Privacy-Aware Agentic AI Frameworks for Secure Human-Agent Collaboration
Keywords:
privacy-aware agentic AI, human-agent collaboration, secure multi-agent systems, governance, federated learning, differential privacy, accountability, socio-technical infrastructureAbstract
Agentic artificial intelligence systems are increasingly conceived as autonomous or semi-autonomous actors that plan, remember, negotiate, and execute tasks across organizational boundaries. This shift from passive prediction to situated action creates a distinctive privacy problem: agents must collaborate with humans and other agents while continuously handling sensitive context, preferences, credentials, and inferred intent. This paper develops a system-level framework for privacy-aware agentic AI in secure human-agent collaboration. It argues that privacy cannot be treated as a post hoc filter or a single technical control; rather, it must be designed into the architecture, governance, and operational lifecycle of agentic infrastructures. The paper examines layered agent architectures, secure aggregation, federated learning, differential privacy, contextual integrity, accountability auditing, and policy compliance as interlocking concerns. It analyzes structural trade-offs among utility, latency, autonomy, explainability, robustness, and regulatory alignment. It also considers deployment realities such as edge-cloud coordination, model provenance, intermittent connectivity, energy sustainability, and multi-stakeholder governance. Through cross-domain comparisons in healthcare, finance, public services, and industrial operations, the paper shows that secure human-agent collaboration depends on negotiated disclosure, least privilege, purpose limitation, and auditable delegation. The conclusion proposes that privacy-aware agentic frameworks should be evaluated not only by task success but also by their capacity to preserve contextual norms, distribute accountability, and remain resilient under adversarial and institutional pressure.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
