SECURE THE AI STACK THROUGH PLATFORM ENGINEERING
As data and AI-driven organizations push the boundaries of innovation, platform engineering has emerged as a key enabler of speed, scale, and reliability. Whether you’re deploying microservices, data products, or advanced AI agents, the promise of self-service developer platforms is to make innovation repeatable and secure (see the last post for reference). But speed without control is risky. The recent Docker security bulletin exposed a significant threat: thousands of unprotected MCP (Model Context Protocol) servers running in production across the internet. These insecure endpoints provide attackers with direct access to AI model internals,posing risks from model theft to poisoning attacks. But it’s not only the MCP servers that pose a threat. When OpenAI announced its ChatGPT Agents, Sam Altman said:
UNLOCKING INNOVATION WITH PLATFORM ENGINEERING
Data, AI, and innovation are essential to staying competitive. Organizations need to accelerate development while maintaining quality to remain ahead. Platform engineering, traditionally tied to cloud-native environments and microservices, is now crucial for enabling data mesh architectures, transforming how data products and AI applications are built. Data mesh allows teams to efficiently develop AI models, AI agents, and analytical tools, such as dashboards. Platform engineering provides the infrastructure and standardized processes – known as the Golden Path – that streamline development, reduce time to market, and improve product quality.
SHAKING THE KINDER EGG - OR: METADATA OF DATA PRODUCTS?
Shaking the Kinder egg - or: metadata of Data Products Who hasn’t done it? shaking the Kinder eggs to “guess” what’s inside and raise the chance of getting one of the figures. We even put them on the vegetable scale to increase the chance (many many years back the figures had higher weight than the assemble stuff). But in the end, it was all guessing. When we deal with data, we don’t want to guess. We want to get the clearest view on “what’s inside” as possible. And with the Data Mesh approach, in with we create the Data Products to reuse existing data, this is more important than ever. But how do we do that?
DATA MESH - NOT SUCH A NEW CONCEPT AFTER ALL?
Not such a new concept after all? I don’t think that I have to tell much about the recent developments on Data Mesh (see this and that), the world doesn’t need another “Data Mesh introduction” article just to tell these things again. But when we dig deeper into that topic and look on the Data Product there might be some similarities ringing a bell. We’ll come to this later. But how is a Data Product defined (by Zhamak)?
FIRST POST
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