Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

My first introduction to UNIX remote access was via telnet and rsh protocols in college, which was the standard method at the time. But I soon started reading articles about how easy it was for someone to sniff the network and capture passwords since they were being transmitted in plaintext. On the shared network segments common to university campuses and early enterprise environments, the tools to intercept traffic were freely available, well-documented, and required very little skill to use.

On April 6, 2026, Microsoft Defender Security Research published an advisory detailing a large-scale phishing campaign that leverages the OAuth Device Code Authentication flow to compromise Microsoft 365 accounts across organizations globally. This campaign represents a significant evolution from manual social engineering to fully automated, AI-driven attack infrastructure.

A definitive guide to how automated and human-reviewed patch-in-place remediation solves both direct and transitive open source vulnerabilities - without forcing risky upgrades. Learn why traditional tools miss transitive risk, and how to evaluate modern platforms based on SLA, provenance, and CI/CD fit.

An AI just found critical vulnerabilities that survived decades of human review. If your security program isn’t built for this moment, it’s already behind. Surprise! An AI just did what your security team couldn’t.

SAP environments, especially in the age of cloud work and hybrid infrastructures, are ripe with security complications. But SAP support and security is nothing to scoff at. Access controls alone in SAP environments require compliance capabilities for ultimate security, regardless of the security solution or deployment scenario.

‍ As GenAI tools become embedded in core business operations, the governance programs meant to oversee them are still catching up. Closing that gap requires visibility into where AI operates and the ability to express exposure in financial terms that leadership can act on. The organizations best positioned to manage AI risk are those that have already started treating it as a measurable business variable rather than an abstract operational concern. ‍

Global enterprises, private equity firms, conglomerates, and other large-scale organizations may share a corporate umbrella, but the entities operating beneath it are far from uniform. Each functions with a distinct technology stack, industry context, and regulatory environment, which inherently means each carries a distinct cyber exposure. Understanding cyber risk at that higher organizational level, therefore, requires more than individual entity modeling.

Most major breaches do not spiral out of control because attackers get in. They spiral because attackers are free to move once they are inside. After gaining an initial foothold through compromised credentials, a misconfigured cloud workload, a remote device, or a third-party connection, sophisticated attackers pivot. They scan the network, escalate privileges, and move laterally across the LAN and datacenter until they reach critical systems.

What can bring together development and operation teams better than DevOps, a prevalent agile methodology? It involves new management principles, cultural change, and technology tools that boost the team’s development, collaboration, and productivity while they cooperate on software development.

Your CNAPP shows green across every posture check—hardened clusters, compliant configurations, no critical CVEs—but when your board asks "Are our AI agents safe in production?", you cannot answer with confidence because your tools see the infrastructure, not what the agents actually do at runtime.