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Enhancing AI Privacy: Federated Learning and Differential Privacy in Machine Learning Sep 12th 2025, 20:00 by Shaurya Jain Privacy-preserving techniques are keeping your data safe in the age of AI. In particular, federated learning (FL) keeps data local, while differential privacy (DP) strengthens individual privacy. In this article, we will discuss challenges associated with this, practical tools, and emerging trends like secure aggregation and personalized FL for stronger privacy in AI. Introduction Shifting Left in Software Testing: Integrating AI-Driven Early Defect Detection into Agile Development Workflows Sep 12th 2025, 19:00 by Gopinath Kathiresan Nobody likes bugs. Not the developers who accidentally write them, not the testers who hunt them down, and certainly not the users who stumble into them. But here's the kicker — according to IBM, 5 to 30% of software defects still slip into production, costing companies up to 30 times more to fix post-release than if caught early.  The statistic highlights the essential nature of discovering software defects at the earliest point in development. Testing processes must shift left in development lifecycles according to the shifting left concept.  AI for Ensuring Data Integrity and Simplifying Migration in Microservices Ecosystems Sep 12th 2025, 18:00 by Nilesh Charankar , Anand Singh Organizations implement microservices architectures to create adaptable, scalable applications that function efficiently, but must overcome significant challenges to maintain data accuracy during migration. The data sharing mechanism of microservices architecture enables beneficial advantages but produces complex data accuracy and reliability challenges. Artificial intelligence functions as a powerful instrument that enables users to manage data integrity and optimize the data migration process. AI delivers efficient solutions for microservices ecosystems to handle data integrity issues and execute data migration operations effectively. Deploying AI Models in Air-Gapped Environments: A Practical Guide From the Data Center Trenches Sep 12th 2025, 17:00 by Siva Kiran Nandipati Organizations are eager to harness machine learning and deep learning — but not everyone is racing to the cloud. For highly regulated industries, government entities, and security-first organizations, air-gapped environments remain essential. The question many are now asking is: How do we bring AI into air-gapped or isolated systems, and do it securely, reliably, and scalably? After nearly two decades managing on-prem data centers and private cloud environments, I've seen the evolution — from physical servers and VLANs to containerized workloads and AI clusters. In this article, I'll share practical strategies for deploying AI models in air-gapped environments, with a focus on lessons learned, key technical considerations, and actionable guidance for both engineers and decision-makers. Security Concerns in Open GPTs: Emerging Threats, Vulnerabilities, and Mitigation Strategies Sep 12th 2025, 16:00 by Vijay Oggu With the increasing use of Open GPTs in industries such as finance, healthcare, and software development, security concerns are growing. Unlike proprietary models, open-source GPTs allow greater customization but also expose organizations to various security vulnerabilities. This analysis explores real-world breaches, case studies, and advanced security techniques to safeguard Open GPT deployments.

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