Enterprise AI systems often process vast amounts of personal and sensitive data, making them vulnerable to breaches and misuse. Consider, for example, a financial institution that trains a predictive model on sensitive customer data that is to be shared with partner organizations. If the model is shared without proper precautions, the partner organization or a malicious employee may attempt to breach the privacy of the original training data by launching an attack to reconstruct this data from model parameters. Such vulnerabilities erode trust in enterprise and AI systems, may fail to comply with privacy legislation, and can lead to financial, intellectual property or reputational loss. Privacy is a cornerstone of ethical and responsible AI deployment. This chapter provides a guide to the tools necessary to navigate the landscape of AI privacy, ultimately facilitating broader and more secure AI adoption in the enterprise.