  Nowadays, organizations collect vast quantities of sensitive information in
`Enterprise Resource Planning' (ERP) systems, such as accounting relevant
transactions, customer master data, or strategic sales price information. The
leakage of such information poses a severe threat for companies as the number
of incidents and the reputational damage to those experiencing them continue to
increase. At the same time, discoveries in deep learning research revealed that
machine learning models could be maliciously misused to create new attack
vectors. Understanding the nature of such attacks becomes increasingly
important for the (internal) audit and fraud examination practice. The creation
of such an awareness holds in particular for the fraudulent data leakage using
deep learning-based steganographic techniques that might remain undetected by
state-of-the-art `Computer Assisted Audit Techniques' (CAATs). In this work, we
introduce a real-world `threat model' designed to leak sensitive accounting
data. In addition, we show that a deep steganographic process, constituted by
three neural networks, can be trained to hide such data in unobtrusive
`day-to-day' images. Finally, we provide qualitative and quantitative
evaluations on two publicly available real-world payment datasets.
