Defending The AI-Powered Commerce Stack: A Security Framework For Prompt Injection, Review Integrity, And Privacy In Genai Retail Systems

Abstract

Generative AI is fundamentally changing digital retail through intelligent search, conversational assistants, personalized recommendations, and generating dynamic content. The use of generative AI has created significant security gaps that disrupt customer trust, regulatory compliance, and operational integrity. Prompt injection attacks exploit untrusted content in product descriptions and user-generated reviews to manipulate assistant behavior and trigger unauthorized actions. AI-generated synthetic reviews undermine rating authenticity and distort marketplace signals at an unprecedented scale. Data poisoning compromises catalog systems and vector embeddings that power recommendation engines, degrading relevance and introducing malicious content propagation. Privacy leaks emerge from over-permissioned tool access and inadequate PII protection in conversational contexts. This framework presents layered defense architectures addressing each threat vector through input isolation, provenance tracking, quarantine systems, and access minimization. Cross-cutting governance mechanisms integrate brand guardrails, policy-as-code enforcement, and human-in-the-loop controls. Observability infrastructure enables continuous monitoring through decision logging, drift detection, and executive dashboards. The framework provides actionable guidance for engineering, security, legal, and customer experience teams building resilient AI-powered commerce systems that balance innovation with protection against adversarial manipulation and privacy violations.