Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As AI becomes embedded across the enterprise — from customer-facing tools to backend automation — it dramatically expands the enterprise attack surface. Models, agents, apps, and data pipelines now span public and private clouds, SaaS, and edge environments, creating a sprawling, opaque risk landscape.

For years, cybersecurity meant defending a clear, defined perimeter. That era is over. Modern organizations are sprawling, borderless, and interconnected in ways security teams were never designed to protect. Employees log in from around the globe, business units spin up SaaS without approval, and third-party vendors extend risk far beyond your control. Attackers no longer need to hammer at a single gateway.

Network teams often risk costly disruptions when aging or unsupported hardware slips under the radar. With Forward Networks’ Network Query Engine (NQE), you can proactively identify devices approaching End‑of‑Sale (EOS) or End‑of‑Life (EOL), plus enforce hardware/software compliance at scale. Get ahead of risks, reduce technical debt, and align your infrastructure with business goals—automatically.

What happens when the AI feature you shipped last quarter is compliant in one region—but illegal today in another? That’s the new normal. In 2025, the EU AI Act, new U.S. state privacy laws, China’s PIPL, and APAC rules are reshaping how organizations collect, process, store, and share data for AI. Privacy isn’t a back-office task anymore; it’s a front-line guardrail for product, security, and data teams.

For government contractors, budgeting isn’t merely a bookkeeping exercise—it’s the pivot point on which projects, compliance, and profitability hinge. Yet, many 8(a) organizations and government contractors still struggle with outdated systems, siloed processes, and compliance complexities that create unnecessary hurdles.

Application environments are more complex than ever, with APIs forming the critical connective tissue. But this proliferation has created a vast, often invisible, attack surface. Security teams are caught in a difficult position: compliance frameworks like PCI and SOC 2 demand API scanning, but offer little guidance. Meanwhile, you’re grappling with incomplete API inventories, and the market is a confusing mix of expensive, hard-to-instrument niche tools.

Imagine a retail chain, CaaT Networkstore, that wants to run a marketing campaign targeting its in-store customers. To do that, they need to know what types of devices their customers are using. They could survey the users, but a better, more accurate approach is to look at their free Wi-Fi logs and count the types of devices customers are using to connect to the network. If the store is small, the solution is fairly trivial.

The recent Zscaler breach has sparked significant attention in the cybersecurity community not just because of its impact, but also because of the complexity of the attack and the multiple claims of responsibility surrounding it. Here’s a breakdown of what happened, who’s claiming involvement, and what we can learn from the incident. This was not a direct hack of Zscaler’s core systems. Instead, it was a supply chain attack that exploited a third-party integration.

Fast-moving cloud environments demand speed, but without the right access controls they invite risk. Resources such as virtual machines, containers, and services are created, modified, and terminated at a rapid pace. At the same time, workloads are becoming increasingly distributed, with data and applications spanning multiple regions, accounts, and even across different cloud service providers (CSPs).

New Linux malware evades antivirus detection, UNC5518 deploys CORNFLAKE.V3 using ClickFix and fake CAPTCHA pages, and a PRC-Nexus campaign hijacks web traffic.