Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Protestware by open source maintainer to hinder agentic coding: The jqwik 1.10.0 Prompt Injection

On May 25, 2026, the maintainer of jqwik, a Java property-based testing library, released version 1.10.0 to Maven Central with a hidden instruction intended for AI coding agents. The payload told agents to disregard previous instructions and delete all jqwik tests and code. It was hidden from humans with ANSI terminal codes but left fully readable to any tool that captures raw output.

SSO for AI Agents: The Identity Gap No One is Talking About

Single Sign-On (SSO) means fewer password headaches, faster access, and better security for human users. But the same cannot be said for AI agents. SSO, a core part of Identity and Access Management (IAM), which was initially built for humans, can no longer be used for AI agents. For humans, it was quite simple - just log in once, and authenticate across connected apps. However, when an AI agent tries to authenticate the same way, the traditional access model breaks fast.

Vercel's Tom Occhino on why access control is product architecture

Zero-Shot Learning is a podcast about how AI gets built, secured, and deployed. Hosted by Nancy Wang, 1Password CTO, and Dev Tagare, Senior Director of Engineering at Google, it's a builder's view of the architecture and the complex choices it takes to ship with AI.

Allowed Is Not Aligned: Why Retrofitted Tools Can't Secure AI Agents

Gartner named Zenity the Company to Beat in AI Agent Governance on April 17, 2026. That recognition, grounded in technical capabilities, customer implementations, ecosystem breadth, and business model, isn't a marketing award. To us, it's the analyst community confirming that purpose-built architecture for agentic AI is winning. The recognition didn't come in isolation. Gartner's own language captures the stakes.

How Weak AI Governance Increases Organizational Exposure to Risks

‍ Artificial intelligence (AI) is transforming businesses rapidly, but weak AI governance creates significant risks. Without proper oversight, organizations face costly data breaches, operational failures, and damage to their reputation. This article explains why strong AI governance is essential to managing these risks.

How technology and new laws are merging AI and data protection

The rapid development of artificial intelligence poses a complex dilemma for businesses: how to harness the enormous potential of neural networks without compromising user privacy? To successfully navigate this technological landscape, companies require strong technical expertise. Experts from the AI service company Data Science UA help businesses intelligently integrate machine learning algorithms and AI agents, balancing innovation with strict information security requirements.

Top 6 Custom Software and AI Development Companies in 2026

Custom software in 2026 is no longer separate from AI. Companies now need products that combine strong engineering with practical AI features, from LLM-powered workflows and automation to machine learning, AI agents, and data-driven decision systems. This guide reviews the top custom software and AI development companies in 2026, focusing on firms with real case studies, proven delivery, and the ability to build production-ready solutions instead of surface-level AI demos.

Smarter Stock, Stronger Operations: The New Standard for Inventory Intelligence

Running a business without proper inventory control is like driving with your eyes closed. You might fool yourself into thinking you're ok... but at some point everything is going to implode. Stock management processes within businesses have evolved hugely in the past ten years. Spreadsheets and manual educated guesses aren't going to get you far. Business intelligence is now for inventory. Those who leverage it are gaining a competitive advantage.

MCP vs. Traditional API Security: Why Your Existing Controls Don't Protect MCP-Powered AI Agents

Traditional API security protects deterministic systems with known endpoints and explicit actions, while MCP-powered AI agents operate through inferred intent, dynamic tool chaining, and natural language interactions. This requires MCP-specific security controls such as tool governance, behavioral monitoring, and semantic anomaly detection.