System for autonomous data analysis with privacy protection in enterprise software

Abstract

A system (100) for autonomous, privacy-preserving data analysis in enterprise software, comprising: a data ingestion and classification module configured to collect and classify data from distributed enterprise sources based on sensitivity and context; a policy management module configured to apply dynamic data protection rules and regulatory compliance policies to the classified data; a consent and access control module configured to enforce user permissions, roles, and data subject rights based on predefined or dynamically acquired consents; a federated analysis module configured to perform decentralized data analysis across multiple nodes without transmitting raw data; a secure computation module configured to perform encrypted computations using homomorphic encryption and differential data protection; an automated monitoring and auditing module configured to log all data access, analysis activities, and policy enforcement actions in real time; and A module for gaining insights and visualization, configured to generate and display privacy-compliant analysis results via dashboards and reports.

Description

[0001] The present invention relates to the field of data protection and data analysis in enterprise software systems. More specifically, it concerns autonomous systems that enable secure, privacy-compliant data analysis in distributed enterprise environments. The invention integrates privacy engineering, automated data governance, and secure computational techniques to ensure compliant and confidential analyses.

[0002] In today's digital business landscape, organizations process vast amounts of sensitive data from various departments, partners, and stakeholders. While data analytics plays a crucial role in decision-making and process optimization, it often comes at the cost of compromising individual privacy and disclosing confidential information. Traditional data analytics systems lack granular control over data access, leaving organizations vulnerable to data breaches, non-compliance with regulations such as GDPR and HIPAA, and the erosion of stakeholder trust.

[0003] Existing enterprise software solutions either centralize data for processing—thus increasing the attack surface—or implement complex anonymization methods that compromise analytical accuracy. Furthermore, manually enforcing data governance policies is error-prone, time-consuming, and unable to keep pace with the growing complexity of enterprise ecosystems. The lack of automation in managing data privacy and secure computing further hinders real-time analytics, preventing organizations from using data efficiently without risking regulatory non-compliance.

[0004] To address these challenges, there is an urgent need for an autonomous system capable of performing data analytics while maintaining data privacy. Such a system should incorporate techniques like federated learning, homomorphic encryption, and differential data protection, all governed by dynamic, self-enforcing data access policies. The invention fulfills this need by providing a privacy-friendly analytics framework that operates independently, ensures regulatory compliance, minimizes human intervention, and maintains data usability in distributed enterprise software environments.

[0005] One objective of this disclosure is to ensure consistent compliance with data protection regulations in the data analysis workflows of companies.

[0006] Another objective of this disclosure is to eliminate the need to centralize sensitive data and thus reduce the risk of data breaches.

[0007] Another objective of this disclosure is to automate the enforcement of dynamic data protection and regulatory policies.

[0008] Another objective of this disclosure is to enable secure, decentralized analyses using federated learning techniques.

[0009] Another objective of this disclosure is to preserve the usefulness of the data while protecting its confidentiality through encryption.

[0010] Another objective of this disclosure is to strengthen user trust by supporting the management of consents and data rights.

[0011] Another objective of this disclosure is to provide real-time monitoring, logging and auditing capabilities for accountability purposes.

[0012] Another objective of this disclosure is to provide actionable insights without disclosing raw or sensitive data.

[0013] The present invention relates to an autonomous system for data protection-compliant data analysis in corporate environments. It ensures the confidentiality and compliance of the data throughout the entire lifecycle of the analysis without manual intervention.

[0014] Another embodiment of the present invention consists in that it includes intelligent data input and classification mechanisms for the detection and marking of sensitive data.

[0015] This facilitates precise downstream enforcement of data protection and context-related data processing across different sources.

[0016] Another embodiment of the present invention consists in the system dynamically enforcing data protection policies based on legal regulations such as GDPR and HIPAA.

[0017] These guidelines are machine-readable and are automatically applied to all data operations in real time.

[0018] Another embodiment of the present invention consists in managing user consent and role-based access via a special module that monitors permissions.

[0019] It enables fine-grained control and allows users to exercise data rights such as access and revocation.

[0020] Another embodiment of the present invention consists in performing the analyses in a network without transferring raw data across nodes or systems. This decentralized approach minimizes data exposure and increases safety during model training.

[0021] Another embodiment of the present invention is the application of advanced cryptographic methods such as homomorphic encryption and differential data protection.

[0022] These methods ensure that the data remains protected even when calculations are performed.

[0023] Another embodiment of the present invention consists in the system continuously monitoring the operation and logging every activity to ensure transparency and auditability.

[0024] Real-time alerts and reports help in the detection of data privacy risks, breaches, or policy violations.

[0025] Another embodiment of the present invention consists in providing the privacy-compliant insights via interactive dashboards and visual reports. Users can securely explore data-driven insights without compromising the privacy of the individual or the company.

