Overcome Barriers for Global Insights
Build Bridges
with Federated AI
Overcome Regulatory Barriers with Federated AI
In a world that is more interconnected than ever, global organizations must derive cross-border insights to stay competitive. However, strict data protection laws create significant obstacles, making it nearly impossible to transfer raw data across borders without facing major compliance challenges. Federated AI offers a solution by allowing organizations to generate global insights without moving raw data, effectively sidestepping these regulatory hurdles. Instead of struggling with regulations like the EU Dual-Use Regulation, U.S. Export Administration Regulations (EAR), ITAR, China's Data Security Law (DSL), and Cybersecurity Law (CSL), Federated AI enables secure, decentralized global learning.
Strict Regulations and Conflicting Compliance Standards
Cross-Border Data Transfer Is a Regulatory Nightmare
Cross-border data transfers between regions like the EU, U.S., and China come with a host of compliance issues. Each region has unique data protection laws, including the EU Dual-Use Regulation, U.S. EAR and ITAR, and China's DSL and CSL. These regulations make it very challenging, if not impossible, to transfer raw data across borders, particularly when aiming to generate cross-border insights. As a result, centralizing data for global insights not only increases compliance challenges but also introduces operational difficulties, security risks, and the potential for hefty fines for non-compliance.
Bridge the Barriers
Federated AI for Cross-Border Learning and Insights
Federated AI allows global organizations to train machine learning models collaboratively across sites without sharing raw data. Each site trains the model on its local data, and only model updates—such as gradients—are shared. This ensures that the data never leaves its original location, which means insights can be gathered across borders without violating compliance regulations.
Simplified Compliance: Federated AI eliminates the need for cross-border data transfers, making it easier for organizations to comply with regulations such as the EU Dual-Use Regulation, U.S. EAR and ITAR, and China's DSL and CSL.
Enhanced Data Security and Privacy: By keeping raw data within its original environment, Federated AI minimizes the risk of exposure and data breaches.
Unlock Cross-Border Insights: Federated AI enables organizations to derive valuable insights across regions like the EU, U.S., and China without getting tangled in complex legal framework
Compliance and Security Without Compromise
Built to protect both your data and AI models at every step. Certified to ISO 27001 standards, our platform employs advanced security features, including encryption at rest and in transit, automated differential privacy (Auto DP), and role-centric attribute-based access control (RABAC). Katulu ensures the highest level of protection, allowing you to comply with regulations like GDPR, HIPAA, export control, and dual-use restrictions — all while enabling AI innovation.
Overcome AI Barriers and Drive Results
Deliver real business impact with efficient, cost-effective AI platform that scales effortlessly.
Reduce Cost and Deliver AI at the Speed of Insight
Skip the lengthy data transfers and regulatory hurdles—Federated AI allows you to run AI directly where your data resides, accelerating outcomes while staying fully compliant
Don't put all your eggs in one basket.
Keep your data secure by avoiding centralization. Federated AI reduces exposure to threats while providing the insights you need.
Uncover the Hidden Data Potential
Transform underutilized sensitive data into strategic assets. Federated AI unlocks insights securely and efficiently by bringing AI to your data.
From Challenges to Results
How Siemens Leveraged Katulu
to Achieve AI Excellence
Siemens faced barriers in developing AI models that met their high performance standards. By leveraging Katulu’s Federated AI Platform — and with that, access to a much larger dataset — they achieved their target F1 score of 99,2%. This led to higher first-pass yields, streamlined quality processes, and reduced manual labor — all without disrupting their existing operations and data infrastructure.
Download the Siemens White Paper
Learn how Siemens and Katulu overcame significant challenges in PCB production using Federated Learning. This white paper provides in-depth insights into the technical implementation, results, and how this collaboration serves as a blueprint for future industrial AI projects.
Download
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Don't let data silos, privacy concerns, or high costs slow you down. Get your AI models ready for deployment within today. Focus on building deep learning algorithms while we handle the rest. Our platform ensures fast, secure and scalable results - so you can innovate at scale, not tweak.
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