Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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.

‍ 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. ‍

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.

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.

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.

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.

Ask anyone what they think when a website requests a driver's license, Social Security number, or email address, and you'll hear the same reaction: "Why do they need that?" It’s a fair question. Not a day goes by without news of another data breach or scam. Many people have either experienced fraud firsthand or know someone who has. While they're more aware of the need to protect their data, they don't feel equipped to actually do it.

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.

Sybil attacks are well documented in academic research. In practice, most organizations discover them too late, after the fake identities have already accumulated enough network influence to do real damage. The attack does not announce itself. It looks like growth. You see more nodes. More accounts. More participation. All of it is controlled by one attacker running a coordinated identity flood.

AI is changing how attacks happen, and how fast they happen. Seemplicity’s 2026 State of Exposure Management report shows why most security teams aren’t struggling to find risk, but to fix it quickly enough. Based on insights from 300 security leaders, it highlights where remediation breaks down, how AI is being used today, and why execution is becoming the real bottleneck.