What question did this study set out to answer?

The aim is to develop a framework that can formally verify access control systems that integrate machine learning.

April 15, 2026Open Access

Formal Verification of Adaptive Access Control

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This approach bridges the verification gap in Zero Trust architectures, enhancing security with ML-driven risk signals.

The aim is to develop a framework that can formally verify access control systems that integrate machine learning.
Proposes a runtime monitoring framework based on Metric First-Order Temporal Logic (MFOTL)
Isolates deterministic policy enforcement from probabilistic neural networks
Generates Quantitative Verification Certificates to ensure timely threat mitigation
Establishes formal security guarantees for access control execution according to ML outputs
Acknowledges limitations if adversaries manipulate ML outputs, potentially compromising security

Macharski Christopher (2026) studied this question.

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April 15, 2026Open Access

View Full Paper

Ask AI

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Authors

This approach bridges the verification gap in Zero Trust architectures, enhancing security with ML-driven risk signals.

The aim is to develop a framework that can formally verify access control systems that integrate machine learning.
Proposes a runtime monitoring framework based on Metric First-Order Temporal Logic (MFOTL)
Isolates deterministic policy enforcement from probabilistic neural networks
Generates Quantitative Verification Certificates to ensure timely threat mitigation
Establishes formal security guarantees for access control execution according to ML outputs
Acknowledges limitations if adversaries manipulate ML outputs, potentially compromising security

Macharski Christopher (2026) studied this question.

View Full Paper

Ask AI

Bookmark