Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

You ship to production every day while your last pentest happened 11 months ago. Just say that sentence out loud, and we ought to rest our entire case of autonomous pentesting for SaaS companies right there. Everything below is just the supporting evidence. The mismatch isn’t subtle. Your engineers deploy continuously, your infrastructure reshapes itself weekly, and your security validation still runs on a calendar designed for software that shipped twice a year.

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.

Arctic Wolf Labs exposes Dark Caracal’s evolving tradecraft, linking 249 samples to two operational build profiles and a resilient Ethereum-based C2 architecture targeting Latin America. In June 2026, Arctic Wolf Labs investigated a targeted intrusion affecting a communications organization in Venezuela.

Scenario libraries grow. A program starts with ransomware and a data breach, adds a third-party failure after a supplier incident, splits ransomware into encryption and extortion variants, adds a cloud outage, and two years later holds forty entries nobody has revisited. ‍ The usual guidance suggests a range, somewhere between five and fifteen, which is a reasonable starting point and answers the wrong question.

Containment guidance for agent incidents already exists and it is largely correct. Revoke the tokens, freeze the orchestration tier, cut egress, set the vector store to read-only. Those steps take minutes and any competent team will find them. ‍ The difficulty sits either side of containment. Deciding what kind of incident this is takes longer than stopping it, establishing what the agent did before you stopped it takes longer still, and both depend on preparation that has to exist beforehand.

WatchGuard Technologies, a global leader in unified cybersecurity, has been recognized as a 2026 CRN Annual Report Card (ARC) Award winner in the Security: Network Security—SMB category by CRN, a brand of The Channel Company. The recognition is based on direct feedback from solution providers across North America, who evaluated technology vendors on product innovation, support, partnership, and managed and cloud services. WatchGuard achieved an overall score of 92.8, ranking first in the category.

As a company grows, more employees join, but the size of the IT team overseeing critical systems often doesn’t grow at the same pace. Admins have to be intentional about prioritizing their efforts to meet the needs of a growing organization. That’s why we’re excited to announce several releases aimed at helping admins optimize their organization’s use of 1Password in two important areas: reducing lockouts and automating provisioning at scale.

In July 2026, two AI stories broke that appeared unrelated on the surface. But were they really? The first was an AI product's shared conversation links, meant for specific people, turning up in Google searches, some holding sensitive personal and company data. The second was a frontier AI lab's own model escaping a security sandbox during an internal evaluation and spending four and a half days inside three companies' systems. One involved ordinary users making a common mistake.