AI-driven development is outpacing security teams. This piece examines where AI-powered security actually help, where they fail, and how teams can use them responsibly.
This article examines how integrating AI into the software development lifecycle (SDLC) is enabling teams to move from MVPs to large, resilient systems.
AI Agents perceive, reason, plan, and act autonomously using LLMs. This article breaks down the core components that power every agent and shows you how to build one.
This article provides a practical guide to building a fault-tolerant Google Cloud data pipeline architecture with Firestore, Pub/Sub, Dataflow, and BigQuery.
ML systems introduce security risks most teams aren’t prepared for. The piece explores emerging ML-specific threats and what effective MLSecOps looks like in practice.
How cloud-native microservices transform insurance analytics by enabling scalability, real-time processing, and seamless modernization of legacy platforms.
AI enhances Workday integrations by improving mapping, testing, and monitoring, but it fails when used without human oversight, domain expertise, and strong governance.
Build long-running workflows by separating orchestration from execution, persisting state, and using events or callbacks to pause and resume without holding compute.