Model Checking for IAM Policy Violation Detection
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Solution Overview
Problem
Existing systems struggle to effectively detect and prevent privilege escalation attacks and policy violations in identity and access management (IAM) systems, particularly in misconfigured environments, which can lead to unauthorized access to sensitive information.
Innovation Solution
A model checking system translates non-Boolean variables into Boolean variables to apply a Boolean Satisfiability (SAT) solver, simulating user actions to identify policy violations by modeling IAM systems as logical formulas, and exhaustively checking for potential privilege escalations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model checking with SAT solver is applied to detect policy violations, then detection precision is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent segments the policy validation process into distinct phases: policy parsing, model construction, SAT formulation, and solving. By dividing the complex validation task into smaller manageable components, the system can process policies more efficiently while maintaining high detection precision through systematic analysis of each segment.
Solution Approach 2:
The patent performs preliminary actions by constructing abstract models and converting policies into logical formulas before executing the SAT solver. This preprocessing step organizes the policy data into a structured format that facilitates faster and more efficient solving, reducing the actual processing time required for violation detection.
2Reliability
If comprehensive model checking is performed on IAM policies, then reliability of security validation is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary abstract model that bridges the gap between complex IAM policies and the SAT solver. This intermediate representation simplifies the validation process by translating complex policy logic into standardized logical formulas, making the system more manageable while maintaining comprehensive validation capability and high reliability.
Solution Approach 2:
The patent replaces manual or traditional automated policy analysis methods with a formal model checking approach using SAT solvers. This substitution provides a systematic and rigorous mechanism for validating policies, ensuring high reliability through mathematical proof techniques while the automation reduces the operational complexity for users.
3Productivity
If string variables are converted to Boolean variables for SAT solving, then productivity of policy analysis is improved, but manufacturing precision of variable representation is worsened
Solution Approach 1:
The patent applies parameter changes by transforming string variables into Boolean variables with associated constraints that preserve the original semantic meaning. This transformation enables the use of efficient SAT solving algorithms while maintaining the precision of variable representation through careful encoding of string operations as Boolean logic with appropriate constraints.
Data Source
AI summary
A model checking system detects violations and conflicts in security and verification policies by running model checking processes. The system detects privilege escalation attacks in misconfigured identification and access management (“IAM”) policies by modeling security policy documents and IAM actions as logical formulas and then running model checking on the model. The system translates non-Boolean variables, such as string variables, into Boolean variables in order to apply an SAT model checker. The model checker also determines whether a policy violation can be achieved in a finite number of steps by elevating privileges of some compromised principal over multiple iterations of the model checking process, or proves absence thereof.