Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

On Monday, August 17, 2026, Cato recorded record-breaking post-Obon traffic across its Japan Points of Presence (PoPs) as employees returned to work together. At one of Cato’s Tokyo PoPs, traffic rose to more than 15 times its typical level for the same morning period on normal business days.

Odysseus left Troy with a fleet and reached Ithaca with nothing, having lost every ship to the sea. While the war was won by force, the journey home was won by wits, and ultimately the fleet barely mattered. Most security operations centers are still running their SecOps voyage the way Odysseus ran the war: more tools, more force, more signal. It is the wrong instinct, because the SOC bottleneck was never how much you can see. It is how fast you can decide.

A mid-sized SOC can have endpoint telemetry in one platform, cloud logs in another, vulnerability findings in a third, and identity alerts somewhere else entirely. Analysts switch consoles, normalize the same event repeatedly, and still miss the incident that required context from several systems. The problem usually isn't a lack of detection technology. It's the absence of a shared data model, disciplined correlation, and evidence that security and compliance teams can use together.

Imagine getting three separate security reports back for your new enterprise AI assistant: On paper, the application looks ready for production, but in reality, a threat actor bypasses your guardrails in minutes. How? By using the AI model as an intermediary. The attacker steers the LLM to invoke the internal utility tool, thereby bridging an untrusted prompt directly to the backend execution sink.

The Gentlemen is a financially motivated ransomware group that combines data theft with encryption to increase pressure on victims. They first came onto the threat scene in July 2025. Rather than relying on encryption alone, the group exfiltrates sensitive business data before locking files, leaving organizations to deal with both operational disruption and the risk of stolen information being exposed.

Attackers are distributing a new Android malware called “WindRelay” via phone-based social engineering attacks, according to researchers at Group-IB. The attackers call the victims, impersonating bank employees and instruct them to install a malicious app. In one instance observed by Group-IB, the scammers carried out the entire attack in just thirteen minutes.

Sending an email is easy, but sending an email securely with the right settings can be time-consuming or difficult if you’re unsure how to configure the right settings. Even so, securing your emails protects your business from phishing attempts and ransomware. If you use an email for personal use, the same risks of email scams apply, so the techniques you learn in this article will keep your personal and business information secure from hackers.

Let me be direct about something the industry keeps dancing around: the vulnerability problem isn’t getting better. It’s getting structurally worse. The wave of AI-generated code and compression of time to exploit thanks to frontier AI models like Mythos is about to make “worse” look quaint. If your vulnerability and exposure management program is still built around scanner cycles, ticket queues, and CVSS scores, you’re not running a security program.

Most organisations now run at least one LLM in production, and a growing number run agents that call tools and act without a human in the loop. The security testing those systems receive was designed for deterministic software. AI applications fail differently. The payload is natural language, the same input can be safe nine times and unsafe on the tenth, and the malicious instruction often arrives inside a document or tool description rather than from the user.