The Right Tool for the Job: On the Selection of Mitigations for GenAI Privacy Threats

Generative Artificial Intelligence (GenAI) has rapidly evolved from an experimental technology into a foundational component of modern software systems. However, as its adoption grows, protecting sensitive personal data becomes increasingly challenging. Specifically, GenAI systems not only amplify traditional privacy threats but also introduce new inference-based risks, such as constructing detailed user profiles from seemingly harmless inputs. In response, privacy threat modeling frameworks are beginning to capture GenAI-specific privacy threats with finer granularity. At the same time, a growing number of mitigation techniques have been proposed to address these threats. However, although knowledge of both threats and mitigations continues to mature, the problem- and solution-space have developed largely independently. This position paper argues that the primary challenge in GenAI privacy engineering is not the lack of knowledge about privacy threats or mitigation techniques, but the missing bridge between them. We decompose this gap into three sub-problems: (i) lack of fine-grained threat-to-mitigation mapping for GenAI systems, (ii) inapplicable solution-space assumptions in the GenAI context, and (iii) prioritization difficulty under GenAI constraints. We derive four recommendations for future mitigation-selection approaches, and outline a suggested approach that extends established threat-to-mitigation mapping methods to GenAI-specific threat characteristics. We propose a research agenda toward more systematic privacy mitigation selection for GenAI-based systems.

As a position paper, this work does not follow a traditional empirical methodology. Instead, it synthesizes existing knowledge from privacy threat modeling and mitigation research to identify and articulate a critical gap. The authors conduct a conceptual analysis of the current state of GenAI privacy engineering, drawing on established frameworks and techniques. They decompose the identified gap into three sub-problems and derive four recommendations for future mitigation-selection approaches. The paper outlines a suggested approach that extends established threat-to-mitigation mapping methods to GenAI-specific threat characteristics, proposing a research agenda toward more systematic privacy mitigation selection for GenAI-based systems. The methodology is thus argumentative and agenda-setting, aiming to stimulate further research rather than present new experimental results.