Online websites use cookie notices to elicit consent from the users, as required by recent privacy regulations like the GDPR and the CCPA. Prior work has shown that these notices are designed in a way to manipulate users into making website-friendly choices which put users’ privacy at risk. In this work, we present CookieEnforcer, a new system for automatically discovering cookie notices and extracting a set of instructions that result in disabling all non-essential cookies. In order to achieve this, we first build an automatic cookie notice detector that utilizes the rendering pattern of the HTML elements to identify the cookie notices. Next, we analyze the cookie notices and predict the set of actions required to disable all unnecessary cookies. This is done by modeling the problem as a sequence-to-sequence task, where the input is a machine-readable cookie notice and the output is the set of clicks to make. We demonstrate the efficacy of CookieEnforcer via an end-to-end accuracy evaluation, showing that it can generate the required steps in 91% of the cases. Via a user study, we also show that CookieEnforcer can significantly reduce the user effort. Finally, we characterize the behavior of CookieEnforcer on the top 100k websites from the Tranco list, showcasing its stability and scalability.