PEPR '25: 2025 USENIX Conference on Privacy Engineering Practice and Respect

Shubhi Asthana, IBM Research


The adoption of Large Language Models (LLMs) has revolutionized AI applications but has introduced complex challenges in enforcing scalable and adaptive privacy safeguards. This talk presents OneShield Privacy Guard, a framework engineered to mitigate privacy risks through context-aware guardrails across the LLM lifecycle—input preprocessing, inference, and output sanitization—while leveraging automated risk assessment for continuous refinement.

The talk explores two key deployments of OneShield Privacy Guard. The first deployment focuses on an enterprise-scale multilingual system for data governance, demonstrating enhanced PII detection accuracy and optimized privacy-utility tradeoffs compared to existing solutions. OneShield's integration provided real-time privacy enforcement, improving compliance adherence in high-volume enterprise environments.

The second deployment highlights an open-source implementation for automated privacy risk triaging, where OneShield reduced manual intervention in privacy-sensitive pull requests by 30% while maintaining compliance precision. This deployment demonstrates its adaptability in privacy-first software development workflows, enabling efficient and automated risk mitigation.

These deployments illustrate OneShield's scalability and deployment flexibility in enterprise and open-source ecosystems. Attendees will gain insights into its technical architecture, tradeoff considerations, and deployment challenges, equipping them with strategies for building automated, high-fidelity privacy safeguards for real-world AI applications.


https://www.usenix.org/conference/pepr25/presentation/asthana