Dev Summit 2026

From routine incidents to national crises, the France Cybersecurity team train our companies for the worst.
Join us as we break down our training methodology, share key results, and reveal the most frequent attack patterns we face today.
We’ll dive deep into a real-world scenario at Leroy Merlin France: after 2 hours of total network isolation, discover how store teams successfully maintained self-service and kept the store open.
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La direction cybersécurité France entraîne nos entreprises à faire face à des situations cybersécurité allant de la réaction à un incident basique à une crise nationale.
Dans ce Talk, nous abordons comment nous mettons en place ces entraînements quels sont les résultats et les scénarios d'attaques qui sont le plus observés et sur lesquels nous devons être prêt.
Nous ferons un retour plus en détail sur l'expérience vécu en Magasin Leroy Merlin France après une isolation totale du réseau pendant 2 heures -> comment les équipes en magasins ont pu maintenir le libre service et ouvrir le magasin.

Deploying custom MCP servers requires more than just a connection—it requires a robust, governed perimeter. This session explores how we leverage our AI Gateway / Developer Portal as the central nervous system for all MCP traffic, transforming standalone LLMs into actionable enterprise tools.

We will do a deep dive into:

* The "Gold Path" Deployment: Utilizing the Developer Portal to publish and manage consumers.
* REST API Synthesis: Strategies for wrapping our existing internal REST ecosystem into MCP tools, allowing LLMs to interact with legacy and microservice architectures securely.
* The AI Gateway Architecture: A breakdown of the security primitives—including identity propagation, request filtering, and automated rate limiting—that protect our data.
* Deep Observability: Utilizing our telemetry stack to ensure every AI-driven action is logged, audited, and debuggable in real-time.

AI looks clean in tutorials. In production, it rarely is. This workshop offers a practitioner’s walkthrough of real-world ML development - exploring what the process actually looks like when you’re building detection systems for real clients, with real data, under real constraints.
Drawing on experience developing and deploying machine learning systems inside global financial market surveillance, the session is structured around three transferable lessons: how to translate a client’s business problem into a well-formed ML problem; how to stay flexible and recalibrate when early assumptions don’t hold; and how to navigate the statistical realities of imbalanced, dependent, and noisy real-world data.
Each lesson is grounded in real project experience - honest about the complexity, and focused on the thinking and decision-making that determines whether a model earns its place in production. Attendees will leave with practical insight they can apply to their own projects, and a clearer picture of what separates ML that ships from ML that stalls.

The "Post-Cookie" era has left the digital ecosystem at a high-stakes crossroads: sacrifice measurement and user experience, or sacrifice privacy. While current workarounds like "Clean Rooms" and "Contextual Targeting" offer temporary relief, they fail to bridge the gap between **total user anonymity** and **hyper-relevant engagement**.

This talk introduces **Fully Homomorphic Encryption (FHE)** as the ultimate architectural pivot—a cryptographic framework that allows for both precise attribution and real-time website personalization without ever decrypting user data. We will explore how FHE enables "Blind Personalization," where a server can rank products and serve tailored content to a user it doesn't "know," and how it solves the attribution loop with mathematical certainty.

By moving past the "FHE is too slow" myth, we will demonstrate how 2026’s hardware acceleration and edge computing are making **Zero-Knowledge Advertising**—from the first click to the final purchase—a production-ready reality.

From Jeremie Forgeon, Director of Product @Decathlon Digital

LMDIS (Last Mile Delivery Intelligence System) is an AI-powered decision support tool for daily delivery fleet planning at Golilla. The system automatically analyses ~300 delivery addresses overnight, combining Google Street View imagery, OpenStreetMap road topology, and Claude AI vision to generate a structured operational briefing per order.
At its core, LMDIS resolves two simultaneous constraints: the minimum vehicle size imposed by cargo characteristics (weight, dimensions, product type — sourced via TMS API) and the maximum vehicle size permitted by road accessibility (carriageway width, turning radius, physical restrictions). The intersection determines the optimal vehicle assignment; when no intersection exists, the system identifies the nearest accessible point for the largest available vehicle and flags the conflict for manual resolution.
The road analysis engine follows actual OSM way geometry to sample points along both the delivery street and the access route from the nearest primary artery — eliminating the linear coordinate offset errors that make naive Street View analysis unreliable. A four-level heading cascade (OSM geometry → Google Roads snap-to-road → panorama inference → manual review flag) ensures no analysis proceeds with an incorrectly oriented image.