At Matters.AI, our mission is to serve as the AI Security Engineer for Data. We help security and privacy teams automatically discover where sensitive data resides, monitor who interacts with it, and rapidly neutralize risks before they lead to a breach.
Following our August focus on data lineage and cloud storage remediations, September brings a massive wave of platform improvements. We are giving teams granular control over scan execution, unifying our policy engine for enterprise scale, expanding coverage across popular collaboration hubs, and automating complex privacy workflows like Data Subject Requests.
Here is a breakdown of everything we shipped in September 2026.
1. Precision Scan Management
Previously, executing a scan was often an all or nothing operation. Administrators needed a way to tailor scan cadences and scopes to specific datastores without impacting active jobs.
What’s New
Scan Management replaces that rigid model entirely. Security administrators can now schedule recurring scans on a per datastore basis, trigger immediate on demand runs, and define precise target scopes using hand picked or rule based groupings.
Key Capabilities and Coverage
- Granular Schedules and History: Set custom scan cadences for each datastore and review a complete history of past runs.
- Flexible Scopes: Target specific datastores using hand picked lists, dynamic rule based filters, or environment wide selections, backed by detailed target count views.
- On Demand Execution: Trigger a Run Now job instantly for any datastore without waiting for the next scheduled interval.
- Intelligent Busy Target Skipping: The platform automatically skips any datastore currently undergoing a scan and proceeds with the rest of the job, eliminating stalled schedules across scheduled or on demand runs.
Impact: Total operational control over when and how scans run, preventing a single busy datastore from blocking your entire scanning pipeline.
2. Policies and Scopes v2
As enterprise data footprints expand, enforcing policies consistently across Data Activity Monitoring, Data Detection and Response, and posture management can create unwanted complexity.
What’s New
We rebuilt our policy, policy scope, and DDR pipeline APIs from the ground up to give teams a single, consistent management framework across every environment.
Key Capabilities and Use Cases
- Unified Management: Manage policies and scopes across DAM, DDR, and exposure detection through one standardized pipeline.
- Out of the Box UEBA Policies: New User and Entity Behavior Analytics policies enable behavior based threat detection right out of the box.
- Expanded Catalog Seeding: Policies now seed directly from every catalog folder, including new policies for MSSQL DAM and Azure DDR.
- Enterprise Background Syncing: Fleet wide policy seeding and syncing now run as background jobs, ensuring large enterprise environments never time out mid sync.
Impact: Seamless, enterprise grade policy enforcement and behavior based threat detection that scales effortlessly without technical friction.
3. New Integration Coverage
Security teams need visibility into every layer where sensitive data and logs reside, especially across collaboration platforms and cloud infrastructure.
What’s New
We introduced four powerful integrations that extend our DSPM, DDR, and posture management capabilities into Slack, Feishu or Lark, Azure data plane logs, and Upwind.
Key Capabilities and Integrations
- Slack DSPM: Scan channel files and canvases for sensitive data with one click remediation available directly inside the console.
- Feishu and Lark Integration: Ingest SaaS audit logs into DDR for real time threat detection, file lineage tracking, and user activity monitoring.
- Azure Log Ingestion: Ingest data plane activity and audit logs covering Azure Activity, Azure SQL, Azure MySQL, and Azure PostgreSQL.
- Upwind Posture Sync: Ingest cloud misconfiguration findings from Upwind directly into your inventory and posture views alongside existing exposure data.
Impact: Eliminates critical blind spots in popular collaboration hubs and cloud environments to give teams unified data visibility.
4. Advanced Lineage and Data Sprawl Intelligence
Tracking data sprawl and connecting identical files across different cloud and endpoint locations is essential for data minimization initiatives.
What Is New
We expanded our intelligence capabilities with Inferred Lineage, Feishu activity lineage, and account wide Data Sprawl leaderboards.
Key Capabilities and Use Cases
- Inferred Lineage: Automatically connect identical files across different sources, such as linking an original file sitting in OneDrive to a local copy on an endpoint, complete with confidence scores and evidence.
- Data Sprawl Leaderboards: Surface your most duplicated and active files across your entire account, backed by drawer views breaking down copy counts and exact locations.
- Enhanced Content Hashing: Rapidly locate where files exist across sources using case insensitive content hash lookups.
Impact: Takes the guesswork out of data governance by surfacing scattered copies and clear file movement history.
5. DSR Scan for Privacy Engineering
Answering Data Subject Requests across complex structured database environments has historically required tedious manual queries and coordination across multiple teams.
What’s New
DSR Scan automates the discovery and evidence layer for access, correction, and erasure requests, allowing privacy teams to locate personal data across databases in minutes.
Key Capabilities and Use Cases
- Classification Driven Discovery: Enter a primary identifier, such as a customer email or custom regional identifier, and Matters searches every connected database for matched personal data elements down to the table and column.
- Column Level Evidence Records: View matched results grouped by data element in a single view that serves as an audit ready record for compliance frameworks like India’s DPDP Rules.
- Rescan to Verify: Re-run the request after remediation teams act to confirm that the personal data was successfully updated or erased.
- Supported Databases: Covers MySQL, PostgreSQL, Oracle, and MSSQL hosted on Amazon RDS, Google Cloud SQL, or self hosted environments.
Impact: Turns a manual, error prone compliance task into an automated, verifiable discovery and audit workflow.
6. Enterprise Platform Enhancements
- DAM Assist Support: Expanded database activity monitoring support for self hosted MSSQL, PostgreSQL, and MySQL databases with instance level grouping.
- All Assets Overhaul: Introduced a unified project wide Data Objects view, 100,000 row CSV exports, enhanced filtering, accurate scan state indicators, and optimized database queries that dramatically improve page load times.
- Dedicated Secrets Management: Customer integration credentials now route through a dedicated secrets service with live connectivity status indicators inside the Assist view.
September Updates at a Glance
- Scan Management: Granular per datastore schedules, custom scopes, on demand runs, and automatic busy target skipping.
- Policies v2 and UEBA: Unified policy framework across DAM, DDR, and exposure detection with background syncing and out of the box behavior policies.
- New Integrations: DSPM for Slack and Feishu or Lark, expanded Azure audit log ingestion, and Upwind posture sync.
- DSR Scan: Automated PII discovery and column level verification across structured databases for rapid compliance reporting.
All of these capabilities are live in your Matters.AI console today. Log in, configure your scan schedules, and test out DSR Scan to experience the new updates firsthand!
