Generative AI Security: The Shared Responsibility Framework (www.enkryptai.com)

A new "Shared Responsibility Framework" for generative AI security lays out who is accountable for safety, compliance, and risk across the model lifecycle — from base‑model alignment through deployment and monitoring. Framed around continuous feedback loops, the paper assigns roles to providers (patch and safety-layer updates), developers (integration and testing), organizations (filtering, pausing endpoints, governance) and end users (surfacing inappropriate outputs). It’s aimed at CISOs and engineering leaders to clarify operational responsibilities so enterprises can safely adopt GenAI without assuming vendors alone will manage every failure mode. Technically, the framework centers on practical controls and questions: how training data is vetted for bias, poisoning or PII; defenses against prompt injection, jailbreaking and adversarial attacks; hardening inference APIs against DDoS and unauthorized access; and robust versioning, changelogs and patching processes. It also covers guardrails for domain‑specific compliance (financial, HIPAA), agent security (API call controls, transaction approvals and kill switches), monitoring/auditability, and alignment with evolving regulations (EU AI Act, NIST AI RMF, SEC). Case studies and contributions from industry and security leaders illustrate implementation tradeoffs. The net implication: secure GenAI requires coordinated vendor–developer–enterprise workflows, clear SLAs and observability so safety becomes an operational, not just technical, responsibility.