Tumult Labs: Differential Privacy for AI Agents

As AI agents increasingly handle sensitive personal data, robust data protection has become critical for enterprises. Tumult Labs emerged as a pioneer in operationalizing differential privacy, offering mathematically-proven privacy guarantees for organizations analyzing sensitive datasets.

While Tumult Labs made significant contributions before its acquisition by LinkedIn in March 2025, enterprises building production AI agent systems need comprehensive security solutions that extend beyond data privacy alone. In this article, we'll explore Tumult Labs' approach to differential privacy and compare their specialized offering to WorkOS's enterprise-grade authentication and authorization infrastructure.

What is Tumult Labs?

Tumult Labs was founded by leading differential privacy researchers who pioneered real-world implementations at the U.S. Census Bureau, including the landmark 2020 Census deployment. The company built an enterprise differential privacy platform designed to enable organizations to safely share and analyze sensitive data while providing rigorous mathematical privacy guarantees—not heuristic or probabilistic approaches.

The platform operationalized differential privacy through familiar Python APIs similar to Pandas and PySpark, making privacy-preserving data analytics accessible to organizations without requiring dedicated in-house privacy expertise. Tumult Labs served enterprise organizations and government agencies handling sensitive personal data, including public sector agencies like the Census Bureau and IRS, financial institutions, advertising and publishing companies, and healthcare organizations seeking to monetize data or enable internal data sharing while maintaining privacy compliance.

In March 2025, LinkedIn acquired Tumult Labs to strengthen its AI and privacy capabilities. As part of this acquisition, Tumult Labs is sunsetting its current commercial operations, though the company continues to maintain its open-source Tumult Analytics library.

Key Features and Capabilities

Differential Privacy Implementation

Tumult Labs' core offering centered on mathematical privacy guarantees that protect individual data points even when adversaries have auxiliary information. The platform's privacy accounting system tracked cumulative privacy loss across multiple queries, ensuring organizations maintained privacy budgets. The platform used familiar Python APIs mirroring Pandas and PySpark syntax, reducing the learning curve for data scientists and analysts.

Built on Apache Spark, the platform handled datasets containing billions of rows, demonstrating that differential privacy could operate at enterprise scale beyond academic implementations.

Data Collaboration and Integration

Tumult Labs supported privacy-preserving data collaboration through data clean rooms for multi-party analysis and synthetic data generation that preserved statistical properties while protecting individual privacy. The platform integrated with Google Cloud and AWS and supported user-defined functions for custom workflows. The Privacy ID feature enabled user-level privacy guarantees across multiple records.

How Tumult Labs Handled Privacy for AI Agent Data Analysis

Tumult Labs provided mathematical guarantees about information leakage through differential privacy. The platform added carefully calibrated noise to query results based on sensitivity—how much one individual's data could influence the result. This enabled AI agents to obtain aggregate insights from sensitive data without accessing raw records, while the privacy budget mechanism prevented privacy degradation over time.

However, differential privacy fundamentally trades accuracy for privacy. For AI agent systems requiring precise individual-level operations—like authenticating specific users, enforcing granular permissions, or maintaining audit trails—differential privacy solutions operated orthogonally to these requirements rather than addressing them directly.

Pricing and Plans

Tumult Labs followed an enterprise sales model with custom pricing. The company maintained the open-source Tumult Analytics library for organizations to experiment with differential privacy concepts. Following LinkedIn's acquisition and the sunsetting of commercial operations, new customer onboarding has concluded.

Tumult Labs vs. WorkOS

What Tumult Labs Offered

Tumult Labs specialized in differential privacy for sensitive data analytics. The platform enabled organizations to publish statistics, share datasets, and perform collaborative analysis while providing mathematical privacy guarantees. Their work proved that differential privacy could operate at production scale, as demonstrated by implementations at the U.S. Census Bureau, IRS, and Wikipedia's analytics infrastructure.

