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AI is proving that it’s here to stay. While 2023 brought wonder, and 2024 saw widespread experimentation, 2025 will be the year that telecommunications enterprises get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

Security has been an integral capability of Snowflake since the company was founded. Through the customer-configurable security capabilities of the Snowflake Horizon Catalog, we empower security admins and chief information security officers (CISOs) to better protect their environments and centralize threat monitoring and role-based access controls across clouds.

Large language models (LLMs) are at the heart of generative AI transformations, driving solutions across industries — from efficient customer support to simplified data analysis. Enterprises need performant, cost-effective and low-latency inference to scale their gen AI solutions. Yet, the complexity and computational demands of LLM inference present a challenge. Inference costs remain prohibitive for many workloads. That’s where SwiftKV and Snowflake Cortex AI come in.

AI is proving that it’s here to stay. While 2023 brought wonder, and 2024 saw widespread experimentation, 2025 will be the year that manufacturing enterprises get serious about AI's applications. But it’s complicated: AI proofs of concept are graduating from the sandbox to production, just as some of AI’s biggest cheerleaders are turning a bit dour.

Over the last three geospatial-centric blog posts, we’ve covered the basics of what geospatial data is, how it works in the broader world of data and how it specifically works in Snowflake based on our native support for GEOGRAPHY, GEOMETRY and H3. Those articles are great for dipping your toe in, getting a feel for the water and maybe even wading into the shallow end of the pool. But there is so much more you can do with geospatial data in your Snowflake account!

Today, we are excited to announce the general availability of Claude 3.5 Sonnet as the first Anthropic foundation model available in Snowflake Cortex AI. Customers can now access the most intelligent model in the Claude model family from Anthropic using familiar SQL, Python and REST API interfaces, within the Snowflake security perimeter.

Though AI is (still) the hottest technology topic, it’s not the overriding issue for enterprise security in 2025. Advanced AI will open up new attack vectors and also deliver new tools for protecting an organization’s data. But the underlying challenge is the sheer quantity of data that overworked cybersecurity teams face as they try to answer basic questions such as, “Are we under attack?”

Marketers at financial services companies have their work cut out for them. Their companies have a wealth of data, but that data is often fragmented among different systems and divisions, and protected-class data has a wide range of restrictions on how it can be used for different product lines.

As organizations mature in their execution of data and AI initiatives, a burning question remains: How do we measure the effectiveness of our teams and our impact on the business? This isn’t the perennial “What’s my data worth?” dilemma often asked rhetorically and answered theoretically. Today’s challenge is concrete: to define and track the metrics used to justify continued investment in data and AI innovation.

We often hear from customers that they’re excited about what they could do with data and AI but are not sure how to do it. Or that the tech teams are “all in” but they can’t convince the powers that be to move forward. It’s not that they don’t know what to do — they could list a number of initiatives or use cases that would benefit from insights from their data or to which they could apply AI. But many organizations seem to suffer from institutional paralysis.