One missing filter. That’s all it took for a fintech startup to accidentally expose customer transactions and churn two enterprise accounts overnight.

This is not meant to alarm you, it's about emphasizing that role-based data governance, powered by dynamic filters and row-level security, is your best safeguard. Whether you're embedding dashboards into your B2B SaaS app or a customer-facing portal, trust, compliance, and scalability must be foundational.

In this practical article, we’ll talk about the mechanics of RBAC and RLS, illustrate precisely how filters secure data at runtime, share relevant use cases, and highlight common pitfalls. By the end, you'll understand clearly why filters at the data layer aren’t optional for data governance—they’re essential.

RBAC & RLS: The Foundation of Embedded Governance

Every robust embedded analytics implementation starts with one foundational principle: clearly defining who has access to what. In complex SaaS platforms serving multiple stakeholders, it's critical to manage this at scale. This section explains how Role-Based Access Control (RBAC) and Row-Level Security (RLS) combine to form the core security mechanism for modern, multi-tenant analytics delivery.

RBAC ensures that each user only has access to the dashboards, metrics, and actions appropriate for their role. It defines visibility based on roles like Admin, Customer, or Partner and determines how much control a user has over what they see.

RLS enforces those rights inside every query, appending filters like WHERE org_id = 123 or region = 'US' directly into the SQL query. That means users only receive the rows they are entitled to—no more, no less.

Together, they allow you to:

• Run one multi‑tenant dashboard instead of hundreds of customer‑specific copies
  
• Meet GDPR, HIPAA, SOC 2 and similar frameworks without bespoke code
  
• Decouple security logic from UI logic, slashing maintenance in future sprints
  

How Role-Based Data Governance Is Implemented

Once the fundamentals of RBAC and RLS are understood, the next step is putting them into action across your entire data and analytics stack. For SaaS teams, this means embedding security into every layer—from backend databases to the front-end UI—so governance isn't bolted on later, but designed from the start.

Start at the workspace level, where roles like Admin, Analyst, and Viewer help define who can access what within your organization. Admins might have full access to workspace settings and dashboards, while Viewers are restricted to read-only insights. This initial segmentation prevents accidental modifications and enforces internal accountability.

Next, address multi-tenant architecture. There are two main approaches:

• Schema-level isolation, where each client gets their own database schema for maximum separation.
  
• Row-level tagging, where all clients share a database, but each record is tagged (e.g., org_id) so filters can isolate access by user.
  

This ensures tenants only see data that belongs to them—no exceptions.

On the data mart layer, implement Column-Level Security (CLS). Sensitive fields like salary bands, personal health information, or financial summaries should be hidden from non-authorized roles. A sales rep might see customer contract dates, but not their billing terms.

At the dashboard and metric level, apply Row-Level Security (RLS) along with default application filters. This ensures any chart, metric, or table displayed in the dashboard already respects user-level access—without having to configure every dashboard manually.

Finally, the embed SDK layer enables your app to pass signed tokens that carry user identity and claims. These tokens determine what the user sees and can do. Based on their attributes (e.g., org ID, role, department), appropriate filters and permissions are applied dynamically and invisibly.

When these layers work together, your analytics become inherently secure, scalable, and easier to manage—even as your user base grows into the thousands.

Static Filters vs. Dynamic Filters

Not all filters are created equal. While static filters are easy to configure and work for smaller datasets or limited user groups, they quickly break down when your user base or access rules start growing. In contrast, dynamic filters adapt at runtime based on user context, ensuring scalability and reducing maintenance overhead. This section outlines the pros and cons of both approaches and makes the case for dynamic filtering as the modern standard.

Static filters become brittle at scale. Dynamic filters, enforced at runtime, adapt automatically and securely.

Procurement SaaS: A Filtering Use Case

Procurement systems are among the most data-sensitive platforms in SaaS. Clients rely on them to handle millions in vendor contracts, purchase orders (POs), invoices, and internal approvals. One misconfigured dashboard could unintentionally expose private vendor rates or invoice data to the wrong department or even another client.

Imagine you're building analytics into a Procurement SaaS platform serving enterprise customers. Each organization wants to ensure their teams—procurement, finance, legal, and operations—only see the data relevant to their function. What's more, the platform must enforce this securely, without requiring engineers to manually duplicate dashboards or manage separate schemas per client.

Each user is issued a signed token that encodes their identity, organizational context, and access rights:

const token = db.generateToken({
  userId: session.user.id,
  orgId: session.user.org_id,
  department: 'finance',
  role: 'procurement_manager'
});

That token then triggers row-level filters at runtime:

Example row-level filter

With this setup:

• Procurement managers see PO statuses, vendor comparison dashboards, and contract lifecycles.
  
• Finance sees invoice aging, payment statuses, and audit trails.
  
