# A Human-LLM Teaming Framework for Privacy Risk Analysis: An Illustration with CBDC-Based Welfare Schemes

arXiv: https://arxiv.org/abs/2608.16461 · HTML: https://mlsanity.com/paper/2608.16461/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 17 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Look Before You Leap: An Exploratory Study of Uncertainty Measurement for Large Language Models](https://mlsanity.com/paper/2307.10236/) (2023)
- [Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits](https://mlsanity.com/paper/2403.07213/) (2024)
- [LLMs in the SOC: An Empirical Study of Human-AI Collaboration in Security Operations Centres](https://mlsanity.com/paper/2508.18947/) (2025)
- [A2C: A Modular Multi-stage Collaborative Decision Framework for Human-AI Teams](https://mlsanity.com/paper/2401.14432/) (2024)
- [LLM Access Shield: Domain-Specific LLM Framework for Privacy Policy Compliance](https://mlsanity.com/paper/2505.17145/) (2025)
- [PILLAR: an AI-Powered Privacy Threat Modeling Tool](https://mlsanity.com/paper/2410.08755/) (2024)
- [Privy: Envisioning and Mitigating Privacy Risks for Consumer-facing AI Product Concepts](https://mlsanity.com/paper/2509.23525/) (2025)
- [Towards AI-Driven Human-Machine Co-Teaming for Adaptive and Agile Cyber Security Operation Centers](https://mlsanity.com/paper/2505.06394/) (2025)

## Abstract (arXiv)

Central Bank Digital Currency (CBDC)-based welfare schemes may be potentially privacy invasive as they process significant volumes of beneficiary personal data and lead to privacy harms such as surveillance, discrimination and stigmatization. Such welfare delivery schemes involve complex digital ecosystems and large number of stakeholders. Consequently, to examine their privacy risks, privacy risk assessments require extensive information gathering and synthesis, complex reasoning, scenario explorations, contextual evaluation and human judgement. Thus, they present ideal scenarios for human-LLM teaming, where effective integration of complementary human and LLM capabilities can yield an outcome far superior to either human-only or LLM-only assessments. In this paper, we propose a first human-LLM teaming framework for the systematic privacy risk analysis methodology called PRIAM. The framework specifies an iterative collaborative process in which the LLM processes large-scale documentary evidence to produce initial outputs, which are then interpreted and evaluated by human experts who direct their further refinement by the LLM and exercise their judgement to finalize the output. We illustrate the framework on the data characterization activity of PRIAM using a CBDC-based welfare scheme use case. The illustration demonstrates that while LLMs generate the initial data categories and assign initial values to data attributes, human experts evaluate and provide feedback to refine them, distinguishing documented evidence from inferences, identifying information gaps, and flagging unsupported or ambiguous outputs. This framework serves as a foundational contribution towards human-AI teaming for privacy risk assessments.