lifecycle. From the moment a user accesses sensitive data, NSC can monitor and log interactions, including file viewing, sharing, and even copying data between applications (e.g., copying data from an Excel file and pasting it into a non-approved messaging service). This full-cycle visibility allows organizations to detect potential data breaches or policy violations earlier, before they reach the backend infrastructure.

5.2 Real-Time, User-Level Data Monitoring

5.2.1 Backend Delays in Data Breach Detection

Traditional backend systems often detect breaches after the fact, relying on logs, anomalies, or changes in server-side behavior to identify issues. This lag in detection can allow a malicious actor or an accidental data leak to go unnoticed until significant damage has been done.

5.2.2 Real-Time Triggers with NSC

NSC’s task mining tools and remote capture client allow for real-time monitoring of user actions. By setting up predefined triggers for sensitive datasets, NSC flags any interaction with such data in real-time, including attempts to share or duplicate it. This enables the system to raise alerts or block actions as soon as suspicious activity occurs, providing a proactive layer of defense that backend solutions lack.

5.3 Granular Data Control and Classification

5.3.1 Limited Control in Backend Systems

Backend observability systems rely on pre-defined rules and logs generated from servers or databases, often providing only coarse insights into how data flows across an organization. These tools are typically unable to classify data with a high level of granularity based on user actions, meaning that critical data leaks might still slip through if they do not trigger the correct backend event.

5.3.2 Dynamic Classification in NSC

NSC excels by offering a dynamic classification model that classifies data at the user level, based on access and interaction. For example, NSC can distinguish between merely viewing a file versus sharing it externally. Data is tagged with a sensitivity index as soon as a user accesses it, and depending on how it is used (e.g., emailing it externally), the system can take appropriate action—whether that’s logging the action for review, blocking the transfer, or even immediately deleting the file via an RPA script. This granularity is impossible to achieve solely with backend tools, which don’t have direct access to user-level interactions.

5.4 Proactive Data Redundancy Management

5.4.1 Lack of Front-End Redundancy Management in Backend Solutions

Traditional backend tools focus on storage optimization and server-side data duplication, often ignoring redundant files stored on individual machines. While backend tools may optimize server storage, they can leave redundant files on user devices untouched, increasing the risk of data breaches and compliance violations.

5.4.2 NSC’s Automated Redundancy Management

NSC introduces a proactive approach to handling data redundancy at the user level. The system periodically scans user machines, identifies redundant files, and enforces deletion based on predefined organizational rules. Through a review process, users can request to keep certain data, ensuring that critical files are not lost while reducing the overall data footprint. Additionally, NSC handles sensitive data with care, ensuring that even redundant data follows DLP policy guidelines by allowing users to request file retrieval only through proper channels. This adds a new layer of operational efficiency and data hygiene that backend solutions do not address.

5.5 Enhanced Compliance with Data Loss Prevention Policies

5.5.1 Backend Limitations in DLP Enforcement

DLP (Data Loss Prevention) systems in traditional backend observability tools typically operate on predefined rules that monitor traffic exiting the network or files uploaded to external systems. However, once data is downloaded or transferred to a user’s local machine, backend systems often lose the ability to enforce DLP policies effectively. There is no way to prevent a user from copying sensitive data to a USB stick or sharing it via a messaging app.

5.5.2 NSC’s Front-End Enforcement of DLP

NSC integrates directly with the organization’s DLP policies to monitor data at the user’s machine level. Whether a user views, copies, or shares sensitive information, NSC tracks the interaction and triggers appropriate actions. For example, if a user attempts to send a sensitive document to an external email address, NSC can either block the transfer outright or log the action for review, depending on the organization's DLP settings. This front-end enforcement significantly reduces the risk of non-compliant data sharing and enhances the organization’s overall data security posture.

5.6 Reduction of Human Error through Automation

5.6.1 Backend Tools Rely on Manual Configurations

Traditional backend observability systems depend heavily on administrators manually configuring rules and thresholds for identifying anomalies or data policy violations. This leaves room for human error, as misconfigured rules or incomplete policies can result in missed violations or false positives.

5.6.2 NSC’s Automation with RPA Scripts and AI-Generated Policies

NSC automates key data observability tasks through dynamically generated RPA scripts and AI-driven policies. The task mining tool allows the system to dynamically adjust its monitoring based on real-time data interactions, while AI models can help refine organizational data policies based on observed behavior. For instance, if NSC detects that certain types of data are frequently misused, it can adjust the sensitivity thresholds or generate new rules to prevent future violations. This automation reduces the risk of human error and ensures that the system remains adaptive and responsive to evolving threats.

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6. Additional Applications of NSC

In addition to the features already discussed, NSC opens up new possibilities for applications that traditional backend solutions cannot easily address. These applications enhance NSC’s appeal as a front-end observability tool that complements and strengthens existing backend architectures.

6.1 Insider Threat Prevention

One major application of NSC is its ability to prevent insider threats. By monitoring data access and transfers at the user level, NSC can identify potential risks posed by employees or contractors who have access to sensitive data. If unusual patterns of data access or sharing are detected—such as downloading large quantities of sensitive files before termination—NSC can flag the behavior for review or take proactive measures like revoking access to certain systems.

6.2 Sensitive Data Audit Trails for Compliance

NSC’s ability to track data interactions at the user level also makes it an excellent tool for creating audit trails that support compliance with regulations like GDPR, HIPAA, and others. By logging every touchpoint, from access to deletion, NSC provides a full audit trail that can be reviewed during compliance audits. This is especially important for industries with stringent data privacy requirements, where proving how sensitive data was handled is crucial.

6.3 Shadow IT Detection

Shadow IT—where employees use unauthorized applications or systems for work-related tasks—poses a significant security risk to organizations. NSC can detect when data is transferred to applications that have not been approved by the IT department, such as sharing company files through personal email or messaging apps. This insight allows administrators to shut down risky practices before they lead to data breaches.

6.4 Cross-Platform and Hybrid Cloud Visibility

As organizations increasingly rely on hybrid cloud environments, maintaining consistent data observability across on-premise, cloud, and cross-platform setups becomes challenging. NSC is adaptable to cloud-based ecosystems, providing visibility into data interactions across both traditional and cloud-native apps. This is particularly useful in multi-cloud architectures where traditional backend systems often struggle to offer comprehensive observability across platforms.

 

7. Conclusion

The NSC system bridges the gap between front-end and backend data observability, offering real-time insights, proactive data management, and seamless integration into existing security infrastructure. By providing deep task mining and remote capture capabilities on the front end and combining this with robust backend integrations, NSC empowers organizations to monitor their data lifecycle holistically, ensuring compliance, operational efficiency, and data security.