• Legal can access only contract metadata and renewal alerts.
  
• No team can access another's sensitive views or metrics.
  

Crucially, this is all done using one set of dashboards. Databrain applies access filters invisibly at the data layer, which means:

• No duplication of assets
  
• No report-specific filter errors
  
• No need to assign reports manually by role or department
  

This approach makes your procurement analytics secure by default, scalable without overhead, and flexible enough to meet enterprise-grade audit requirements.

Common Pitfalls to Avoid

Implementing role-based data governance isn’t just about adding filters—it’s about understanding where those filters can break down or be bypassed entirely. Many SaaS teams fall into traps that seem harmless in early stages but become critical liabilities as the product scales. This section outlines the most common governance pitfalls and how to proactively avoid them.

Best practices from the industry

Governance isn't just a feature—it's a discipline. We’ve distilled hard-won lessons from teams running production-grade embedded dashboards and offers actionable best practices to keep your implementation secure, scalable, and audit-ready.

• Sign your tokens: Always use signed JSON Web Tokens (JWTs) to securely transmit user identity and roles. These tokens should include an expiration time (TTL) to reduce the risk of long-term misuse, and the signature must be tamper-proof to ensure no malicious alterations can be made client-side. A well-implemented token system prevents unauthorized access and forms the cornerstone of runtime filter enforcement.
  
  
• Log all access: Maintain detailed logs of every data access event. This includes who accessed what dashboard, under which role, and what filters were applied during query execution. These logs are crucial not just for audit compliance (e.g., SOC 2, HIPAA) but also for diagnosing access issues or detecting suspicious behavior in real-time.
  
  
• Minimize exposure: Reduce visible options and controls in the dashboard interface based on the user’s role. This means hiding filters, tabs, and export options that are not relevant to the current session. Minimizing UI clutter doesn’t just enhance usability—it also prevents accidental or intentional misuse of analytics capabilities.
  
  
• Test governance: Integrate governance rule validation into your CI/CD pipeline. Write unit tests to simulate different user roles and token payloads, verifying that RLS and CLS filters apply as intended. This prevents regressions where a code change could inadvertently disable key filters or expose sensitive data.

Databrain’s Role-Based Governance Framework

Building your own governance model from scratch is time-consuming and error-prone. Databrain bakes role-based governance directly into the platform—offering a full spectrum of controls across data, dashboards, and UI elements. This section outlines the key features that make Databrain a trusted foundation for secure, multi-tenant analytics delivery.

Databrain’s governance framework spans every critical layer of the analytics stack, ensuring that data is secure, context-aware, and compliant by design. Here's how it works:

• Workspace Roles: Define access levels within your internal team. Admins can configure everything, Editors can create dashboards, Viewers are restricted to consumption, and Developers can manage data models and integrations.
  ‍
  
• Data Mart Tenancy: Offers flexibility to isolate client data through either schema-level separation (each client has their own schema) or row-level tagging (all data in a shared schema, with access filtered by org_id or client_id).
  
  
• Column-Level Security (CLS): Restrict access to sensitive fields like financial metrics, employee salaries, or personal identifiers. Only authorized roles can view these columns, while others get a masked or empty value.
  
  
• Row-Level Security (RLS): Dynamically applied conditions (e.g., WHERE org_id = {{ user.org_id }}) ensure that each user only sees data relevant to their organization or department.
  
  
• Embed SDK Permissions: Allows your customer to control how their own end-users interact with the dashboards—whether they can download charts, change layouts, or archive metrics.
  
  
• Scheduled Reports: Enable secure delivery of dashboards and metrics via email or third-party integrations. RLS and CLS are automatically applied to each scheduled export to prevent data leakage.

Databrain provides a unified, SDK-first governance model that combines runtime personalization, RBAC, RLS, CLS, and permission-based access control into one cohesive system. It's not just about filters—it's about how those filters are enforced automatically through signed tokens and intelligent defaults.

This full-stack governance ensures that whether you're building for 10 users or 10,000, your analytics infrastructure remains reliable, scalable, and enterprise-ready.

Final Thoughts: Governance Is Not Optional

Strong governance is no longer a nice-to-have—it's a requirement for any SaaS platform offering customer-facing analytics. One mistake can compromise sensitive data, damage customer trust, and expose your business to legal consequences. In this final section, we reiterate the value of building governance into the analytics layer from day one, and how Databrain simplifies the journey from risk to resilience.

The cost of getting data governance wrong isn’t just technical debt—it’s customer churn, lost contracts, and reputational risk.

With Databrain, implementing RBAC and RLS doesn’t mean building a custom security layer. Instead, it’s a few lines of code and a well-structured filter strategy.

Ready to secure your dashboards? Request a demo and ship governed analytics this sprint.