If you’ve ever tried to implement meaningful data security inside Snowflake, you already know the conversation. 

It usually starts with good intentions and ends in a stalemate. Data teams worry about disruption. Security teams worry about exposure. 

And somewhere in the middle, progress slows to a crawl while both sides argue over who should be allowed to touch what. 

We’ve seen this play out dozens of times. In one recent case, it took nearly six months, dozens of stakeholders, and countless meetings to resolve a single question: who owns masking and access controls inside Snowflake? 

By the end of it, one of the architects involved said something that stuck with me: “Now that we see it, it’s obvious. But we wasted months fighting over it.” 

They weren’t alone. And the problem wasn’t technology. It was ownership. 

Why Data and Security Keep Colliding 

Modern data security doesn’t just “protect” information. It changes how data is accessed, how it’s stored, and how it behaves downstream. 

That’s where the friction starts. 

Data teams are responsible for pipelines, transformations, quality, and availability. They’ve often been forced, by necessity, to take on security work as well: writing masking policies, managing roles, and maintaining access logic directly inside Snowflake.  

Security teams, on the other hand, are accountable for risk reduction, auditability, and compliance. They want centralized control, consistent policy enforcement, and proof that sensitive data is being protected. 

Both sides are right. And both sides are frustrated. 

The false assumption many organizations make is that one team has to own everything. Either data keeps control and security stays advisory, or security takes over and data braces for disruption. 

That assumption is wrong. 

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The Missing Piece Isn’t Control. It’s Division of Labor. 

Modern data platforms can’t succeed if securing sensitive data requires rewriting pipelines or slowing delivery. When that happens, trust erodes, and teams work around the platform instead of with it. Snowflake accounted for this reality by providing a native mechanism that allows responsibility to be shared cleanly between teams: tags. 

Tags aren’t new. They’ve been there for years. What’s been missing is how they’re used. 

When applied consistently, tags become more than metadata. They become a shared contract between data and security, one that defines what needs protection without dictating how each team does its job. 

Here’s what that contract looks like in practice. 

A Shared Model That Actually Works 

Data teams do what they already do best: 

• Ingest data through Snowpipes, ETL tools, or transformations 

• Classify data, either manually or automatically 

• Apply agreed-upon security tags to columns or tables as data moves into Snowflake 

That’s it. No policy logic. No masking rules. No access rewrites. Just tagging. 

Security teams do what they are built to do: 

• Define masking and access policies  

• Decide who sees full values, partial values, or masked values 

• Bind those policies to the agreed-upon security tags 

• Deploy and audit those policies without touching pipelines 

Once policies are written, Snowflake enforces them natively every time data is queried, regardless of who’s consuming it. 

No handoffs. No duplicated work. No fragile custom logic buried in SQL. 

Why This Matters More Than It Sounds 

This division of responsibility changes more than workflows; it changes accountability. 

Data teams are no longer the last line of defense for sensitive information. They can focus on data quality, performance, and availability without carrying security risks they were never meant to own. 

Security teams gain real control. Not screenshots. Not assumptions. Actual, enforceable policies tied directly to data classification. 

And the business gains something it rarely has today: proof. 

Proof that sensitive fields are tagged, that policies are applied, and that access is behaving exactly as intended. 

One Source of Truth Beats Endless Meetings 

The final piece most organizations overlook is visibility. When responsibility is shared, accountability must be shared too. 

That’s why mature teams rely on a single data protection view, one place where both data and security can confirm that: 

• Sensitive columns are properly tagged 

• Masking policies are attached and active 

• Coverage gaps are visible immediately 

This isn’t about dashboards for dashboards’ sake. It’s about eliminating guesswork before an audit, or an incident, forces the issue. 

Stop Arguing About Ownership. Start Designing for It. 

The data–security standoff isn’t a cultural problem. It’s a design problem. 

Snowflake already gives organizations the primitives to solve it. Tags create a shared language. Policy enforcement stays centralized. Pipelines stay intact. 

When teams stop fighting over who controls everything and start agreeing on who owns what, security stops being a bottleneck, and data stops being a liability. 

That’s not just better collaboration. It’s how modern data platforms were meant to operate. 

At ALTR, this is the operating model we’ve built around. Our role isn’t to replace Snowflake’s native security features or disrupt data workflows, but to give security teams a dedicated control plane where they can define, test, deploy, and audit data protection policies, while Snowflake enforces them natively through tags. ALTR also provides a shared data protection dashboard that gives both data and security teams a single source of truth: where sensitive data is tagged, where policies are applied, and where gaps may exist. The result is a clean separation of responsibilities with shared visibility; data teams stay focused on availability and quality, security teams maintain provable control, and the business gains confidence that sensitive data is protected by design, not by assumption.