Summary: Snowflake Coco’s ability to generate governance SQL on demand is a genuine productivity win for data engineers. But productivity and compliance are different problems. This article breaks down the structural gaps between AI-generated policies and audit-ready governance and makes the case for treating Coco and a dedicated control plane as complementary tools rather than alternatives.
There is a lot of excitement right now about what AI coding agents can do for data teams. And honestly, some of it is warranted. Snowflake Coco, the AI coding agent unveiled at Summit 2026, can now generate governance SQL on demand. Classification rules, dynamic masking policies, row access controls — type a request, get working code. For a data engineer juggling three deadlines, that is genuinely useful.
But here is the question nobody seems to be asking loudly enough: when an AI agent writes your masking policy, who owns the audit?
That question matters. A lot. Because convenience and compliance are not the same thing, and the gap between them tends to show up at the worst possible moment, such as during an audit, a breach investigation, or a regulator review.
Writing a Policy Is Not the Same as Governing Data
Coco is a productivity tool and a very capable one. It accelerates the translation of intent into SQL. That is real value, and it would be intellectually dishonest to dismiss it.
But governance is not a task you complete. It is a discipline you sustain.
The relevant question is not “can it write the policy?” Almost anything can write a policy. The real question is whether the resulting control is something you can stand behind. Something deterministic. Something auditable. Something that holds up when a compliance officer asks you to prove what ran, when it ran, who approved it, and why it exists.
AI can accelerate governance authoring. It does not eliminate the need for human ownership, validation, and accountability. Those remain the foundation of any defensible governance program.
The Six Things an Auditor Actually Cares About
When regulators show up, they are not impressed by how fast your team moves. They want to know six things:
Determinism. Does your control resolve the same way every single time, for every user, on every query? Or does it depend on what an LLM happened to generate last Tuesday?
Ownership. Can you point to a named human being who is accountable for each policy? Or is the owner of record a probabilistic AI that might generate something slightly different tomorrow?
Auditability. Can you prove what ran and why, with a complete, tamper-resistant log? Not just what an agent suggested, but what was approved, deployed, executed, and monitored in production.
Depth. Does protection exist below the query layer, or only at read time? If sensitive values are sitting unprotected at rest, read-time masking is only part of the story.
Breadth. Does one policy follow your data across platforms? Or does governance dissolve the moment data moves to another system?
Cost predictability. Can you budget for governance spend, or does it scale with how many questions your analysts ask an AI?
Coco has genuine gaps across all six. And these are not missing features that will ship next quarter. They are structural limitations of what Coco fundamentally is: a probabilistic agent generating native SQL.
Four Gaps Worth Taking Seriously
The determinism gap. LLMs are probabilistic by design. That is what makes them flexible and useful for generating code. But governance controls are held to a different standard than development assistance. When an AI agent generates a masking policy, the organization still owns validating that policy, approving it, and proving it behaves as intended. “Mostly right” may be acceptable for drafting SQL. It is not acceptable for regulated data controls.
The depth gap. Coco’s enforcement ceiling is Snowflake’s native primitives — dynamic masking and row access policies, applied at read time. That is meaningful protection, but it is only one layer. It does not include tokenization of sensitive values at rest, format-preserving encryption, rate limiting, anomaly blocking, or vaulted storage. If your compliance program requires proof that sensitive data is protected even outside the read path — and many PCI DSS and HIPAA programs do — you need protection that lives below the query layer.
The breadth and ownership gap. Coco is single-platform. The moment data lands in Databricks, SQL Server, Redshift, or any other system, the policy does not follow it. You are back to managing governance in silos, with different controls and different audit trails for each environment. Coco does not inherently provide a centralized policy registry, named policy ownership, or a governance control plane. Organizations can build those processes themselves, but they remain separate from the AI-generated policy workflow.
This is where many governance conversations break down. Regulators do not audit AI agents. They audit organizations. When an auditor asks why a particular user could access sensitive data on a specific day, the answer cannot be “the agent generated the policy.” Someone must own that control, approve it, and be accountable for its outcome. AI can accelerate governance. It cannot assume accountability for it.
The cost gap. This one tends to surprise teams the most. Coco’s governance work is consumption-priced. Classification scans and agent interactions consume Snowflake Cortex resources, meaning governance-related costs can increase alongside AI usage. As a result, governance spending may become increasingly tied to AI usage patterns rather than governance requirements, making budgeting and long-term planning more challenging. You do not want a token-metered LLM sitting in your governance workflow. Otherwise, governance becomes a variable cost that scales with curiosity rather than risk.
This Does Not Have to Be Either/Or
Here is the thing: Snowflake is a great platform, and Coco is a useful tool for the teams building on it. The goal is not to pick sides. ALTR is a Snowflake Premier Horizon partner precisely because these capabilities are designed to work together, not compete.
The clean framing is this: use Coco to explore and move fast. Use a dedicated control plane when the control has to hold up. Your developers keep their productivity agent. Your security and compliance teams get governance they can actually defend.
When a financial institution serving millions of customers needs to demonstrate that its masking policies are consistently enforced, owned, and auditable across every query against regulated data, “the agent wrote it” is not an acceptable answer. Those teams need a named human owner for every policy, a complete and exportable audit log of every sensitive data access, and protection that extends beyond read-time masking to include tokenization and encryption at rest.
That is what a purpose-built governance platform delivers. Not instead of the productivity tools your engineers love, but alongside them.
What Defensible Governance Actually Looks Like
The controls that hold up in audits share a few characteristics. They are no-code and deterministic, so the same input resolves to the same enforcement every single time. They provide real-time activity monitoring with active blocking, and they stream that activity directly to your SIEM or SOC so your security team can act on it. They include protection at rest, not just at read time, through vaulted tokenization and format-preserving encryption. And they span your full data environment through a single policy plane, so governance does not fragment when data moves across platforms.
They also have a named human owner for every policy. That is not a minor detail. It is the accountability structure that makes the rest of it defensible.
Governance cost that is predictable and flat is part of this picture too. When compliance spend does not scale with LLM token consumption, you can actually budget for it and make the business case for sustaining it long term.
The Question to Ask Before Your Next Audit
The next time someone on your team proposes using an AI agent to generate governance SQL, ask one follow-up question: if an auditor asked you to prove that control is working correctly, who owns the answer?
If the answer involves scrolling through AI-generated code and hoping it still says what it said last month, you have a gap. Not a “we’ll fix it eventually” gap. An “our compliance program rests on this” gap.
Governance is not a task you hand off to an agent. It is a discipline you build a control plane around.