[0026] The present invention relates to an autonomous system designed for performing data analysis while maintaining privacy in enterprise software environments. It comprises seven intelligent modules that work together to ensure secure data processing and regulatory compliance. The system begins with data ingestion and classification, followed by policy enforcement and consent-based access control. Federated analysis and secure computing modules enable decentralized and encrypted data processing. Finally, the findings are visualized, while all operations are continuously monitored and audited to ensure transparency. Module for data acquisition and classification:

[0027] This module is responsible for the secure collection of data from various enterprise sources, such as databases, applications, APLs, and IoT devices. It utilizes AI-based parsing and machine learning classifiers to identify the type, format, and sensitivity of the data. Metadata is automatically tagged, and the data is categorized according to predefined sensitivity classes such as personal, confidential, or public. This classification helps determine the appropriate privacy measures for each data segment. Policy management module:

[0028] This module is responsible for creating, updating, and enforcing data protection and data sharing policies within the system. It stores company-specific rules and legal requirements and automatically maps them to the data repositories. Using a policy-driven approach, it ensures that every data transaction complies with legal and organizational standards. The module also supports customizable rules for different departments, roles, and regions. Consent and access control module:

[0029] This module manages data access based on user roles, data ownership, and consent agreements. It ensures that only authorized users or systems can access specific data and dynamically tracks consent status. The module also facilitates the rights of data subjects, such as access, withdrawal of consent, and rectification of data, and makes the system transparent and user-friendly in accordance with data protection laws. Federated Analysis Module:

[0030] This module enables decentralized data analysis across multiple departments or geographic locations without the need to transfer raw data. It utilizes federated computational techniques, where machine learning models are trained locally and only the aggregated, anonymized insights are shared. This ensures data privacy while simultaneously enabling robust analysis across distributed data sources. Module for secure calculations:

[0031] This module enables data processing in encrypted form using techniques such as homomorphic encryption and differential data protection. It ensures that calculations are performed without ever revealing the raw data to the system or analysts. Furthermore, it employs noise injection and masking strategies to prevent the reverse engineering of sensitive information from the analysis results. Automated monitoring and audit module:

[0032] This module provides continuous monitoring of all data analytics activities and enforces accountability. It logs access events, policy violations, and data flows for auditing purposes. Real-time alerts and visual dashboards help administrators identify abnormal behavior or data privacy risks. The module also supports the generation of audit logs for compliance reports and internal reviews. Module for generating and visualizing insights:

[0033] This module transforms securely processed and privacy-compliant data into actionable insights through charts, reports, and dashboards. It integrates with business intelligence tools and allows users to customize views based on their roles and permissions. The insights are generated without exposing sensitive data, ensuring the visualization respects the privacy restrictions defined in previous modules.

[0034] The invention is explained again below with reference to the figure. This shows: Fig. : a system (100) for autonomous privacy-friendly data analysis in enterprise software.

[0035] Fig.Figure 100 illustrates a system (100) for autonomous, privacy-friendly data analysis in enterprise software. This system operates through the seamless integration of specialized modules that work in a coordinated manner to ensure secure and legally compliant data processing. First, the Data Ingestion and Classification module collects data from various enterprise sources and automatically classifies it based on sensitivity and context using AI-powered techniques. Following classification, the Policy Management module applies dynamic data protection rules and legal requirements to determine how the data should be handled. Simultaneously, the Consent and Access Control module validates user roles and consent status, restricting data access according to company policies and user permissions.The Federated Analytics Module enables decentralized analysis by allowing models to be trained locally on different nodes, ensuring that the raw data remains in its original source environment. For added protection, the Secure Computation Module processes the data using homomorphic encryption and differential data protection techniques, enabling meaningful insights without exposing sensitive information. All activities are tracked by the automated monitoring and auditing module, which provides real-time monitoring, audit logs, and alerts for policy violations to ensure transparency and accountability.Finally, the Insight Generation and Visualization module summarizes the analysis results in dashboards and reports, providing users within the company with meaningful insights while maintaining data confidentiality throughout the entire process.

Claims

[1] A system (100) for autonomous privacy-preserving data analysis in enterprise software, comprising: a data ingestion and classification module configured to collect and classify data from distributed enterprise sources based on sensitivity and context; a policy management module configured to apply dynamic data protection rules and regulatory compliance policies to the classified data; a consent and access control module configured to enforce user permissions, roles, and data subject rights based on predefined or dynamically acquired consents; a federated analysis module configured to perform decentralized data analysis across multiple nodes without transmitting raw data; a secure computation module configured to perform encrypted computations using homomorphic encryption and differential data protection; an automated monitoring and auditing module configured to log all data access, analysis activities, and policy enforcement actions in real time; and A module for gaining insights and visualization, configured to generate and display privacy-compliant analysis results via dashboards and reports. [2] System (100) according to claim 1, wherein the data acquisition and classification module uses machine learning and natural language processing techniques to identify sensitive information and tag metadata. [3] System (100) according to claim 1, wherein the policy management module dynamically updates the privacy rules in response to changes in the legal framework such as GDPR, HIPAA or CCPA. [4] System (100) according to claim 1, wherein the consent and access control module enables the revocation, modification or extension of user consent and access rights in real time via a self-service interface. [5] System (100) according to claim 1, wherein the federated analysis module uses local model training and aggregation techniques to generate global insights without exposing raw data. [6] System (100) according to claim 1, wherein the secure computing module supports zero-knowledge proofs and noise injection for additional data confidentiality and re-identification risk reduction. [7] System (100) according to claim 1, wherein the automatic monitoring and testing module generates test paths, warnings for the detection of anomalies and conformity reports in real time. [8] System (100) according to claim 1, wherein the module for generating insights and visualizing supports role-based access to dashboards and customizable data views based on user rights. [9] System (100) according to claim 1, wherein all modules operate autonomously with minimal human intervention using an AI-controlled orchestration framework that manages workflows and communication between the modules.

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