E-Signature Threat Detection Using CMS Collaboration History
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Solution Overview
Problem
Legacy electronic signature systems and content management systems lack precise fraud detection mechanisms, leading to increased vulnerability to malevolent activities due to the abundance of information available, which existing security profiles fail to adequately address.
Innovation Solution
Combining threat-predictive information from electronic signature systems with content management systems for real-time identification and remediation of suspicious activities, utilizing data structures and machine learning models to reduce memory and processing demands while enhancing detection precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If more information is tracked and made accessible in electronic signature systems, then collaboration and tracking capabilities are improved, but vulnerability to malevolent exploitation increases
Solution Approach 1:
The patent segments information access by creating distinct security zones and permission levels. Different users can access different subsets of the tracked information based on their roles, preventing any single user from accessing all information while maintaining necessary collaboration capabilities.
Solution Approach 2:
The patent introduces an intermediary security layer that mediates between the need for information access and security protection. This intermediary monitors and controls information flow, allowing legitimate access while blocking malevolent exploitation attempts.
2Device complexity
If legacy security profiles are used to protect electronic signature systems, then implementation simplicity is maintained, but detection precision of fraudulent activities remains insufficient
Solution Approach 1:
The patent changes the parameters of security detection by moving from static security profiles to dynamic, context-aware security parameters. The system continuously adjusts security parameters based on real-time analysis of user behavior, document properties, and system state, significantly improving fraud detection precision.
Solution Approach 2:
The patent implements preliminary security actions by establishing baseline behavior patterns before fraudulent activities occur. The system pre-configures detection rules and security policies based on historical data and threat intelligence, enabling early detection and prevention of fraudulent attempts.
3Loss of time
If real-time threat detection and remediation are implemented, then security response time is improved, but computational resource demands increase
Solution Approach 1:
The patent applies partial action by focusing computational resources on the most critical security functions. Instead of analyzing every piece of data in real-time, the system selectively monitors high-risk operations and uses heuristic rules to identify potential threats, reducing computational overhead while maintaining effective real-time protection.
Solution Approach 2:
The patent implements periodic security scanning and analysis interspersed with real-time monitoring. Less resource-intensive periodic checks complement continuous real-time detection, allowing the system to maintain security effectiveness while managing computational resource consumption through alternating intensities of analysis.
Data Source
AI summary
Methods, systems, and computer program products for malevolent intent detection. Multiple cloud-based computer components are operatively interconnected to carry out operations for malevolent intent detection and remediation. In operation, a content management system (CMS) collects collaboration activities over time and over a content object so as to form a historical record of collaborator activities that includes a time-wise tracking of collaboration events over the content object. The CMS is interfaced with an electronic signature system (ESS) that captures e-signing events at the electronic signature system. Operational modules are invoked so as to recognize an occurrences of an e-signing event, and thereafter to perform a risk analysis of the e-signing event using both (a) portions of the historical record of collaborator activities for the content object at the CMS and (b) any information from an interaction with the ESS. Recommended remediation actions are emitted based on results of the risk analysis.