Tokenizing Sensitive Data Across Execution Contexts
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Solution Overview
Problem
Traditional runtime security solutions are inflexible and inadequate in defending against application-level attacks, particularly in systems where sensitive data may be exposed when potentially dangerous code is executed, leading to security breaches and non-compliance with data privacy laws.
Innovation Solution
A system and method for tokenized intrinsic runtime security that includes a trusted execution context, sandboxed execution contexts, and a policy enforcement module with a tokenizing sub-module, enabling safe execution of untrusted code by tokenizing sensitive information, thereby reducing risks and ensuring compliance with data privacy regulations like GDPR.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional runtime security solutions are used to defend against application-level attacks, then some security monitoring is provided, but the solutions are inflexible and inadequate against new or creative exploits
Solution Approach 1:
The system segments the execution environment into trusted and untrusted contexts, isolating sensitive operations within trusted boundaries. This segmentation allows the system to maintain security policies that are enforced consistently across different code contexts, providing both reliability and adaptability to new exploit attempts.
Solution Approach 2:
The patent introduces a security policy enforcement module as an intermediary between untrusted code and sensitive resources. This mediator enforces security policies dynamically, allowing the system to adapt to new exploits while maintaining reliable security enforcement through policy-based control.
2Object-affected harmful factors
If potentially dangerous code is isolated in sandboxed contexts, then security risks are reduced, but sensitive data may still be exposed when passed to these contexts
Solution Approach 1:
The patent extracts sensitive data from the untrusted execution context by tokenizing it before passing it to sandboxed code. The tokenized representation maintains functionality while removing the actual sensitive values, preventing data exposure while allowing the sandboxed code to operate with necessary data placeholders.
Solution Approach 2:
The system uses tokenized data representations that are ephemeral and context-specific. These tokens serve their purpose within the sandboxed execution and then become obsolete, providing a cost-effective way to protect sensitive data without requiring complex long-term security management.
3Reliability
If tokenization is implemented to protect sensitive data in untrusted contexts, then data security is improved, but system complexity increases
Solution Approach 1:
The security policy enforcement module serves multiple functions: it manages tokenization, enforces security policies, and controls data flow between trusted and untrusted contexts. This multi-functionality reduces overall system complexity by consolidating security operations into a single versatile component.
Solution Approach 2:
The tokenization system is designed to be self-managing through automated policy enforcement. The security module automatically tokenizes sensitive data based on policy rules without requiring manual intervention, reducing operational complexity while maintaining robust data protection.
Data Source
AI summary
Embodiments herein relate to new and useful systems and methods for tokenization across code trust boundaries. An embodiment includes a method for securing data across execution contexts in a computing device. The method includes determining that first data is to be passed from a first code in a first execution context to a second code in a second execution context. The method further includes, based on determining that the first data is to be passed, tokenizing the first data to generate tokenized first data, wherein tokenizing the first data comprises substituting the first data with second data that is based on the first data to secure the first data from the second code, the second data being the tokenized first data. The method further includes passing the tokenized first data from the first code to the second code.