Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Insurance has always been a data-rich industry. But recent threat intelligence makes it clear that attackers are not only going after insurers because they are large organizations. They’re going after them because insurance touches some of a threat actor’s favorite things: money, identity, healthcare, legal claims, third-party relationships, and highly sensitive customer records.

AI adoption is moving faster than most of us anticipated and, more importantly, faster than most organizations can govern it. Organizations are implementing the use of AI-enabled applications, browser extensions, coding assistants, and automated agents to enable employees to work faster. In many cases, these tools are adopted without security review, procurement approval, or a clear understanding of where organizational data is being sent.

Most of the exposure research we publish goes one way. We count some category of internet-exposed devices, the number is bigger than you hoped, and it is climbing. Webcams. Industrial control systems. NTP servers old enough to vote. The story is almost always "there is more of this exposed than there should be, and it is getting worse.".

Financial institutions are not short on cybersecurity policies, frameworks, or regulatory requirements. Turning those requirements into a living risk management program can be challenging. Organizations need a program that can keep pace as technology environments expand, cloud adoption grows, third parties are added, and external exposures change. Especially as the threat landscape continues to evolve.

AI is quickly becoming one of the most useful tools available to security researchers. Its ability to analyze enormous volumes of data, identify vulnerabilities, reconstruct attacks, connect seemingly unrelated signals, and help defenders respond faster than humans could alone is incredibly beneficial.

In this post, we will uncover the “Fuyao Enterprise,” a previously unknown, sophisticated and highly modular botnet operating within Android TV boxes. This operation marks a shift in modern ad-fraud, where automated bots fake both clicks and views to defraud advertisers and ad-networks. While deploying novel tactics and techniques, Fuyao managed to escape public research for several years. Now, its operators openly advertise their network of over 120,000 “AI digital humans.".

AI is compressing the cybersecurity timeline faster than most institutions can adapt to it. Not only do security leaders have to deal with this new reality, they have to answer key questions to internal and external audiences about their efforts — including regulators.

Bitsight's annual Ratings Algorithm Update (RAU) has been in effect as of July 16, 2026. In preparation, RAU 2026 Preview was made available in April 2026. As in the past, RAU 2026 is an effort to account for the continuous evolution of the threat landscape the Bitsight security ratings seek to quantify. This year's update is focused on modernizing the rating by improving how it is composed from various risk vectors (RVs). In particular, this entails the following.