Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

A credential access event fired. An AI agent investigated it, correlated it against running processes, assessed the risk, and closed the ticket. No analyst touched it. The entire loop ran in minutes. This is what security operations look like when AI can actually operate in the environment rather than advise from outside it. Security operations have always required a special kind of person.

Over 300 age-related bills were introduced across several US states in 2025 alone. We’ve heard firsthand from numerous legal and compliance teams that keeping up with these regulations is incredibly overwhelming. That’s why we developed Atlas, a global database tracking evolving age assurance regulations. Atlas tracks recent legislation impacting social media platforms, adult content, age-restricted services, and other related legislation.

On April 22, 2026, Google's Threat Intelligence Group and Mandiant disclosed a campaign by a threat actor they're tracking as UNC6692. The group breached enterprise networks by impersonating IT helpdesk staff over Microsoft Teams, ultimately exfiltrating Active Directory databases and achieving full domain compromise. What's notable about UNC6692 is what they didn't do. They didn't use a zero-day. They didn't exploit a software vulnerability.

Understand where short-lived credentials reduce risk in agentic systems and where operational complexity requires stronger monitoring and governance controls.

In early February 2026, users of Qinglong (青龙), a popular open source timed task management platform with over 19,000 GitHub stars, began reporting that their servers were maxing out CPU usage. The cause was a cryptominer binary called.fullgc, deployed through two authentication bypass vulnerabilities that allowed unauthenticated remote code execution. The attacks went largely unnoticed in the English-speaking security community.

On April 28, 2025, a massive power outage affected large areas of the Iberian Peninsula and parts of southern France. Traffic lights, elevators, point-of-sale systems, and many mobile phone and internet networks suddenly stopped functioning. Subways and parts of the rail network ground to a halt. Industrial production and numerous service businesses were interrupted for several hours to a full day.

Most organizations treating AI security as a model problem are defending the wrong layer. Security teams filter prompts, patch jailbreaks, and tune model behavior, which is all necessary work, while the actual attack surface sits largely unexamined underneath. That surface is the API layer: the endpoints AI systems use to retrieve data, call tools, and take action on behalf of users. This isn't a theoretical gap.

You can easily split the room in half if you mention autonomous pentesting in a room full of security professionals. One-half will argue it’s the most important shift in offensive security to date, capable of solving the challenge of monitoring attack surface expansion faster than any manual pentester can prove it secure. The other half will push back hard.

When exploring the regulatory environment, data privacy continues to be a critical area of focus for organizations worldwide. With rapid advancements in artificial intelligence, the proliferation of connected devices, and the increasing sophistication of cyber threats, safeguarding personal information has never been more critical. Governments worldwide are responding with stringent regulations, while consumers are becoming more discerning about how their data is collected and used.

For years, the defensive side held the asymmetric advantage over threat actors. Writing exploits requires a deep understanding of how memory corruption works, how authentication tokens can be forged, etc. That knowledge gap is what made it hard to exploit a vulnerability. LLM proliferation lowered that floor and quickly removed that advantage. Even script kiddies can now carry out cyberattacks like APTs without understanding POC.