Post-quantum cryptography combined with neuro-symbolic AI to safeguard sensitive psychiatric therapy models against future cyber threats

The protection of psychiatric therapy models presents a critical challenge as healthcare increasingly adopts digital platforms for diagnosis, treatment personalization, and predictive analytics. These models contain highly sensitive patient data and therapeutic strategies that must remain secure against both current and future cyber threats. Traditional cryptographic safeguards, while effective today, are vulnerable to the emerging capabilities of quantum computing, which threatens to undermine widely deployed encryption algorithms. From a broader perspective, post-quantum cryptography offers mathematically resilient encryption schemes that are designed to withstand attacks from quantum adversaries. Simultaneously, neuro-symbolic artificial intelligence (AI) combines the adaptive power of neural networks with the interpretability and reasoning strengths of symbolic systems, enabling intelligent detection of anomalous behaviors and policy-driven access control. Narrowing the focus, this study explores a security architecture that integrates post-quantum cryptographic algorithms with neuro-symbolic AI to protect psychiatric therapy models. The proposed framework uses lattice-based cryptography for data confidentiality, signature schemes for secure model provenance, and hybrid AI systems for continuous monitoring of access and threat patterns. By embedding reasoning-driven safeguards alongside machine learning detection, the framework offers both proactive and explainable defense mechanisms. Furthermore, compliance with privacy regulations is supported through automated verification of access policies and transparent audit trails. This dual approach not only shields therapy models from quantum-era decryption risks but also ensures ethical and trustworthy deployment in sensitive mental health domains. Ultimately, safeguarding psychiatric therapy models through post-quantum and neuro-symbolic methods contributes to future-ready healthcare systems that uphold both data integrity and patient trust.