This paper proposes a secure communication system for a STAR-RIS (Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface) aided Integrated Sensing and Communication (ISAC) system. The system aims to maximize the long-term average security rate for legitimate users (LUs) while ensuring the echo signal-to-noise ratio (SNR) and rate constraints for LUs. The STAR-RIS is used to simultaneously transmit and reflect signals, enabling enhanced communication and sensing performance. The system employs energy splitting and time switching protocols to manage the STAR-RIS operations. The paper introduces two reinforcement learning (RL) algorithms, Deep Deterministic Policy Gradient (DDPG) and Soft Actor-Critic (SAC), to address the complex non-convex optimization problem of jointly designing the base station (BS) transmit beamforming and receive filter, as well as the STAR-RIS transmitting and reflecting coefficients. The DDPG algorithm is used for continuous action spaces, while the SAC algorithm is designed to maximize the entropy of the policy, leading to better exploration and learning efficiency. Simulation results show that the STAR-RIS significantly outperforms conventional RIS and dual-spliced RIS in terms of security rate and communication performance. The SAC algorithm demonstrates superior exploration capabilities and learning efficiency compared to DDPG, although it has higher computational complexity. The TS (Time Switching) protocol for STAR-RIS is found to be more effective than the ES (Energy Splitting) protocol in the ISAC system. The proposed algorithms are evaluated under various scenarios, including different numbers of STAR-RIS elements and BS transmit power levels. The results indicate that the STAR-RIS-assisted ISAC system achieves higher security rates and better performance compared to conventional systems. The study highlights the potential of STAR-RIS in enhancing the security and efficiency of integrated sensing and communication systems in 6G networks.This paper proposes a secure communication system for a STAR-RIS (Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surface) aided Integrated Sensing and Communication (ISAC) system. The system aims to maximize the long-term average security rate for legitimate users (LUs) while ensuring the echo signal-to-noise ratio (SNR) and rate constraints for LUs. The STAR-RIS is used to simultaneously transmit and reflect signals, enabling enhanced communication and sensing performance. The system employs energy splitting and time switching protocols to manage the STAR-RIS operations. The paper introduces two reinforcement learning (RL) algorithms, Deep Deterministic Policy Gradient (DDPG) and Soft Actor-Critic (SAC), to address the complex non-convex optimization problem of jointly designing the base station (BS) transmit beamforming and receive filter, as well as the STAR-RIS transmitting and reflecting coefficients. The DDPG algorithm is used for continuous action spaces, while the SAC algorithm is designed to maximize the entropy of the policy, leading to better exploration and learning efficiency. Simulation results show that the STAR-RIS significantly outperforms conventional RIS and dual-spliced RIS in terms of security rate and communication performance. The SAC algorithm demonstrates superior exploration capabilities and learning efficiency compared to DDPG, although it has higher computational complexity. The TS (Time Switching) protocol for STAR-RIS is found to be more effective than the ES (Energy Splitting) protocol in the ISAC system. The proposed algorithms are evaluated under various scenarios, including different numbers of STAR-RIS elements and BS transmit power levels. The results indicate that the STAR-RIS-assisted ISAC system achieves higher security rates and better performance compared to conventional systems. The study highlights the potential of STAR-RIS in enhancing the security and efficiency of integrated sensing and communication systems in 6G networks.