Formal Verification of Adaptive Access Control

The integration of Machine Learning (ML) into Attribute-Based and Capability-Based Access Control (ABAC/CBAC) enables dynamic, risk-aware security in modern enterprise environments. However, the black-box opacity and adversarial vulnerability of deep learning models render traditional static formal verification computationally intractable, creating a “verification gap” in Zero Trust architectures. To bridge this gap, this paper proposes a runtime monitoring framework based on Metric First-Order Temporal Logic (MFOTL). Rather than attempting to prove the internal correctness of probabilistic neural networks, the framework treats ML risk signals as opaque environmental facts evaluated within a decidable guarded safety fragment. This approach isolates the deterministic policy enforcement logic from the ML engine, proposing the generation of Quantitative Verification Certificates that mathematically bound the temporal gap between threat detection and mitigation. The formal security guarantees provided by this framework operate within a strict operational boundary: they mathematically prove that the access control rules were flawlessly executed according to the received signals. But they rely on the assumption that the ML outputs are genuine. If an adversary successfully evades the AI or poisons the model to produce false risk scores, the monitor will formally enforce the compromised signal. Ultimately, this framework provides a deterministic backstop that strictly secures the physical enforcement mechanism, acknowledging that formal verification in ML-augmented systems proves the soundness of the logic, not the algorithmic truth of the AI.