Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

The security landscape has fundamentally changed, and many organizations haven’t caught up. If you’re still relying on quarterly scans, annual penetration tests, or spreadsheet-based vulnerability tracking to manage risks within your applications, you’re not managing risk. You’re documenting it after the fact.

Sophos X-Ops looks at the Atomic macOS Stealer and its capabilities Sophos Managed Detection and Response (MDR) teams recently responded to a customer incident involving an infostealer infection on a macOS host. When we investigated, we found that the infostealer appeared to be a variant of AMOS (Atomic macOS), a well-known malware family we’ve written about before. The attack began with a ClickFix-style ruse, where a user was tricked into running a terminal command.

The days when compliance was only a documentation exercise are long gone. Now, it’s a critical priority for a wide variety of organizations. But compliance is more of a result than a goal. The goal is achieving resilience. Cybersecurity and data protection regulations are rapidly evolving far beyond traditional compliance checklists. Global frameworks and regulations such as NIS 2, DORA, GDPR, HIPAA, SOX and NIST 2.0 are placing greater emphasis on operational resilience.

It's 9:47 AM on a Tuesday. A Slack message from legal lands in the security channel: "Did anyone approve the marketing team's new AI vendor? They're feeding customer data into it." Nobody approved it. The vendor's terms say they can use input data for model training, and the contract was signed three weeks ago. That moment, some version of which plays out at most organizations now, is what makes AI governance an operational priority rather than a compliance exercise.

On May 11, 2026, Mend Defender flagged more than 120 malicious packages newly published to RubyGems — the standard package manager for the Ruby ecosystem. Within 24 hours, that initial cluster expanded into something far larger: tens of thousands of packages pushed by thousands of attacker-controlled accounts, forcing RubyGems to suspend new account registration entirely while the cleanup got underway.

Sensitive data has become the target, the signal, and the source of risk in nearly every modern security program. Source code, customer records, intellectual property, credentials, and regulated data now move continuously across endpoints, cloud apps, SaaS platforms, browsers, collaboration tools, and GenAI applications. That movement is not inherently bad. It is how modern work gets done.

Modern identity‑based attacks often rely on shared infrastructure and reusable attack frameworks, rather than bespoke tooling built for a single target. Phishing kits and phishing‑as‑a‑service (PhaaS) platforms are the clearest example of this model — and today they are the most prevalent sources of account compromise across organizations of all sizes. Device code phishing illustrates how quickly this model evolves.

Cybersecurity is no longer something only large enterprises need to worry about. In 2026, it affects every organization, from small businesses and managed service providers to global companies and individual users. What has changed is not just the number of cyberattacks. It is how quickly they happen, how far they can spread, and how much damage they can cause.

Cyberattacks still break trust. That hasn’t changed. What has changed is how quickly organizations are expected to understand what’s happening and act on it. In today’s environments, answers are demanded in minutes, not days. Leadership needs clarity while systems are still running, customers are still online, and the situation is still unfolding. This is where digital forensics is entering its next chapter.

Networking teams have invested heavily in automation to help them manage increasing workloads and reduce manual tasks. Yet many still face the same issues, like outages, stalled operations, and managing growing incident volume. This problem isn’t a lack of automation: it’s what happens after automation runs. Automation is useful for individual tasks, but it can’t handle the complexity of real-world networking processes, which demand coordination across teams, environments, and tools.