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AI agents have become incredibly useful across many industries by their ability to reach inboxes, documents, and financial information, then search, summarize, or act on what they find. But that access creates a data-in-use problem: conventional encryption protects information in storage and transit, but an agent generally needs it decrypted in memory while processing it, where sensitive material can be exposed to application code, logs and debugging systems, infrastructure operators, or a compromised host. Conan Yu's work offers one practical response to that problem. He is co-founder of Rena Labs , which develops infrastructure for confidential AI training and inference using trusted execution environments (TEEs), hardware-isolated environments designed to limit the surrounding host's access while code and data are processed. Over roughly two years, Yu has worked across hardware and cloud TEE deployments, privacy-preserving financial-data analysis, and private AI inference. He a...

As organizations deploy AI coding agents across large monorepos and microservices environments, a fundamental problem emerges: the model may be capable of making the change, yet still lack the organizational context required to make the right change safely. A developer can ask an AI coding agent to deprecate an API field, update an authentication flow, or modify a service interface. The agent can inspect the code available on the developer's machine and search for references. What it may not know is that the field is consumed by four other services across separate repositories, that one of those services belongs to another team, or that the same field eventually carries sensitive data into a third party integration. This is not simply a context window problem. It is a code context problem: providing AI agents with accurate, current, organization wide evidence about how software actually behaves. One emerging approach is to generate that evidence directly from source code us...

In today's dynamic web threat landscape, staying a step ahead of risk is crucial. Businesses want to keep improving their websites with the latest customer service experience while maintaining a strong security posture and complying with strict privacy rules. With the help of a new risk assessment tool - Exposure Rating - we have calculated the risk exposure for nine leading customer service chatbots compared against each other. For the full chatbot ratings report, click here . But first, what is an Exposure Rating risk assessment tool? Contextual Risk Assessment for the Web Exposure Rating goes beyond traditional website security solutions. It delves deeper, providing a comprehensive assessment of your web risk exposure, benchmarked against industry leaders. The rating system analyzes every website, application, and domain within your environment, giving you a clear picture of your threat landscape. But Exposure Rating is more than just a report card. It's a powerful to...

Security has always been a game of risk management, not risk elimination. Every decision to address one threat means potentially leaving another unattended. That deciding of which threat to address – and in what order – is the name of the game. In this triage process, abuse vulnerabilities,  i.e. , exploiting legitimate features of a platform in unintended ways to conduct digital misdeeds such as phishing campaigns, can get pushed down the priority list of security issues. I would like to argue that it's time we stop separating the concept of abuse vulnerabilities and security vulnerabilities.  Unlike security vulnerabilities that are, in essence, exploited loopholes or bugs in the code, fixes for abuse vulnerabilities can be slow to come. Yet these openings for abuse can easily lead to disaster if left unattended. Recent figures show that  68% of breaches  originate from these exact types of exploitations involving the human element making a mistake such as phish...