AI is no longer just surfacing data, it’s helping make decisions with it. And that changes what a data catalog must do: provide meaning and ensure governance travels with the data. That’s the idea behind a Universal AI Catalog and how we’re building Snowflake Horizon Catalog: ❄️ Semantic context that gives data business meaning ❄️ Interoperability so governance follows data everywhere ❄️ Built-in policies, lineage, and security by design Without a universal AI catalog, AI guesses. But with it, AI reasons and acts—with trusted, governed context. Read more 👉 https://lnkd.in/eNWZmsAr
Intelligence and Interoperability: Data Catalog Must-Haves for AI Data Governance
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If Horizon is the universal AI catalog for meaning and governance around data, we’ve been working on the missing piece once agents move from “reasoning” to acting on that data. We’re building PrivateVault as an execution‑time control layer: every agent action that touches Snowflake (queries, writes, policy‑sensitive workflows) is evaluated against enterprise policy, context, and risk before it’s allowed to run, with a cryptographically verifiable audit trail. In my mind: Horizon ensures AI reasons with trusted, governed context; PrivateVault ensures those AI‑driven decisions are actually executed within policy. Would be keen to compare notes on how an execution firewall could plug into Horizon for high‑stakes use cases.
A well-defined semantic layer is non-negotiable for reliable AI. Without that business grammar, agents are essentially guessing at intent and interpreting data incorrectly. I also believe universal data catalog is critical for interoperability across different ai agents. Without that, duplication of logic and terminology, and inconsistent interpretations is inevitable. Sridhar Ramaswamy - one thing I didn’t see mentioned in the article or post: OSI (Open Semantics Interchange). Isn’t OSI‑compliant metadata a foundational piece for a truly universal AI catalog?
AI doesn’t struggle with data anymore. It struggles with decisions. You can give it perfect context, governance, lineage - and still end up with slow organizations. Because the bottleneck isn’t how data flows. It’s how decisions move across teams. If decision ownership, authority, and trade-offs aren’t explicit, AI just scales ambiguity faster. That’s where most systems break - not at the data layer, but at the decision layer.
Sridhar Ramaswamy Creating a universal AI catalog is essential. Embedding meaning and governance ensures AI decisions are reliable and aligned with business context rather than just data patterns.
Sridhar Ramaswamy - This is a strong articulation of what catalog infrastructure needs to do. Semantic Views with enforced join paths, governance that travels across engines, policies baked into the access path — this is the right foundation. If I may add — the layer it doesn't address — and no catalog platform currently does — is decision provenance. Horizon Catalog tells an agent what TX_LMT means and who's authorized to see it. That's governance-layer context. What it doesn't capture is what was decided using TX_LMT — by whom, under what conditions, what alternatives were considered, and whether that decision was overridden downstream. As agent proliferation accelerates, that gap becomes structural. Organizations won't just need to know what their data means — they'll need to know what their agents decided and why, including the unstructured human context (approvals, escalations, exceptions) that shapes how those decisions get made. Semantic catalogs are the prerequisite. Decision context is what makes them accountable at scale. We've been working through this architecture: luminitydigital.com/what-context-graphs-actually-require-upstream/
This is the shift most people underestimate. Adding context doesn’t guarantee good decisions. It just makes decisions possible. What actually matters is whether governance is enforced at the moment of action. Because once AI moves from “understanding” to “acting,” the question isn’t: does it have the right data? It’s: who defines what it is allowed to do with it. That’s the layer most architectures still don’t encode.
Luiza Jarovsky raises a critical point about the ethical implications of AI development and its impact on human roles. From my extensive experience in technology leadership, I've seen how important it is to maintain transparency and accountability as we advance artificial intelligence systems. It’s essential that these technologies are developed with safeguards against manipulation and misuse while ensuring they augment rather than replace the valuable skills of human workers. Looking ahead, fostering a culture where AI serves society's best interests will be key to navigating this transformative era responsibly. I encourage readers like you to stay informed about these developments by following thoughtful discourse on platforms such as Luiza’s Newsletter and engaging in open dialogues within the community.
Love the emphasis on interoperability here. The biggest challenge with AI right now isn't the models—it's the context. Having governance and lineage 'travel with the data' (especially with Iceberg/Open Catalog support) is exactly what’s needed to move past the POC stage and into production.
The logic here is sound but how does this handle the specific nightmare of unstructured data lineage for regional clients who can't use cloud-native governance tools? In our recent on-prem deployments, we’ve found that even with a catalog, the AI "Reasons" perfectly well until it hits a legacy permission structure that the catalog doesn't actually mirror. Genuine question—can a universal catalog ever truly be the source of truth if it doesn't also capture the physical data residency constraints that usually kill these projects in the Gulf?