Abstract
This disclosure presents a computer-implemented autonomous cyber-defense framework for generating, validating, deploying, monitoring, and evolving cybersecurity policies. The framework treats a security policy as an evolving control object and combines continuous security and operational telemetry, risk-aware analysis, generation of multiple candidate policy changes, machine-readable digital-twin validation, constrained candidate evaluation, explainable decision recording, controlled deployment, post-deployment monitoring, automatic rollback, and feedback-driven policy evolution. Candidate policies are evaluated before operational deployment against modeled security and operational conditions, including threat reduction, reachability, availability, latency, resource impact, policy conflicts, scope integrity, confidence, and recovery readiness. A decision controller may approve, modify, reject, defer, or prepare a candidate for controlled deployment based on validation results and applicable constraints. Deployment outcomes, rollback events, simulation results, and analyst assessments are retained through policy lineage and may influence subsequent candidate generation, ranking, validation, or selection. The disclosed architecture therefore establishes a closed-loop control lifecycle: observe, assess, generate, validate, decide, deploy, monitor, recover or learn, and re-evaluate.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Jafari Chashmi, Amir Hossein, "Autonomous Cyber Defense Policy Evolution Framework Using Risk-Aware Digital Twin Validation and Explainable AI Feedback", Technical Disclosure Commons, (September 07, 2026)
https://www.tdcommons.org/dpubs_series/11625