Automated system for risk profiling and remediating compliance deficiencies using behavioral models

Abstract

An automated system (100) for risk profiling and compliance remediation using behavioral models, comprising: (a) a data ingestion and pre-processing module configured to collect and normalise structured and unstructured data from multiple sources; (b) a behavioral module configured to generate dynamic behavioral baselines and detect anomalies using machine learning algorithms; (c) a risk assessment and profiling module configured to assign and update risk assessments for entities based on detected behavioural anomalies and contextual parameters; d) a compliance rules mapping module configured to compare activities with internal policies and external rules to detect compliance violations; e) a real-time alerting and visualisation module configured to present detected anomalies and violations through dashboards with prioritised insights; (f) an automatic remediation module configured to initiate context-based corrective actions based on predefined rules and adaptive feedback; and g) a test and learn feedback module configured to capture system decisions, user responses and outcomes to improve the behavior and risk models over time.

Description

[0001] The present invention relates to automated risk management systems, particularly to a system that creates dynamic risk profiles and corrects compliance. It uses behavioral models to detect anomalies, assess compliance, and initiate corrective actions. The invention is applicable to all areas where compliance and continuous risk assessment are required.

[0002] Modern organizations operate in an increasingly complex regulatory environment where failure to adhere to compliance standards can result in significant financial and reputational damage. Traditional compliance monitoring methods are often reactive, manual, and unable to adapt to the rapidly changing threat landscape. Therefore, there is an urgent need for intelligent systems that can proactively detect and respond to compliance risks in real time.

[0003] Existing risk assessment tools primarily rely on static, rule-based engines that lack the flexibility to interpret behavioral deviations and contextual anomalies. These systems are difficult to scale with business growth and are often unable to capture subtle changes in user or system behavior that could indicate emerging risks or compliance violations.

[0004] Behavioral modeling, powered by machine learning and data analytics, offers a more dynamic approach to risk profiling. By analyzing historical and real-time user activity, system logs, and environmental context, behavioral models can uncover hidden patterns, predict risk levels, and prioritize threats based on impact and likelihood. However, integrating such models into automated remediation workflows remains a technical challenge.

[0005] There is a growing demand for systems that not only identify risks but also autonomously recommend or implement remedial actions consistent with internal policies and external regulations. These systems should be able to learn from past incidents to continuously improve their accuracy and efficiency, thus reducing the burden on compliance teams.

[0006] The present invention addresses these challenges by introducing an automated system that leverages behavioral models to dynamically create risk profiles, detect policy violations, and trigger remedial actions for compliance in real time. It provides a scalable, intelligent, and adaptable solution that bridges the gap between detection and action in enterprise risk management.

[0007] Another objective of this disclosure is to reduce the manual effort required to monitor compliance with regulations through automated risk profiling.

[0008] Another objective of this disclosure is to improve the accuracy of threat identification using continuously learning models.

[0009] Another objective of this disclosure is to support multiple legal frameworks through customizable rule mapping.

[0010] Another objective of this disclosure is to minimize response time through automated, context-dependent remediation.

[0011] Another objective of this disclosure is to provide intuitive dashboards for a quick overview of risks and violations.

[0012] Another objective of this disclosure is to ensure continuous improvement through feedback-driven model refinement.

[0013] Another objective of this disclosure is scalable deployment across different IT environments and industries.

[0014] Further objects and advantages of the present disclosure will become apparent from the following description, which is not intended to limit the scope of the present disclosure.

[0015] The present invention relates to a system that uses machine learning-based behavioral models to detect anomalous activities and deviations from normal patterns. These models continuously evolve by learning from historical and real-time data.

[0016] Another embodiment of the present invention involves assigning entities such as users, devices, or systems to dynamic risk scores based on behavior, anomaly frequency, and contextual sensitivity. The risk profiles are continuously updated to reflect the latest threat landscape.

[0017] Another embodiment of the present invention is that the system compares organizational behavior with internal policies and external regulations such as HIPAA, GDPR, and SOX. This enables the automatic detection of compliance violations in real time.

[0018] Another embodiment of the present invention is that it collects diverse data streams from enterprise systems, preprocesses them, and transforms them into a unified, analyzable format. This ensures a unified view of activities across all platforms.

[0019] Another embodiment of the present invention is that risk events and regulatory violations are presented via interactive dashboards with prioritized alerts. Stakeholders can monitor and drill down into current incidents for investigation.

[0020] Another embodiment of the present invention is that the system autonomously initiates remedial actions based on the severity and context of the violations, such as restricting user access, enforcing policies, or automatic reporting.

