NEUROSYMBOLIC AI FOR CYBERSECURITY POLICY ENFORCEMENT AND RISK ASSESSMENT

With the rising number of sophisticated and constantly evolving cyber threats, it becomes harder for the rule-based systems and black-box AI models to effectively provide policy enforcement and risk assessment in the dynamic digital environment. Neurosymbolic AI, which seeks to integrate the advantages of symbolic reasoning and neural network-based representation, provides a potential remedy. Neurosymbolic systems combine the interpretability and structure of symbolic logic with the pattern recognition power of deep learning in order to offer a more federated and transparent solution to security. This work provides a survey of the current and potential of neurosymbolic AI for cyber security policy enforcement and risk assessment. We investigate how symbolic reasoning frameworks can represent formal security policies, compliance regulations, and regulatory specifications while neural models model uncertain, unstructured, or incomplete inputs such as system logs, user behaviour, or threat indicators. In practice, applications include automatic policy audit, anomaly detection with policy context, risk propagation analysis, and the explainable security decision making process. We compare existing architectures and tools for neurosymbolic reasoning, present the benchmark datasets and evaluation metrics used, and describe key bottlenecks, including knowledge representation, scalability, and interfacing with legacy systems. Finally, we discuss potential future research directions, such as the development of real-time symbolic-neural inference engines, federated neurosymbolic models for cross-organization policy compliance, and the use of large language models for synthesis and reasoning about policies. This paper is a step towards bridging the gap between high-level governance (enforcement and auditing) and low-level facts, allowing to move into the direction of more secure and accountable AI-driven systems.