AI systems introduce a new kind of security problem. For years, enterprise security has been built around familiar boundaries. Users authenticate. Services authorize. APIs validate requests. Databases enforce access. Network boundaries restrict communication. Sensitive operations require explicit permissions. Then we…
For years, enterprise systems have been modernized in familiar ways. A monolith becomes services. A database becomes several databases. Synchronous calls become events. Batch processing becomes near-real-time processing. Older interfaces are replaced with APIs. Infrastructure moves to newer platforms. The…
For years, we knew how to test software. A method receives an input. It produces an output. We write a test. For a traditional service, that model works remarkably well. We test business rules. We test APIs. We test database…
A user asks: “Review this supplier invoice, verify that everything looks correct, and prepare it for payment.” From the user’s perspective, this is one request. But imagine the system decides to use several AI agents: At first glance, this looks…
A user asks an AI agent: “Pay this invoice, update the records, and let the supplier know when it’s done.” It sounds like one business operation. From the user’s perspective, it is one request. From the system’s perspective, it may…
A customer asks: “How much can I safely spend today?” An LLM can understand the question. It can explain the customer’s account. It can summarize recent transactions. It can even reason about what information might be relevant. But there is…
