Input 1 and OneShield have partnered to integrate payment and premium finance solutions into OneShield's platform, simplifying processes for insurers.

Rick Becker

The adoption of Large Language Models (LLMs) has revolutionized AI applications but poses significant challenges in safeguarding user privacy. Ensuring compliance with privacy regulations such as GDPR and CCPA while addressing nuanced privacy risks requires robust and scalable frameworks. This paper presents a detailed study of OneShield Privacy Guard, a framework designed to mitigate privacy risks in user inputs and LLM outputs across enterprise and open-source settings. We analyze two real-world deployments:(1) a multilingual privacy-preserving system integrated with Data and Model Factory, focusing on enterprise-scale data governance; and (2) PR Insights, an open-source repository emphasizing automated triaging and community-driven refinements. In Deployment 1, OneShield achieved a 0.95 F1 score in detecting sensitive entities like dates, names, and phone numbers across 26 languages, outperforming state-of-the-art tool such as StarPII and Presidio by up to 12\%. Deployment 2, with an average F1 score of 0.86, reduced manual effort by over 300 hours in three months, accurately flagging 8.25\% of 1,256 pull requests for privacy risks with enhanced context sensitivity. These results demonstrate OneShield's adaptability and efficacy in diverse environments, offering actionable insights for context-aware entity recognition, automated compliance, and ethical AI adoption. This work advances privacy-preserving frameworks, supporting user trust and compliance across operational contexts.

OneShield, a Marlborough-based insurance software firm, is looking to enter its next phase of growth after appointing a new CEO and creating the new role of chief innovation officer.

The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safety, privacy, and ethics have emerged, and all the key actors are working to address these issues with protective measures for their own models and standalone solutions. The constantly evolving nature of LLMs makes the task of universally shielding users against their potential risks extremely challenging, and one-size-fits-all solutions unfeasible. In this work, we propose OneShield, our stand-alone, model-agnostic and customizable solution to safeguard LLMs. OneShield aims to provide facilities for defining risk factors, expressing and declaring contextual safety and compliance policies, and mitigating LLM risks, with a focus on each specific customer. We describe the implementation of the framework, the scalability considerations and provide usage statistics of OneShield since its first deployment.

OneShield, a provider of core insurance technology for property and casualty carriers and MGAs, launched the OneShield AI Hub — a centralized platform for building

Creator, Author and Presenter: Shubhi Asthana, IBM Research Our thanks to USENIX for publishing their Presenter’s outstanding USENIX Enigma ’23 Conference content on the organization’s’ YouTube channel.

Medical Assurance Company of Mississippi adopts OneShield Market Solutions for core system modernization.

The platform has been selected by a Michigan-based specialty insurer to support AI-enabled operations.

The centralized platform aims to enable carriers, MGAs, and partners to build and deploy AI solutions within a governed ecosystem.