This paper proposes a novel approach for enhancing physical layer security (PLS) in wireless networks by combining reconfigurable intelligent surfaces (RIS) and artificial noise (AN). The proposed aerial RIS (A-RIS) concept uses a RIS-attached unmanned aerial vehicle (UAV) to improve signal quality for legitimate users and jam signals for illegitimate ones. The method involves virtually partitioning the RIS to optimize signal and AN effects for legitimate and illegitimate users. Closed-form expressions for ergodic secrecy capacity (ESC) are derived and validated. Optimization problems are formulated to maximize network ESC by optimizing the 3D deployment of A-RIS and RIS portions under quality-of-service constraints. Simulation results show that the proposed joint A-RIS deployment and partitioning framework significantly improves network security compared to benchmarks. The proposed deployment approaches converge quickly, making it suitable for dynamic A-RIS deployment. The paper also introduces a novel RIS partitioning method that allows simultaneous improvement of the intended signal and AN for legitimate users and eavesdroppers, maximizing overall SC. The paper presents analytical expressions for ESC using Meijer-G functions and validates the double Nakagami-m model for RIS channel modeling. The results show that the proposed method outperforms existing models. The paper also formulates optimization problems to maximize ESC by jointly optimizing RIS partitioning and UAV deployment. The results demonstrate that the proposed method significantly improves ESC compared to benchmarks. The paper also presents closed-form solutions for optimal RIS portions and UAV deployment, showing that the proposed method achieves high ESC with low computational complexity. The paper also discusses the impact of various system parameters on ESC performance and validates the analytical findings through simulations. The paper concludes that the proposed method provides a promising solution for enhancing PLS in wireless networks.This paper proposes a novel approach for enhancing physical layer security (PLS) in wireless networks by combining reconfigurable intelligent surfaces (RIS) and artificial noise (AN). The proposed aerial RIS (A-RIS) concept uses a RIS-attached unmanned aerial vehicle (UAV) to improve signal quality for legitimate users and jam signals for illegitimate ones. The method involves virtually partitioning the RIS to optimize signal and AN effects for legitimate and illegitimate users. Closed-form expressions for ergodic secrecy capacity (ESC) are derived and validated. Optimization problems are formulated to maximize network ESC by optimizing the 3D deployment of A-RIS and RIS portions under quality-of-service constraints. Simulation results show that the proposed joint A-RIS deployment and partitioning framework significantly improves network security compared to benchmarks. The proposed deployment approaches converge quickly, making it suitable for dynamic A-RIS deployment. The paper also introduces a novel RIS partitioning method that allows simultaneous improvement of the intended signal and AN for legitimate users and eavesdroppers, maximizing overall SC. The paper presents analytical expressions for ESC using Meijer-G functions and validates the double Nakagami-m model for RIS channel modeling. The results show that the proposed method outperforms existing models. The paper also formulates optimization problems to maximize ESC by jointly optimizing RIS partitioning and UAV deployment. The results demonstrate that the proposed method significantly improves ESC compared to benchmarks. The paper also presents closed-form solutions for optimal RIS portions and UAV deployment, showing that the proposed method achieves high ESC with low computational complexity. The paper also discusses the impact of various system parameters on ESC performance and validates the analytical findings through simulations. The paper concludes that the proposed method provides a promising solution for enhancing PLS in wireless networks.