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At UN Global Compact Leaders Summit, EDB reinforces governance at the data layer as energy becomes a defining constraint in the agentic AI era. With 69% of respondents in new EDB research giving energy greater weight in AI infrastructure decisions, EDB outlined its work to govern autonomous agents at the data layer and reduce AI energy demands. Date -2026-09-25 NEW YORK, Sept. 25, 2026 /PRNewswire/ - Featured at the United Nations Global Compact Leaders Summit this week, EnterpriseDB (EDB) called on business and government leaders to treat energy efficiency and sustainability as core architectural decisions in the age of agentic AI, not afterthoughts. As AI agents scale, EDB made the case that responsibility must be engineered into AI infrastructure, highlighting a patent-pending approach to governing autonomous agents and validated results showing dramatically lower compute and emissions. In a keynote, EDB Chief Legal Officer Rob Feldman argued that the world already has the chips, the models, and the infrastructure for the AI era but is missing the operating system to tie them together securely, sovereignly, and sustainably. Initial findings from new EDB research among 300 respondents underscores the pressure on organizations to make AI more efficient (Sept. 2026). Sixty-nine percent say energy receives more consideration in data and AI platform decisions than a year ago. Reliability of supply ranks among the leading energy factors at 49 percent, followed by cost and energy efficiency, both at 48 percent. The findings put energy firmly among the operating requirements for AI infrastructure. By 2030, more than one billion AI agents could be at work, while global data-center electricity demand is projected to roughly double, according to the International Energy Agency. In his address, Feldman said companies cannot wait for regulation to anticipate every use or consequence of autonomous technology. The protections need to be part of how these systems are designed. Building responsibility into the core EDB framed the challenge through its Responsible AI work, guided by a set of principles: that AI systems should be sovereign, governed, trusted, and beneficial, and that these qualities must be part of how systems are designed, not added after the fact. Organizations, the company argued, need control over where their data resides, how it is accessed, and how AI systems interact with it. What ties the chips, models, and infrastructure of the AI era together, EDB said, is a governing layer for how they interact with data, a sovereign AI operating system. To that end, the company is building governance directly into the data layer, giving organizations the tools to govern their data at the source. The company recently filed a patent application for an approach that uses AI agents to govern other agents. This is comparable, Feldman noted, to the redundant, self-checking systems built into modern aircraft. EDB also pointed to its support for open source software and sovereign data as a means of bringing greater security and control to sensitive information. Intelligence per watt: Efficiency as a design principle Central to EDB's argument is that efficiency belongs in that same list of elements designed from the start. Rather than build technology that simply requires customers to add more servers and compute to support AI workloads, EDB Postgres(R) AI (EDB PG AI) reduces unnecessary compute, consolidates workloads, and moves less data, an approach it calls "intelligence per watt." EDB benchmarks show the approach can cut token consumption by up to 57 percent for one customer, reducing the processing required to support agent activity. Making a measurable difference To test whether responsible design makes a measurable difference, EDB worked with three of its larger customers, operating more than 120 data centers, and is now building a carbon calculator to measure the before-and-after impact of consolidating workloads. In one case, independent analysis by Incendium Consulting found up to 94 percent fewer compute cores and up to 87 percent expected emissions reduction. EDB was careful to note that such steps do not eliminate every risk but offered them as an example of the design thinking the AI era demands. "We don't believe performance and the planet have to be a trade-off," said Rob Feldman, chief legal officer at EDB. "Using fewer resources can mean better performance, lower costs, lower environmental impact, and more resilient businesses. To me, that is what responsible innovation looks like." Those results echo EDB's broader research. Its global Sovereignty Matters study of 2,050 executives across 13 economies found that 13 percent of enterprises are treating AI and data sovereignty as mission critical today. These leaders achieve up to 5x the ROI of their peers and are 250 percent more likely to build their data centers to be green. And that urgency is showing up in how organizations buy: In initial findings from EDB's latest research, more than two-thirds of enterprise technology decision-makers (69 percent) say they now weigh energy more heavily in AI and data infrastructure decisions than they did a year ago, with energy efficiency (48 percent) ranking alongside cost and reliability as a top consideration, well ahead of purely environmental factors (EDB, Sept. 2026). Energy, in other words, has become an operational infrastructure criterion, not only an ESG one. That shift is reinforced by rising disclosure expectations: Environmental reporting through frameworks such as CDP has become a baseline demand from capital markets and large buyers, making the efficiency of the infrastructure underneath AI both an operational and a reporting concern. Leading by example, and calling for shared action Beyond its products, EDB pointed to its own practices: The company has set science-based emissions reduction targets and is working to reduce its environmental footprint, and more than a year ago it established an internal AI Governance Committee and an "AI for Good" initiative to align its development and deployment of AI with its principles. Feldman, who serves on the UN Global Compact Legal Council and the UN's Think Lab on AI for Business Integrity, closed with a call for shared action, arguing that no organization will solve these challenges alone and that leaders should openly share what works and what does not. "AI is creating extraordinary possibilities. The challenge for all of us is making sure responsibility keeps pace," Feldman told the summit. "At EDB, we are focused on the part we can influence: protecting and governing data, giving organizations greater control, making the infrastructure underneath AI more efficient, and holding ourselves accountable for our own impact." EDB noted that its platform is already in production. EDB Postgres AI runs live for approximately 1,700 customers worldwide, spanning financial services, telecommunications, manufacturing, retail, transportation, and the public sector. Organizations can estimate the potential energy, cost, and performance gains of the approach using EDB's public Efficiency Calculator. EDB Postgres(R) AI (EDB PG AI) is the sovereign data and AI platform for the agentic enterprise. Built on Postgres, the world's leading open source database, EDB PG AI unifies transactional, analytical, and AI workloads in a single architecture, eliminating the data movement, ETL, and operational fragmentation that slow enterprises down. With governance enforced at the data layer and the flexibility to deploy on-premises, in hybrid environments, or across clouds, enterprises operationalize their data and AI on infrastructure they own and control - reaching production-ready sovereign AI in weeks, not months. As one of the most active contributors to the PostgreSQL project, EDB is deeply invested in the vitality of the global open source community. To learn more, visit www.enterprisedb.com. EnterpriseDB and EDB are registered trademarks of EnterpriseDB Corporation. Postgres and PostgreSQL are registered trademarks of the PostgreSQL Community Association of Canada and used with their permission. All other trademarks are owned by their respective owners.

