An automated data governance system with AI-powered anomaly detection

Abstract

An automated data governance system with AI-powered anomaly detection that includes: an AI-based anomaly detection module configured to analyze historical and real-time data patterns using machine learning (ML) models to detect suspicious activity, inconsistencies, and security threats; an automated compliance checker configured to ensure that data governance policies comply with the legal framework by monitoring data transactions and access controls; a data integrity management module configured to validate the consistency, authenticity, and security of the data using cryptographic hashing, blockchain-based tracking, and checksum validation; an adaptive policy engine configured to dynamically update governance policies based on real-time analytics, risk assessments, and industry regulations; a user access and role management module configured to implement role-based access control (RBAC) and leverage AI-driven behavioral analysis to dynamically adjust access permissions; and a real-time alerting and remediation module configured to generate alerts upon detection of anomalies and perform automatic remediation actions such as access revocation, data isolation, or regulatory enforcement.

Description

[0001] The present invention relates to data governance and security. More specifically, it relates to an automated data governance management system that uses artificial intelligence (AI) to detect anomalies to ensure compliance, security, and data integrity in various domains.

[0002] In the digital age, companies across industries generate and process massive amounts of structured and unstructured data across multiple platforms, including cloud-based storage, on-premises databases, and hybrid IT environments. Managing such vast amounts of data while ensuring security, compliance, and quality has become a critical challenge. Effective data governance is essential to maintain data integrity, enforce security policies, and ensure compliance with regulatory requirements such as GDPR, HIPAA, ISO 27001, and CCPA. However, traditional data governance systems and methodologies suffer from several limitations that hamper their ability to effectively manage dynamic data environments.

[0003] Traditional data governance systems rely heavily on manual monitoring and predefined rule-based mechanisms for policy enforcement and inconsistency detection. These rule-based approaches operate with static thresholds and rigid compliance parameters, making them unable to adapt to evolving business requirements, regulatory updates, and emerging cybersecurity threats. As a result, organizations struggle to maintain compliance in real time, increasing the risk of fines, reputational damage, and data breaches.

[0004] A major disadvantage of traditional systems is their inability to monitor and detect anomalies in real time. Traditional governance frameworks typically rely on batch processing models, where data audits and anomaly detection are performed at regular intervals rather than continuously. This delayed response to data inconsistencies and unauthorized activity significantly increases the risk of data leaks, fraud, unauthorized changes, and compliance violations. The lack of real-time alerting mechanisms means that organizations often don't discover data governance failures until significant damage has already been caused.

[0005] Another key limitation of traditional anomaly detection methods is their reliance on threshold-based or statistical detection methods, which are prone to high false positive and false negative rates. These systems often generate unnecessary alerts for non-critical deviations, overwhelming administrators and leading to alert fatigue. Conversely, critical anomalies such as unauthorized data access, insider threats, and sophisticated cyberattacks often go undetected because the predefined thresholds fail to detect evolving attack patterns.

[0006] Furthermore, existing governance frameworks lack machine learning-based adaptive policy management, leading to inefficiencies in maintaining data consistency, detecting irregular access patterns, and regulatory compliance. They require frequent manual intervention to update governance policies, making them impractical for dealing with dynamic data environments where policies must continuously evolve based on business trends, user behavior, and regulatory changes.2 Furthermore, traditional approaches to managing data integrity often rely on checksum-based or basic cryptographic verification techniques, which are inadequate for detecting subtle, unauthorized changes in large, distributed data sets.These methods lack comprehensive data tracing and lack the ability to detect potential data corruption before it impacts business operations. Due to these inherent drawbacks, there is a critical need for an automated, AI-powered data governance system that can detect anomalies in real time, dynamically enforce compliance, and ensure robust data integrity with minimal human intervention. The present invention addresses these challenges by integrating artificial intelligence, machine learning, and adaptive policy enforcement mechanisms to create a next-generation data governance solution that is self-learning, scalable, and highly responsive to security threats and regulatory changes.

[0007] To solve this problem, the present invention provides a system for accelerating the introduction of new products in the areas of AI and cloud computing through early design development.

[0008] The automated data governance system with AI-powered anomaly detection can continuously monitor data transactions, user access patterns, and system behavior to detect anomalies, unauthorized activities, and potential security threats in real time.

[0009] The automated data governance system with AI-powered anomaly detection can improve compliance enforcement by integrating an automated policy engine that dynamically updates governance policies in accordance with evolving regulatory standards.

[0010] The automated data governance system with AI-powered anomaly detection can improve the accuracy of anomaly detection and reduce false positives and false negatives over time.

[0011] The automated data governance system with AI-powered anomaly detection can instantly detect policy violations, data inconsistencies, and security threats, allowing administrators to take prompt corrective action.

[0012] The automated data governance system with AI-powered anomaly detection can continuously monitor, analyze, and control enterprise data in real time to ensure security, compliance, and data integrity with minimal human intervention.

[0013] The automated data governance system with AI-powered anomaly detection can leverage machine learning (ML) and artificial intelligence (AI) algorithms to detect unusual patterns, unauthorized access, and data inconsistencies with high accuracy while minimizing false positives and false negatives.

[0014] The automated data governance system with AI-powered anomaly detection can dynamically update governance policies in accordance with the evolving regulatory framework.

[0015] The automated data governance system with AI-powered anomaly detection can generate real-time alerts and suggest corrective actions when anomalies or policy violations are detected through AI-driven decision-making processes.

[0016] The automated data governance system with AI-powered anomaly detection can improve data integrity management using cryptographic techniques, hashing mechanisms, and blockchain-based data history tracking to prevent unauthorized changes and ensure the traceability of corporate data.

