ange of techniques to ensure "safe policies"-minimizing the risk associ- ated with introducing policies that govern the automation that ONAP enables. ONAP uses advanced analytics to validate individual and combinations of policies before field deployment. Other mecha- nisms enable policies to be safely introduced into the system in a controlled fashion, and to retire expired policies. During runtime, "guard rails" limit the potential impact of inappropriate policies. For example, a guard rail may prevent too many VNFs from being taken out of service at a time, which would impact the network's ability to carry traffic. The policy framework empowers operators and designers to control ONAP's behavior. However, the domain knowledge captured in ONAP policies is often distributed across many network opera- tors and engineers, making it a challenge to collate. Machine learning techniques come to the rescue here by helping service providers automatically learn policies. In the case of control loops, machine learning enables signatures and responses to be automatically captured. The policy platform plays an important role in realizing the network cloud vision of closed- loop automation and lifecycle management. The policy platform's main objective is to control/ affect/modify the behavior of the complete network cloud environment (NFV infrastructure, VNF, ONAP, etc.) using field configurable policies/rules without always requiring a development cycle. Conceptually, "policy" is an approach to intelligently constrain and/or influence the behaviors of functions and systems. Policy permits simpler management/control of complex mechanisms via abstraction. Incorporating high-level goals, a set of technologies and architecture, and supportive methods/patterns, policy is based on easily updateable conditional rules, which are implemented in various ways such that policies and resulting behaviors can be quickly changed as needed. Policy is used to control, influence, and help ensure compliance with goals. "A policy" in the sense o