[0021] Another embodiment of the present invention is that the results of the remedial actions are fed back into the system to refine the behavioral thresholds and risk assessment logic. This ensures continuous learning and adaptive decision-making.

[0022] Another embodiment of the present invention is that the invention is designed with decoupled, interoperable modules that can be scaled across departments or enterprises. It supports integration with legacy systems and modern cloud platforms.

[0023] The present invention relates to an automated risk profiling and compliance remediation system that uses advanced behavioral models to detect, assess, and respond to compliance risks in real time. It includes key modules such as data ingestion and preprocessing, a behavioral module, risk assessment and profiling, compliance rule mapping, real-time alerting, automated remediation, and a learning loop. These modules work together to analyze enterprise data, detect anomalies, assess risk levels, compare with regulatory requirements, and autonomously initiate corrective actions. The system continuously learns from past events to improve decision accuracy. It provides a scalable, intelligent solution for proactive risk management and regulatory enforcement.

[0024] The invention is explained again below with reference to the figure. It shows: Fig. : an automated system for risk profiling and compliance remediation (100) using behavioural models.

[0025] Fig.illustrates an automated risk profiling and compliance remediation system (100) using behavioral models. The invention discloses an automated risk profiling and compliance remediation system using behavioral models, comprising multiple integrated modules that work together to monitor, assess, and remediate compliance risks in real time. The data ingestion and preprocessing module collects structured and unstructured data from various enterprise sources such as user activity logs, transaction systems, audit trails, and third-party APIs, and normalizes and enriches it for subsequent analysis. The behavioral module applies machine learning algorithms to create dynamic behavioral baselines for users, systems, and processes, continuously learning from historical data to identify anomalous or non-compliant behavior patterns.The Risk Assessment and Profiling module leverages the results of the Behavior module to assign risk scores based on factors such as deviation severity, frequency, and legal context, and dynamically update user or system risk profiles. The Compliance Rule Mapping module compares internal policies and external rule sets (e.g., HIPAA, GDPR, SOX) with observed behaviors, enabling the system to detect violations in real time via a continuously updated rule repository. The Real-Time Alerting and Visualization module presents detected anomalies, risk scores, and violation details via intuitive dashboards, providing prioritized insights for compliance officers and security teams.The Automated Remediation Module initiates context-aware remediation actions such as access revocation, workflow disruption, policy enforcement, or escalation based on predefined rules and adaptive feedback from past findings. Finally, the audit and learning feedback loop captures system actions, user responses, and investigation results to refine model accuracy and decision logic over time, ensuring continuous improvement and adaptability to changing compliance landscapes. This modular, intelligent system provides end-to-end risk management automation and enables proactive compliance enforcement across multiple industries.

Claims

[1] An automated system (100) for risk profiling and compliance remediation using behavioral models, comprising: (a) a data ingestion and pre-processing module configured to collect and normalise structured and unstructured data from multiple sources; (b) a behavioral module configured to generate dynamic behavioral baselines and detect anomalies using machine learning algorithms; (c) a risk assessment and profiling module configured to assign and update risk assessments for entities based on detected behavioural anomalies and contextual parameters; d) a compliance rules mapping module configured to compare activities with internal policies and external rules to detect compliance violations; e) a real-time alerting and visualisation module configured to present detected anomalies and violations through dashboards with prioritised insights; (f) an automatic remediation module configured to initiate context-based corrective actions based on predefined rules and adaptive feedback; and g) a test and learn feedback module configured to capture system decisions, user responses and outcomes to improve the behavior and risk models over time. [2] The system (100) of claim 1, wherein the data ingestion and preprocessing module supports data from logs, transaction records, APIs, and user activity monitoring tools. [3] The system (100) of claim 1, wherein the behavioral module uses supervised and unsupervised learning models, including clustering and neural networks. [4] The system (100) of claim 1, wherein the risk assessment module updates the profiles of companies in real time using a weighted score based on severity, frequency, and regulatory impact. [5] The system (100) of claim 1, wherein the compliance rule mapping module can be dynamically updated with changes in global, regional, and industry-specific regulations. [6] The system (100) of claim 1, wherein the alerting module provides role-based access to dashboards with filters for time, risk category, and resolution status. [7] The system (100) of claim 1, wherein the automatic remediation module performs actions including revoking access, interrupting workflow, escalating incidents, or generating compliance tickets. [8] The system (100) of claim 1, wherein the testing and feedback module uses result data to periodically retrain models and adapt remediation strategies for improved performance.

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