Most teams today don’t struggle to find vulnerabilities; they struggle to decide what to fix first. With SAST, SCA, secrets, and CI/CD checks all generating signals, the real challenge is prioritization: what’s actually exploitable, what’s reachable, and what can be fixed without breaking things. Instead of relying only on severity, modern teams are shifting toward risk-based remediation, combining exploitability, context, and stability, while reducing noise across tools and automating safe fixes through PRs. If you’re dealing with alert fatigue or slow remediation cycles, this checklist is a practical starting point → https://go.xygeni.io/ai-driven-remediation-risk-prioritization-checklist

Most Spring Boot applications run perfectly in development.

The APIs respond quickly, tests pass, and everything seems stable.

But once the application reaches production, things can change dramatically — slow responses, memory issues, and unexpected failures start appearing.

In many cases, the problem isn't Spring Boot itself.
It's a set of common mistakes developers unknowingly introduce into their applications.

In this article, we'll explore 7 critical mistakes that cause many Spring Boot apps to fail in production — and how to avoid them.

Software Regression Testing Services: Ensuring Stability After Every Change

Incident dashboards measure system health, but rarely show the workload and strain engineers face when responding to alerts. Incident load isn’t only about the number of incidents, but the patterns surrounding them. In this article we explore these patterns and introduce On-Call Health, an open-source tool that analyzes engineering signals to surface early burnout trends, highlighting why incident volume alone isn’t enough and why after-hours interruptions, workload stacking, and long-term trends matter.

A practical breakdown of the most common Spring Boot performance bottlenecks — and how we optimized our API from 3 seconds to 200 ms.

AI copilots like GitHub Copilot and Cursor can push production-ready code instantly—but CI-only security scans catch issues after merge, when it’s too late. In this live session, see how to detect vulnerabilities in real time inside the IDE, validate reachability before merge, apply guardrails to AI-generated code, and auto-fix safely. Includes live demo securing Copilot and AI agent workflows. 📅 March 11, 2026 👉 Register https://www.linkedin.com/events/7432784645383110656/

AI is now a core execution layer in software delivery. In 2025, attackers exploited automation, trusted pipelines, and AI-generated code instead of vulnerabilities. This report explains why traditional AppSec signals failed and what must change in 2026.

2025 exposed a shift in software supply chain attacks. AI-assisted malware, self-propagating techniques, and widespread trust abuse altered how compromises spread across dependencies, registries, and CI/CD pipelines.

This upcoming LinkedIn Live SafeDev Talk examines what truly changed, why long-held security assumptions are breaking down, and what development teams need to rethink as they head into 2026.

📅 January 20th | ⏰ Time: 𝟏𝟔:𝟑𝟎 (𝐂𝐄𝐒𝐓)/𝟏𝟎:𝟑𝟎 (𝐄𝐃𝐓)

Join Us!

In Securing the Museum of Software in an AI Coding Tsunami, Eran Kinsbruner argues that software now consists of legacy, modern, and rapidly AI-generated code, creating unprecedented complexity and risk. Traditional AppSec can’t keep up with machine-speed development. He calls for a unified, developer-first, agentic AppSec platform that embeds security into coding workflows to prevent, fix, and secure all code eras before vulnerabilities reach repositories.