Databricks Completes Acquisition of Panther: Accelerating the Security Lakehouse Era
Summary
Databricks has officially completed its acquisition of Panther, combining mature SOC workflows and a software-driven detection engine with Lakewatch’s open security lakehouse foundation. Security teams can now retain
Summary generated by brickster.ai. For the full article, follow the source link above.
More from Databricks Blog
Databricks joins the Open Secure AI Alliance to advance AI safety and security
* Databricks is an inaugural member of the Open Secure AI Alliance, a coalition of industry leaders committed to advancing AI safety and security through open research: open models, open harnesses, open tooling, and shared learnings. * We bring open code and concrete architecture
The New Monday Morning Report: How Generative AI can deliver the insights your executives need.
Rebuilding the retail Monday Morning Report with generative AI converts passive weekly reviews into actionable execution by fusing internal and external signals, ranking store and SKU watch-outs, and drafting recommendations. Implemented directly on your governed lakehouse, this architecture relies on Genie Ontology for context, Unity AI Gateway for control, and complete freedom of model choice across any
Ingest semi-structured data faster and more efficiently with Variant - Now Generally Available
Databricks Variant is now Generally Available, enabling teams to achieve up to 30x faster reads on semi-structured data while handling unpredictable schema changes without pipeline updates. The feature is broadly integrated across the platform, supporting data workloads like Auto Loader and Spark Declarative Pipelines alongside AI tools like Agent Bricks and AI Functions.
Backstage with Lakebase, part 3
In the first part of this series, running Backstage on Databricks Lakebase gave us...
Foundations for an AI-forward healthcare organization
Foundations for an AI-forward healthcare organization tackles the real blocker to health system AI adoption: fragmented data, rigid or undefined governance, and a misaligned operating model — not procurement. It argues that with governance built into the platform and serverless infrastructure, health systems can get a first governed AI use case live in weeks rather than months.
Agentic media buying cannot scale without the right foundation. See how buyers and sellers get there on Databricks.
Databricks has released a reference implementation and self-deploy accelerator for agentic media buying, giving buyer and seller organizations a working blueprint where autonomous agents transact using governed data, transactional state, identity, hosted models, and observability unified on one platform. Teams can stand this up directly in their own workspace rather than stitching together a multi-vendor integration, adapting the agents to transact with partners over open standards and shifting effort from manual coordination to strategy and results.