nd NFV together introduce new opportunities to systematically automate network functions and to introduce new flexibility and rapid, dynamic responses to changing network and customer conditions. However, achieving this requires sophisticated intelligence-knowing what to automate, when, and how across a large number of VNFs and PNFs. Automatically resolving network issues, for example, will require analytics and policies to detect anomalous network conditions, to accurately correlate events to effectively localize issues and sophisticated logic to reason as to what actions should be taken. If the analytics fail to accurately detect and localize an event, or if the responses are inappropriate, then erroneous actions could be taken, which could poten- tially introduce more harm than good. Thus, there are significant challenges associated with how to identify effective intelligence (analytics, workflows, policies, and available platform actions) that should be introduced into the ONAP ecosystem to enable operations automation. How can we scalably learn the policy rules required here? Can we leverage historical (PNF) rules and operational experiences? How can we create policies that are Building the Network of the Future reusable across diverse VNFs? How can machine learning be used to automatically iden- tify policies, such as correlation rules and actions to be taken in response to detected condi- tions? And how can we ensure the integrity of the ecosystem in the face of bad or missing data, which is inherent in large-scale systems? 3. Safe policies: As discussed in Section 15.2.4.1, policies introduce great flexibility through the ability to rapidly enable new functionality via ONAP. However, with this flexibility comes risk. How can we most effectively balance the benefits versus risk here, and create and use technology to minimize the risk? How can we most effectively police and man- age policies-ensuring that (bad) policies that can cause harm are not introduced to the system, and carefully managing interaction