Customer Overview
Credit Saison is a premier financial services institution operating across a complex, multi-entity enterprise ecosystem. Navigating a highly regulated environment where sensitive customer information flows across SaaS platforms, distributed corporate endpoints, and large-scale cloud databases, the company requires an enterprise-grade data protection framework. To safeguard its vast operational footprint without introducing architectural rigidity or performance trade-offs, Credit Saison partnered with Matters.AI to unify its security posture and establish a continuous, audit-ready data defense foundation.
The Data Lineage Blind Spot: While existing tools secured data at rest within isolated cloud and SaaS silos, the team could not track data flow. This lack of end-to-end lineage tracking was the organization’s largest pre-implementation visibility gap.
Siloed Multi-Surface Visibility: Managing data security across disparate environments including cloud databases, SaaS collaboration tools, and hybrid endpoints, created critical coverage gaps and left data movement unmonitored.
Endpoint Performance Constraints: Deploying traditional, resource-heavy monitoring agents across a highly distributed device estate risked degrading user experience and facing immediate pushback from IT operations.
Unsustainable Cloud Logging Economics: Scaling database activity monitoring across production environments threatened to generate massive, unmanageable cloud infrastructure and logging costs that made standard deployments financially unviable.
Continuous Data Flow Lineage: Matters.AI replaced fragmented monitoring with an integrated framework that maps data movement across cloud data stores, SaaS platforms, and enterprise endpoints into a single pane of glass. This allows the security team to trace a file’s entire lifecycle from inception to destination.
Content-Aware File Fingerprinting: To secure the last mile, Matters.AI deployed low-footprint endpoint monitoring that includes native USB peripheral control. By tracking the core identity of the data rather than fragile metadata, Matters maintains persistent visibility. Even if a user alters a file name, changes the extension, or moves it to a new location, the lineage remains intact.
Cost-Optimized Database Auditing: The platform re-engineered the log ingestion pipeline to bypass expensive cloud logging systems, directly collecting database activity through optimized native connections to maintain full compliance and visibility at a fraction of the traditional cost.
Matters’ Impact
- Continuous Data Lineage Across Cloud, SaaS, and Endpoint
Maintained 100% lineage continuity as sensitive financial records moved across cloud, SaaS, and endpoint, tracking every handoff so no surface became a blind spot. The differentiator most posture tools cannot follow, since they stop at cloud and SaaS. - Multi Surface Scope Mapped
Unified visibility across hundreds of terabytes of data, thousands of SaaS enterprise users, and a hybrid device fleet under a single, cohesive security layer. - Endpoint Onboarding Without Friction
Brought a hybrid device fleet under continuous monitoring without disrupting end users, extending lineage tracking onto the surface most posture tools never reach, the endpoint. - Real Time Awareness
Maintained a stable Mean Time to Detect under 5 minutes across all monitored surfaces, instantly identifying unauthorized access patterns and catching lineage breaks the moment they occur. - Continuous Audit Readiness
Accelerated regional data privacy compliance through automated classification of sensitive financial data types, without adding architectural or operational friction.
Strategic Takeaway
Securing a modern enterprise requires a data protection framework that matches the speed of business operations without introducing friction. By breaking down the silos between cloud, SaaS, and endpoint security, while simultaneously optimizing our infrastructure economics Matters.AI has delivered an outcome-driven security posture that scales effortlessly alongside our business.”
