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Securing the Model Context Protocol (MCP): New AI Security Risks in Agentic Workflows Oct 2nd 2025, 19:00 by Pranjal Sharma The Model Context Protocol (MCP), introduced in late 2024, is a significant move forward towards transforming the agentic AI revolution by providing a mechanism for them to connect with enterprise tools, APIs, and databases. The protocol presents a standardized way for large language models (LLMs) and business workflows to communicate with business systems, databases, APIs, and even development environments. Just as Open Database Connectivity (ODBC) standardized access to databases, MCP offers a standard way for AI agents to interact with data and applications across an enterprise.  However, as MCP is adopted across organizations, we are also seeing the introduction of new types of security risks that did not exist before. The same abilities that make MCP so powerful, such as bidirectional communication, agentic features, tool descriptions, etc., all introduce a new threat landscape that cybersecurity professionals may not be ready for. Testing Updates in Insert-Only Ledger Tables and Understanding Updates in Updatable Ledger Tables Oct 2nd 2025, 18:00 by arvind toorpu In SQL Server, ledger tables offer powerful tamper-evident functionality, which is essential for systems that require high levels of trust and auditability. Two distinct types serve different needs: insert-only ledger tables and updatable ledger tables. Insert-only tables enforce strict immutability, allowing data to be added but never altered or deleted, making them ideal for transaction logs or event sourcing. Conversely, updatable ledger tables permit modifications and deletions while meticulously maintaining a cryptographically verifiable history of all changes, much like a blockchain. This article provides a hands-on demonstration of these principles. We will test update operations against insert-only tables to confirm their constraints and then explore how updates are seamlessly and transparently managed in updatable ledger tables, complete with practical examples. AI Infrastructure Guide: Tools, Frameworks, and Architecture Flows Oct 2nd 2025, 17:00 by Vidyasagar (Sarath Chandra) Machupalli FBCS Building robust AI infrastructure requires understanding both the theoretical foundations and practical implementation details across multiple layers of technology. This comprehensive guide provides the definitive resource for architecting, deploying, and managing AI systems at any scale — from experimental prototypes to enterprise-grade production deployments serving millions of users. Modern AI applications demand sophisticated infrastructure that can handle the computational intensity of large language models, the complexity of multi-agent systems, and the real-time requirements of interactive applications. The challenge lies not just in selecting the right tools, but in understanding how they integrate across the entire technology stack to deliver reliable, scalable, and cost-effective solutions. Building ML Platforms for Real-Time Integrity Oct 2nd 2025, 16:00 by Ilia Volkov Large-scale social networks face a universal challenge: maintaining safe and reliable environments as user traffic grows exponentially. Manual processes often break under load, while ad-hoc machine learning models frequently fail to generalize. This article explores how a large-scale platform could address the challenge by developing a comprehensive machine learning infrastructure. Single filters or stand-alone models rarely survive long at scale.

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