Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

If you look at the cybersecurity setups of massive companies like Rockstar Games, Medtronic, or Amtrak, they look like digital fortresses. They spend millions on top-tier firewalls, hire elite security teams, and lock down their perimeters. Yet, all of them have made headlines for major data breaches. Recent Data Breaches How does this happen if their security is so good? The answer is simple: Attackers didn’t kick down the front door.

How AigentX found 24 real vulnerabilities, confirmed what wasn't exploitable, and discovered risks that code review alone could never catch.

When AI agent workloads start generating more alerts than your SOC can keep up with, the instinct most teams reach for is to deploy more triage on top of what they already have. If the SIEM is producing thousands of atomized alerts, plug in something downstream that can cluster, prioritize, and auto-resolve them faster than a human can. The market has consolidated around exactly this answer.

Across every industry, one thing has become abundantly clear: Cloud security has never been more critical, nor more complex. Organizations are scaling cloud environments faster than ever, but the explosion of identities, configurations, and services has created an attack surface that traditional approaches simply can’t keep up with. Teams are drowning in alerts, struggling to identify which issues matter, and facing increasing pressure to respond to threats with limited resources.

AI coding tools have changed who builds software. The barrier to entry has dropped to the point where a designer, an analyst, or a first-time founder can turn an idea into a working app in an afternoon. That shift is real, and it's accelerating.

Securonix Threat Research tracks TAX#TRIDENT, an active fake Indian Income Tax-themed campaign that uses three delivery paths to reach Windows endpoints. The campaign starts with fake tax assessment lures and then moves victims toward ZIP files, VBScript downloaders, or PHP-looking web endpoints that actually return script content.

Some of the security industry is still cautiously evaluating its relationship with AI. They are weighing questions, sitting with uncertainty, and waiting for something to ease their concerns about trusting AI in production. This post isn't for that group. This is for AI tool developers already in motion. The ones who vibe-coded a log parser over a weekend, spun up local inference on dedicated hardware, or ran cross-model research pipelines across multiple data sources.

This article was authored by Dave Burg Cybersecurity has long been framed as an arms race driven by increasingly sophisticated attacks. But that framing is increasingly outdated. The reality emerging from the front line is more uncomfortable: Today’s cyber risk is defined less by breakthrough innovation and more by the industrialization of existing weaknesses.

The moment teams move from one cloud to two, identity governance starts to fracture. Roles don’t translate cleanly, and access reviews lag behind deployment velocity. Multi-cloud identity management is the practice of controlling who can access what across AWS, Azure, GCP, Kubernetes, SaaS tools, databases, and other cloud-connected systems.

Financial services cybersecurity has evolved into a prerequisite for institutional solvency, moving far beyond traditional perimeter defense into the realm of total digital operational resilience. As the industry scales toward hyper-connected API ecosystems and decentralized service delivery, the sector’s risk profile has expanded significantly.