Additive manufacturing (AM) is a disruptive technology that holds many benefits for the Department of Defense throughout product lifecycles including supporting the digital century-series concept of aircraft acquisition. The variability of parts created by AM is challenging, and the Department of the Air Force lags behind other departments in its adoption of AM. The main barrier to taking advantage of these benefits is the lack of confidence in certifying AM parts for safety-critical flight applications. The airworthiness process relies on process controls, testing, and analysis for risk mitigation and flight certification. The Air Forces current path towards certification has been to closely define and control AM process parameters, lock down that process, and conduct thorough non-destructive testing on individual parts. A better path to certification is to mitigate risks through informed awareness based on sufficient analysis of data created during each parts build process based on the parts criticality. This paper offers a Predict-Built-Test-Validate model as a means to create and analyze a digital passport allowing for certification of parts and informing airworthiness. In-situ data collection, closed-loop control of build process parameters, and machine learning algorithms for defect detection all offer promising methods of collecting data during a build, controlling the process, and ensuring enough information is known about the part to support certification. This paper argues that the Department of the Air Force should not seek to certify individual AM processes or machines, but instead define technical data package requirements and certify processes of collecting and analyzing data whereby the properties of the AM part can be matched with the validated model ensuring it will meet designed performance requirements and be safe for flight.