[0017] The automated data governance system with AI-powered anomaly detection can automate role-based access control (RBAC) and user authentication by integrating AI-powered behavioral analytics to dynamically adjust data access permissions based on real-time risk assessment models.

[0018] The automated data governance system with AI-powered anomaly detection integrates seamlessly into multi-cloud, on-premises, and hybrid IT environments, enabling organizations to enforce consistent data policies across different platforms.

[0019] The automated data governance system with AI-powered anomaly detection can improve decision-making through predictive analytics and natural language processing (NLP) to create intelligent reports, risk assessments, and compliance dashboards for business administrators and auditors.

[0020] The automated data governance system with AI-powered anomaly detection can reduce operational costs and increase efficiency by minimizing human intervention, automating compliance audits, and streamlining policy enforcement through AI-based workflow automation.

[0021] In one embodiment, a system is provided to accelerate new product adoption in AI and cloud computing through early design development. The system is designed to ensure data integrity, security, and regulatory compliance in enterprise environments. The system uses machine learning (ML) and artificial intelligence (AI) to monitor data transactions, access patterns, and governance policies in real time, and to detect anomalies, unauthorized access, and compliance violations. The system includes an AI-based anomaly detection module that identifies suspicious data patterns and security threats, and a data integrity management module that validates data quality using cryptographic hashing and blockchain tracking.Additionally, an Adaptive Policy Engine dynamically updates governance policies based on regulatory changes and risk analysis, while a User Access and Role Management module enforces role-based access control (RBAC) and AI-driven authentication. The Real-Time Alerts and Remediation module provides instant notifications and automatic remediation actions to mitigate risk. The system can be deployed in on-premises, cloud, and hybrid environments and integrates seamlessly with SIEM (Security Information and Event Management) platforms. By automating governance workflows, reducing manual intervention, and leveraging self-learning AI models, the system improves compliance efficiency, security, and risk mitigation.

[0022] The invention is explained again below with reference to the figure. It shows: Fig. : a system to accelerate the introduction of new products in AI and cloud computing through early design development.

[0023] Fig.demonstrates a system for accelerating new product launches in AI and cloud computing through early design development. The automated data governance system with AI-powered anomaly detection includes an AI-based anomaly detection module, an automated compliance checker, a data integrity management module, an adaptive policy module, a user access and role management module, and a real-time alerting and remediation module. The AI-based anomaly detection module is configured to analyze historical and real-time data patterns using machine learning (ML) models to detect suspicious activity, inconsistencies, and security threats.The automated compliance checker is configured to ensure compliance with regulatory requirements by monitoring data transactions and access controls. The data integrity management module is configured to verify data consistency, authenticity, and security using cryptographic hashing, blockchain-based tracking, and checksum validation. The adaptive policy module is configured to dynamically update governance policies based on real-time analytics, risk assessments, and industry regulations. The user access and role management module is configured to implement role-based access control (RBAC) and leverage AI-driven behavioral analytics to dynamically adjust access permissions.The Real-Time Alerts and Remediation module is configured to generate alerts upon anomaly detection and perform automatic remediation actions such as access revocation, data isolation, or regulatory enforcement. The AI-based Anomaly Detection module leverages deep learning models, recurrent neural networks (RNNs), and transformation models for improved anomaly prediction accuracy. The Automatic Compliance Checker integrates with Security Information and Event Management (SIEM) systems to generate audit logs and regulatory compliance reports. The Data Integrity Management module uses federated learning techniques to ensure data validation without exposing sensitive information. The Adaptive Policy module automatically modifies governance rules based on real-time threat intelligence and regulatory updates.The User Access and Role Management module implements multi-factor authentication (MFA) and biometric authentication for enhanced security. The Real-Time Alerts and Remediation module integrates with incident response platforms to enable automated threat containment and remediation. List of reference symbols 100 systems

Claims

[1] An automated data governance system with AI-powered anomaly detection, including: an AI-based anomaly detection module configured to analyze historical and real-time data patterns using machine learning (ML) models to detect suspicious activity, inconsistencies, and security threats; an automated compliance checker configured to ensure that data governance policies comply with the legal framework by monitoring data transactions and access controls; a data integrity management module configured to validate the consistency, authenticity, and security of the data using cryptographic hashing, blockchain-based tracking, and checksum validation; an adaptive policy engine configured to dynamically update governance policies based on real-time analytics, risk assessments, and industry regulations; a user access and role management module configured to implement role-based access control (RBAC) and leverage AI-driven behavioral analysis to dynamically adjust access permissions; and a real-time alerting and remediation module configured to generate alerts upon detection of anomalies and perform automatic remediation actions such as access revocation, data isolation, or regulatory enforcement. [2] The system of claim 1, wherein the AI-based anomaly detection module uses deep learning models, recurrent neural networks (RNNs), and transformer models for improved anomaly prediction accuracy. [3] The system of claim 1, wherein the automated compliance audit is integrated with security information and event management systems (SIEM) to generate audit trails and compliance reports. [4] The system of claim 1, wherein the data integrity management module uses federated learning techniques to ensure data validation without disclosing sensitive information. [5] The system of claim 1, wherein the adaptive policy module automatically changes the governance rules based on real-time threat data and regulatory updates. [6] The system of claim 1, wherein the user access and role management module implements multi-factor authentication (MFA) and biometric authentication to enhance security. [7] The system of claim 1, wherein the real-time alerting and remediation module is integrated with incident response platforms to enable automatic threat containment and recovery.

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