# The Economics of Autonomy: Real-Time Risk Indexing for Insurable AI-Driven 6G Systems

arXiv: https://arxiv.org/abs/2607.18267 · HTML: https://mlsanity.com/paper/2607.18267/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 16 references that are in the map.
- First posted on arXiv 22 May 2026.

## Builds on (most cited first, 4 of 4)

- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021)
- [Berlin V2X: A Machine Learning Dataset from Multiple Vehicles and Radio Access Technologies](https://mlsanity.com/paper/2212.10343/) (2022)
- [The Data that Drives Cyber Insurance: A Study into the Underwriting and Claims Processes](https://mlsanity.com/paper/2008.04713/) (2020)

## Abstract (arXiv)

The transition to sixth-generation (6G) networks transforms wireless infrastructure into a cognitive substrate supporting Vehicle-to-Everything (V2X), Industrial IoT (IIoT), and Integrated Sensing and Communication (ISAC). In this paradigm, autonomous agentic AI performs orchestration at millisecond scales, rendering traditional static governance frameworks fundamentally inadequate for risk management. This paper introduces GIRAF(Governance-Integrated Risk and Assurance Framework), a Governance-as-Code (GaC) framework for real-time risk quantification and trust modulation in agentic 6G systems. GIRAF derives a continuous Aggregate Risk Index (Rt) from machine-readable runtime signals, including epistemic confidence, network jitter, and verification latency. A core contribution is the formalization of the verification staleness trade-off, where safety mechanisms induce risk if computational latency exceeds 6G deadlines. We demonstrate that GIRAF identifies 'Confidence Gaps' discrepancies between agent reported certainty and environmental ground truth, triggering automated safety envelopes when conditions deteriorate. Crucially, GIRAF serves as the foundational governance groundwork and conceptual 'glue' that externalizes these technical risks into machine-readable telemetry. Through simulations with fine-tuned Large Language Models (LLMs), we validate that the framework preserves operational integrity while providing the essential actuarial baseline required for multi-stakeholder liability attribution and dynamic premium quantification in the 6G ecosystem.