PolicyGuard-RAG: Compliance-Aware Retrieval-Augmented Recommendation Systems for AI-Generated Commercial Content with Verifiable Governance Mechanisms (International Journal of Artificial Intelligence Engineering and Systems, 2026)
The proliferation of generative artificial intelligence in commercial content creation has introduced unprecedented challenges in ensuring regulatory compliance, content authenticity, and ethical governance. Existing retrieval-augmented generation (RAG) systems, while effective at grounding generated outputs in external knowledge bases, lack native mechanisms for enforcing policy constraints, verifying provenance, or auditing recommendation decisions in a verifiable manner. This paper presents PolicyGuard-RAG, a novel architecture that integrates compliance-aware retrieval and recommendation with verifiable governance mechanisms for AI-generated commercial content. The system extends conventional RAG pipelines by embedding policy enforcement layers that operate at retrieval, generation, and post-hoc verification stages. A unified policy representation using W3C ODRL and C2PA content credentials enables micro-licensing and provenance tracking. A path-level intervention framework, inspired by recent work on safety in large foundation models, provides robust enforcement even under adversarial inputs. The architecture supports multi-tenant deployment for small and medium businesses through a standardized API layer that abstracts governance complexity. We analyze structural trade-offs between retrieval fidelity, generation creativity, and policy stringency. Deployment considerations include latency constraints, federated audit trails, and sustainability of on-chain verification. Robustness against backdoor attacks and fairness in cross-domain recommendations are addressed through prototype consistency methods and representation debiasing. Policy implications are discussed with respect to regulatory frameworks such as the EU AI Act and emerging standards for synthetic content labeling. Our evaluation on commercial content benchmarks demonstrates that PolicyGuard-RAG achieves high compliance rates with minimal degradation in relevance metrics. The system offers a scalable, auditable, and policy-compliant foundation for AI-generated content in regulated industries.
Keywords: Retrieval-Augmented Generation, AI Governance, Content Compliance, Verifiable Mechanisms, Recommendation Systems, Policy Enforcement, C2PA, ODRL