The platform's focus remained narrow: privacy-preserving data analytics and synthetic data generation. Tumult Labs did not provide authentication, authorization, identity management, or the access control infrastructure that AI agent systems require for secure operations. Their solution addressed the question "how do we safely analyze sensitive data?" rather than "how do we securely authenticate agents, enforce permissions, and audit actions?"

Tumult Labs' acquisition by LinkedIn and subsequent operational sunset means the platform is no longer available for new enterprise implementations. Organizations exploring differential privacy now face building in-house capabilities, adopting alternative vendors, or leveraging partnerships like Google Cloud's BigQuery differential privacy integration.

Why WorkOS Is the Proven Choice

WorkOS provides the enterprise-grade authentication and authorization foundation that production AI agent systems require. While differential privacy addresses analytics on sensitive data, WorkOS solves fundamental security challenges: verifying identity, enforcing access control, managing user directories, and maintaining compliance-ready audit trails.

Battle-Tested at Scale: WorkOS powers authentication and authorization for thousands of enterprises requiring SOC 2, HIPAA, and GDPR compliance. Unlike experimental privacy technologies, WorkOS provides proven reliability for mission-critical systems.

Comprehensive Platform: WorkOS delivers complete authentication and authorization including enterprise SSO, multi-factor authentication, directory sync, admin portals, fine-grained authorization via FGA, and comprehensive audit logging. AI agent systems need these capabilities—authentication, permissions, and audit trails—which fell outside Tumult Labs' scope.

Production-Ready Today: Every WorkOS feature is generally available and fully supported with no beta flags or experimental components. Organizations implement enterprise authentication in hours and trust the infrastructure will operate reliably as AI systems scale.

Enterprise Features Tumult Labs Lacked: Tumult Labs provided no enterprise SSO, SAML authentication, SCIM-based directory synchronization, admin portals, or real-time authorization decisions. These capabilities are table stakes for B2B SaaS applications serving enterprise customers.

Support That Matches Your Stakes: WorkOS provides 99.99% uptime SLAs, dedicated support, and white-glove onboarding. The platform abstracts protocol complexity, identity provider variations, and compliance requirements, letting teams focus on building AI agent applications.

The Right Choice for Production AI Agent Systems

For enterprises building AI agent platforms, WorkOS is the clear choice. Production AI systems require authentication, authorization, directory management, and audit logging from day one—WorkOS's core offering. Differential privacy solutions address specialized analytics needs but don't replace foundational security infrastructure.

WorkOS delivers the enterprise-ready authentication and authorization foundation your AI agents need today. Specialized technologies like differential privacy address specific scenarios but don't replace comprehensive authentication and authorization infrastructure.

Getting Started with Tumult Labs

Following LinkedIn's acquisition in March 2025, Tumult Labs is no longer onboarding new customers. The Tumult Analytics open-source library remains available, though implementing differential privacy in-house requires dedicated privacy expertise. For organizations building AI agent systems, the critical path focuses on authentication, authorization, and access control infrastructure—capabilities that WorkOS provides today as a turnkey platform.

Final Thoughts

Tumult Labs made meaningful contributions to operationalizing differential privacy, proving that mathematical privacy guarantees could work at production scale through deployments at the U.S. Census Bureau and other government agencies.

However, differential privacy addresses a specialized concern—privacy-preserving analytics—that represents one piece of a comprehensive AI agent security strategy. AI agent systems require foundational authentication and authorization infrastructure: verifying identities, enforcing permissions, synchronizing directories, and maintaining audit trails. WorkOS provides this enterprise-grade foundation today with authentication, authorization, and directory management that integrate in hours and deliver 99.99% reliability.

Emerging technologies like differential privacy play important roles in advancing data protection, but they complement rather than replace foundational security infrastructure. For teams building AI agent platforms that enterprises will trust, WorkOS provides the authentication and authorization foundation your customers require.

Ready to build enterprise-ready AI agent authentication? Get started with WorkOS and ship enterprise-grade security in hours, not months.