Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

After a laptop compromise, the hard question is which credentials were on it. See why blast radius scoping is hard, and how to turn it into a revocable list.

MSPs are operating in an increasingly demanding environment. Every client they onboard adds more applications, identities, endpoints, cloud services, data, and third-party dependencies. All of this drives up the daily operational workload that teams have to absorb.

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.".

The July 2026 Apache Traffic Server security release addresses a large batch of vulnerabilities across the proxy core and its plugin ecosystem, disclosed on July 29, 2026 and fixed in versions 9.2.15 and 10.1.4. Published counts of the batch differ. The Apache Traffic Server project describes the release as addressing 34 CVEs, while Belgium’s Centre for Cybersecurity puts the figure at 38 vulnerabilities.

Employees are already using AI at work. They build agents in Microsoft Copilot Studio, write code with Claude Code, and paste sensitive data into chatbots in the browser. Each of these actions can expose sensitive information outside of approved workflows, and most of it happens where traditional endpoint, network, and data loss prevention (DLP) security controls can't see the prompt or the tool call.

AI agents are rapidly moving from experimental projects into everyday business operations. Unlike traditional AI systems that generate content or answer questions, AI agents can take action. They can call APIs, access applications, retrieve data, execute workflows, and make decisions with limited human intervention. That shift is creating a new security challenge for enterprises.

In our last post, we shared how we began to scale our security code review process with SAGE. We discussed how we gathered historical Product Security (ProdSec) review records to create a 1Password-specific ruleset, the three-stage Finder/Critic/Judge pipeline, and the limitations of our v1 implementation.

AI agents are doing more than just generating code. Increasingly, they are working autonomously on complex coding challenges, touching production APIs, databases, and infrastructure across development environments, often without thorough human review. To perform these operations and access multiple systems, agents rely on developer secrets and non-human identities (NHI). But often, developers lack a secure way to share these secrets, leading to overprivileged, invisible access.

Every mature cyber program eventually hits the same wall. Security teams collect enormous amounts of telemetry, threat intelligence, and control data, and yet the conversation with the CFO about what to fund next still feels like an argument about opinions rather than evidence. The reason is not that the data is missing.

AI agent identity is a unique, digitally verifiable credential assigned to an autonomous AI system that defines who the agent is, what resources it can access, and on whose behalf it is acting. As AI systems move from answering questions to executing real-world actions independently, agent identity has become the new control plane for enterprise cybersecurity. Legacy identity and access management tools were built for humans behind a login screen and static service accounts running deterministic code.