Additive manufacturing (AM) process chain relies heavily on cloud resources and software programs. Cybersecurity has become a major concern for such resources. AM produces physical components, which can be compromised for quality by many other means and can be reverse engineered for unauthorized reproduction. This work is focused on taking advantage of layer-by-layer manufacturing process of AM to embed codes inside the components and reading them using image acquisition methods. The example of a widely used QR code format is used, but the same scheme can be used for other formats or alphanumeric strings. The code is segmented in a large number of parts for obfuscation. The results show that segmentation and embedding the code in numerous layers help in eliminating the effect of embedded features on the mechanical properties of the part. Such embedded codes can be used for parts produced by fused filament fabrication, inkjet printing, and selective laser sintering technologies for product authentication and identification of counterfeits. Post processing methods such as heat treatments and hot isostatic pressing may remove or distort these codes; therefore, analysis of AM method and threat level is required to determine if the proposed strategy can be useful for a particular product.