Last June I had the chance to spend two intense, inspiring days at Nexus Luxembourg 2025 โ and this time not just as an attendee, but on stage, sharing something Iโm deeply passionate about: secure, AI-powered applications built fully inside Oracle Database.
Together with my colleague Thomas Minne, we showed how AI, data security, and low-code development come together in a real healthcare use case โ no slides-only theory, but a live demo running entirely on Oracle Database 23ai.
One database. One secure AI-ready foundation. One future.
That was the core message of our sessionโฆ and it really resonated.
Why Secure AI Pipelines Matter (Especially in Healthcare)
Our story started from a simple but uncomfortable truth:
You cannot treat AI pipelines as side projects when the data involved is sensitive.
In healthcare, weโre dealing with:
Patient records
Clinical notes
Medication history
Internal guidelines and protocols
Now mix that with vectorization (embeddings) and RAG (Retrieval-Augmented Generation) and you quickly get into risky territory if data leaves its secure home.
The traditional approach often looks like this:
Export data from the database
Push it into an external vector store or AI service
Try to re-create security rules somewhere else
This introduces more systems, more attack surface, and more inconsistent security models. At Nexus, many people told us the same story: the AI stack is fragmented, complex, and expensive to secure.
Our answer was different:
What if the database could be your vector store, your AI orchestration engine, and your security boundary โ all at once?
Our Session: Secure AI Workflows Inside Oracle Database 23ai
On June 17 at 16:00, we walked through a complete end-to-end scenario: a healthcare application that uses GenAI while respecting strict privacy requirements.
We focused on four core capabilities inside Oracle Database 23ai:
AI Vector Search โ to store and query embeddings directly in the database
DBMS_VECTOR_CHAIN โ to orchestrate RAG-style workflows natively in SQL/PL/SQL
Oracle Machine Learning (OML) โ to build and operationalize models close to the data
Real Application Security (RAS) โ to ensure every user only sees what theyโre allowed to see, even in AI flows
And on top of all that, we used:
Select AI โ to speed up development and make it easier to build smart behaviors
Oracle APEX โ as the low-code front end where doctors, pharmacists, or analysts can actually use the AI securely
Everything ran inside the database. No data movement. No external vector store. No copy-paste security.
The Healthcare Demo: RAG With Real Security
In the live demo (Iโll embed a short video recap here in the post ๐ฅ), we walked through a concrete scenario:
A healthcare professional logs into an Oracle APEX app connected to an Oracle Database 23ai backend.
They ask a question that requires combining:
patient-related information,
internal medical guidelines,
and contextual knowledge stored as documents and vectors.
The system performs a RAG pipeline:
Uses AI Vector Search to find the most relevant chunks of information.
Orchestrates the flow via DBMS_VECTOR_CHAIN.
Ensures that Real Application Security rules are applied at every step:
A doctor, a pharmacist, and an admin do not see the same data.
Produces a GenAI-powered answer with the supporting evidence and proper traceability.
The key point: Even the embeddings (vectors) are subject to the same RAS policies as the original data.
That means:
No โshadowโ copies of sensitive data in external services.
No duplicated security logic.
No additional tools just to secure the AI layer.
As Thomas explains beautifully in his article on securing vectorized data with RAS in Oracle Database 23ai, this is where database-native AI becomes a real game changer for regulated industries like healthcare.
(Iโll link his article here in the blog so you can dive deeper into the technical details.)
The APEX Layer: Where Security Meets User Experience
Of course, all of this would stay theoretical if it lived only at database level.
Thatโs where Oracle APEX came in.
We built an application that:
Authenticates different healthcare roles
Calls the RAG pipeline inside the database
Visualizes:
the AI-generated answer,
the source documents used,
and the underlying security constraints in action
The message we wanted to send was clear:
You donโt need 10 different platforms to build secure, AI-powered apps. You can do it with one converged database and a low-code front end.
And based on the reactions after the sessionโฆ this really clicked.
Conversations at Nexus: Fragmentation vs. Convergence
Outside of the session, Nexus Luxembourg was all about conversations.
Over coffee and in the expo area, we heard the same pattern from many organisations:
Multiple databases
Different security models
Separate AI services
Integrations everywhere
It worksโฆ but itโs fragile and costly. Every time you add a new AI use case, you also add more complexity.
Thatโs why our message โ โOne database. One secure AI-ready foundation. One future.โ โ landed so strongly with both business and technical audiences.
Business leaders care about risk, cost, and speed. Technical teams care about architecture, security, and maintainability.
A converged platform gives both sides what they need.
Personally: What This Event Meant to Me
On a personal level, Nexus Luxembourg was special.
I got to represent Oracle and show work that I truly believe can make AI safer and more useful.
I had the chance to collaborate closely with Thomas, whose expertise on RAS and security is at the heart of this story.
I felt the strength of having strategy and field experience aligned โ from product vision to real demos.
The Luxembourg Oracle office also made us feel at home (itโs a small office, so I think we met almost everyone ๐).
For me, this event was another reminder that:
The real power of AI in the enterprise is not just having a model. Itโs about integrating AI securely into existing systems and workflows โ without breaking trust.
Whatโs Next?
This is just the beginning for us:
Weโll continue evolving this healthcare use case.
Weโre exploring how the same architecture can apply to other regulated industries (finance, public sector, etc.).
And weโll keep sharing what we learn โ in conferences, webinars, and yes, here on this blog.
If youโre curious about:
Securing vectorized data and RAG workflows in Oracle Database 23ai
Using RAS, AI Vector Search, DBMS_VECTOR_CHAIN, OML, Select AI, and APEX together
Or just want to understand how to simplify your AI architecture
โฆstay tuned. More posts (and more diagrams and demos!) are coming.
And if you were at Nexus Luxembourg and we met there: thank you for the conversations, the questions, and the energy.