Adaptive Semantic Integrity and Privacy Shielding in Agentic AI Networks for Trustworthy 6G Cognitive Communication System

The 6G cognitive communication systems are evolving very fast and present novel autonomy, semantic awareness, and agent-driven decision intelligence. Since agentic AI systems dynamically communicate through sharing contextual knowledge to make optimal use of the communication resources, it allows the capabilities of self-learning, the integrity, privacy, and trust become a pressing issue. Traditional cryptographic and authentication systems are incapable of securing the changing semantic data structures and dispersed collaborative AI activities. The new 6G cognitive system is susceptible to semantic manipulations, adversarial reasoning, identity fraud, and privacy intrusion as a result of distributed reasoning, cross-agent cooperation, and real-time training. Such threats undermine reliability, transparency, and trust of the users. This paper proposes AI-SHIELD (Adaptive Intelligence Shield for Hierarchical Explainable Learning and Distributed Security), a new multilayer security model, that incorporates self-adaptive semantic validation, embedding of differentially private, cross-agent trust consensus, and explainable decision auditing. The AI-SHIELD is an active knowledge exchange assessor based on a hybrid semantic fingerprinting model and implements agent negotiation based on federated reinforcement learning at the cost of privacy. The results of simulation show decreased inference attacks by 41%, enhanced semantic consistency by 37%, and better metrics of explainability without compromising the communication latency. AI-SHIELD strengthens trust, resilience, and transparency of agent-based 6G cognitive networks to facilitate autonomous communications at large scale securely.