Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Sophos named a 2026 Gartner Peer Insights Customers' Choice for Managed Detection and Response

Sophos named a 2026 Gartner Peer Insights Customers' Choice for Managed Detection and Response Third consecutive time being named a Customers’ Choice for MDR Sophos has been named a 2026 Gartner Peer Insights Customers' Choice in the 2026 Gartner Peer Insights Voice of the Customer for Managed Detection and Response (MDR).

Stop Measuring Effort. Start Measuring Outcomes in the SOC

By: Beth Dannemilller, Senior Director, Product Marketing For years, security operations have been measured by effort. More alerts processed. More data ingested. More tools deployed. It looks like progress. It isn’t. CIOs know the reality. Teams are overwhelmed. Costs keep rising. And when the board asks a simple question, “Are we reducing risk?”, the answer is often unclear. This is the breaking point for the SOC.

The Floor Was Selling AI. The Hallways Were Asking for Help.

One man’s perspective on RSA 2026 and what the AI agent security market actually looks like up close. Every year at RSA, there's a theme, not the official one printed on the lanyards, but the real one. The one that shows up in every booth conversation, every hallway argument, every dinner where people finally say what they wouldn't say on a panel. A few years back, it was cloud. Then zero trust took over and held the room for a while. XDR came through and confused everyone. Identity had its moment.

Codex API In DevSecOps: Balancing Developer Speed With Secure Code Review

AI-assisted coding is no longer a side experiment. It is becoming part of daily engineering workflows, from drafting functions and refactoring legacy code to generating tests and accelerating routine implementation work. That shift is why the Codex API now belongs in a broader DevSecOps conversation, not just a developer productivity discussion.

Digital Transformation in Manufacturing: Complete Implementation Guide

The integration of software, data, and machines is the key driving factor of digital transformation in the manufacturing industry. By leveraging technology such as: Manufacturers can improve productivity, reduce costs, and enhance decision-making. Scalable by design, these systems also support long-term growth and innovation.

The Art of Lightning-Fast Token Sniping on Solana: Why Speed Is Everything

You know that feeling when you spot a new token launch and think "I should probably buy some of this" - then by the time you actually get around to it, the price has already done a 10x? Yeah, we've all been there. I've missed more opportunities than I care to count just because I was too slow on the draw. That's exactly why the world of automated token sniping has become such a fascinating corner of the Solana ecosystem.

Flutter App Security Testing: Why most tools fail and what actually works

Most mobile security workflows end in a familiar way. A scan runs, a report is generated, and the output looks reassuring. There are no critical issues, maybe a few medium findings, nothing that blocks a release. The process completes, the team moves forward, and the app ships. At that moment, the assumption is clear. The app has been tested. The risk is understood. But there is a question that rarely gets asked, and it changes the entire conversation.

4 steps teams can take to mitigate Iranian cyberattacks on critical infrastructure

COMMENTARY: When the United States and Israel launched coordinated strikes against Iran on February 28, the security community mobilized around the visible response. I’ve watched that response for two weeks: teams tracking hacktivist DDoS campaigns, incident counts climbing, news coverage following close behind.

AI Application Security: 6 Focus Areas and Critical Best Practices

AI application security protects AI-powered apps, including those powered by large language models ( LLMs), from unique threats like prompt injection, data poisoning, and model theft. It achieves this by securing the entire lifecycle, including code, data, algorithms, and APIs, using specialized tools and processes that go beyond traditional security measures. It involves securing the AI model’s behavior, training data, and outputs.