The perimeter-based defense model — a centralized, physical firewall guarding a well-defined network edge — is effectively obsolete. As business operations move to hybrid, multi-cloud environments, the “network boundary” now stretches to wherever an authorized user or workload happens to be running. ...
Part 1 traced the firewall from stateless packet filters through NGFWs — four generations, each one built to close the previous generation’s blind spot. This part covers what happened once there was no longer a single perimeter to defend at all. ...
Part 1 covered the firewall’s evolution through NGFWs, and Part 2 covered the shift to SASE and Zero Trust. This part is where AI enters the picture — and it enters from two directions at once. ...
Part 3 covered the AI Security Gateway conceptually — inbound, RAG-context, and outbound protection for GenAI traffic. This part is the actual build: a working deployment using LiteLLM, an Apache 2.0 open-source AI gateway, with Redis for state tracking and rate limiting. ...
This series has traced the firewall from stateless packet filters (Part 1) through SASE and Zero Trust (Part 2), the two-front AI security problem (Part 3), and a working AI Gateway deployment (Part 4). This last part looks at where the architecture goes from here — two directions, moving on very different timelines. ...
Every AI model is, at its core, a direct product of its training data. Long before a system answers its first prompt or makes its first real-world prediction, decisions about data collection, sourcing, and processing have already permanently shaped its behavior. ...
In Part 1 we looked at how little organizations actually know about where their AI training data comes from. This time, we look at what happens once that unverified data gets baked directly into a model. ...
Parts 1 and 2 of this series covered the data going into a model. This time we look at the technical choices made while training and optimizing it — decisions that introduce their own, separate set of security trade-offs. ...
So far this series has covered the data going into a model and the technical choices made while training it. Now: what happens when your organization doesn’t build a model at all, but fine-tunes someone else’s. ...
The first four parts of this series covered where AI training data comes from, what gets permanently baked into a model, how training and optimization choices introduce risk, and how fine-tuning inherits all of it. This part covers a harder problem: you often can’t check any of it yourself. ...