Request Context Generator for Security Policy Validation

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Solution Overview

Problem

Managing and validating complex security policies across computing resources in data centers becomes increasingly difficult as the number and types of users and resources expand, leading to challenges in ensuring secure network configurations and effective security policies.

Innovation Solution

Implementing an automated system that generates and evaluates request contexts using propositional logic to determine access permissions, allowing for proactive and automated validation and remediation of security policies, and providing tools for clients to analyze and improve their security policies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If the number and types of users and resources expand to facilitate increased utilization of data center resources, then productivity and resource utilization improve, but the difficulty of managing and validating security policies increases

Engineering Contradiction:

Improveresource utilizationVSAvoidsecurity policy management complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system enables security policies to self-validate through automated generation of request contexts and evaluation against policy rules. The satisfiability engine automatically determines whether security policies can be satisfied without manual intervention, allowing the system to self-manage security validation as it scales.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements feedback mechanisms where security policies are continuously evaluated against generated request contexts. The validation results provide feedback on policy effectiveness and conflicts, enabling automated remediation and continuous improvement of security configurations as the system expands.

Inventive Principle:

Principle #23Feedback

2Reliability

If manual management of security policies is used to ensure secure access control, then security validation accuracy improves, but the ease of operation deteriorates as the system scales

Engineering Contradiction:

Improvesecurity validation accuracyVSAvoidsecurity policy management ease

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system replaces manual mechanical processes of security policy validation with automated computational mechanisms. The satisfiability engine uses automated reasoning and context generation to validate security policies, substituting human manual review with algorithmic evaluation that maintains accuracy while improving ease of operation.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system introduces an intermediary automated validation layer between security policy definition and enforcement. The request context generator and satisfiability engine act as intermediaries that automatically evaluate policies before deployment, ensuring validation accuracy without requiring manual intervention in policy management operations.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Ease of operation

If automated validation systems are implemented to reduce management complexity, then ease of operation improves, but the device complexity increases

Engineering Contradiction:

Improvesecurity policy management easeVSAvoidvalidation system complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The automated validation system is segmented into distinct functional components: request context generation, satisfiability evaluation, and policy analysis. This segmentation allows each component to be independently developed and managed, reducing overall system complexity while maintaining ease of operation through modular architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The validation system is designed with universal components that can handle multiple security policy types and evaluation scenarios. The satisfiability engine and context generator serve multiple functions across different policy validation tasks, reducing the need for separate specialized systems and thereby managing complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS10922423B1Request context generator for security policy validation service

Publication Date: 2021.02.16 AMAZON TECH INC

AI summary

A security policy analyzer service of a computing resource service provider performs evaluations of security policies provided by the service provider's users, to determine whether the security policies are valid, satisfiable, accurate, and/or sufficiently secure. The service may compare the user-provided policy to a stored or best-practices policy to begin the evaluation, translating encoded security permissions into propositional logic formulae that can be compared to determine which policy is more permissive. The service determines values of the parameters in a request for access to a computing resource based on the policy comparison, and generates request contexts using the values. The service uses the request contexts to generate one or more comparative policies that are then used iteratively as the second policy in the comparison to the user-provided policy, in order to produce additional request contexts that represent allow/deny “edge cases” along the borders of policy permission statements.