EDB brings postgres-proven Disaster Recovery to petabyte-scale WarehousePG where downtime is not possible. For banks and regulated enterprises running analytics around the clock, a warehouse recovery measured in days is a business risk. EDB Postgres(R) AI for WarehousePG brings it down to minutes and lets enterprises keep control over where that data runs. Date -2026-08-20 WILMINGTON, DE - AUGUST 20, 2026 - Today, EnterpriseDB (EDB), the leading sovereign data and AI company, announced the general availability of Disaster Recovery for WarehousePG, adding mission-critical capabilities to the analytical foundation of EDB Postgres AI (EDB PG AI). Customers now have a supported path to protect petabyte-scale data and restore warehouse operations across infrastructure boundaries. Enterprises can keep a recovery environment current wherever they choose - on-premises, in the cloud, or in a hybrid deployment - and decide when to bring it online. A warehouse outage is no longer a reporting problem. It is an operational event. AI agents, analysts, and business teams increasingly depend on live analytical data to make decisions and keep critical services moving. Yet at petabyte scale, a nightly backup can leave hours of data exposed, while a logical restore can take days and compete with production workloads along the way. For enterprises in regulated and mission-critical environments, that gap puts recovery objectives, customer commitments, and data sovereignty at risk. WarehousePG now extends EDB PG AI's converged analytics capabilities with a critical layer of resilience for enterprise-scale analytical workloads. Organizations can keep a separate analytical environment ready for recovery while retaining control over where the standby runs, how frequently restore points are created, and when the standby is promoted. "At petabyte scale, a recovery process that takes days is not a recovery plan, it is unhedged business risk. WarehousePG brings the full distributed cluster back to a consistent point in time without forcing customers to trade production performance for protection. That moves disaster recovery from a best-effort exercise to a capability teams can test, measure, and trust," said Max Romanenko, chief engineering officer, EDB. From two days to 16 minutes In one banking deployment supporting hundreds of terabytes of business-critical data and a 24x7 analytical pipeline, the existing logical backup and recovery process had outgrown the warehouse. Full backups took nearly two days, restores took just as long, and backup jobs ran through most of the day. With Disaster Recovery for WarehousePG, the customer established hourly restore points on cost-optimized recovery infrastructure, without changing the application architecture. * Approximately 180x faster recovery: Restore-point replay averaged 16 minutes, compared with a process that previously required nearly two days. * Four-times faster full backups: Full backup time fell from 44 hours to approximately 11 hours. * Nearly 99% less query lock time: Physical backup sharply reduced the lock contention imposed by the previous logical backup process. * No application redesign: The customer achieved the new recovery model without changes to the application architecture above the warehouse. Results reflect a specific customer deployment; actual outcomes vary by environment, workload, and configuration. Postgres-proven recovery, engineered for MPP scale Because WarehousePG runs on Postgres, disaster recovery is familiar to Postgres teams and is now applied across a full standby cluster for warehouse scale. The coordinator and every primary segment recover together to a single point in time, so the standby cluster comes up as one consistent warehouse. EDB has built and supported mission-critical Postgres disaster recovery for years. Disaster Recovery for WarehousePG brings that expertise to petabyte-scale analytics, using open source Barman and the Postgres write-ahead log (WAL) to coordinate recovery across the distributed cluster, giving enterprises a faster and supported path back to operations without adding a separate proprietary stack. WarehousePG's built-in high availability uses segment mirrors and a standby coordinator to protect against component failures within the cluster. Disaster Recovery for WarehousePG addresses the next level of risk - the loss of an entire site - by enabling the full cluster to be recovered in a separate environment. WarehousePG Enterprise Manager already gives operations teams a unified place to manage cluster health, query diagnostics, and resource utilization. With the impending September version update, Disaster Recovery configuration will be included as well, unlocking new value for customers: * Recovery aligned to business objectives: Teams can set restore-point frequency, continuously archive WAL, and validate replay performance against required recovery point and recovery time objectives. * Sovereign control through an outage: Organizations choose the region, jurisdiction, and infrastructure where the recovery environment runs, while retaining authority over when it is promoted. * A right-sized standby, without double-loading data: The recovery cluster can use a smaller footprint or cost-optimized cloud compute, avoiding the need to run every ingestion job twice into a duplicate production-scale warehouse. * Integrated operations and enterprise support: Disaster Recovery is delivered within the supported EDB PG AI for WarehousePG stack, reducing add-on complexity and giving teams access to EDB's Postgres and distributed warehouse expertise. Beyond Disaster Recovery, the same foundation gives enterprises a controlled path to migrate analytical workloads to the cloud and test peak-volume scenarios without disrupting production. Point-in-time restores can also offload intensive month- or quarter-end reporting, allowing teams to release the compute when the work is complete. Open, sovereign analytics for the agentic enterprise WarehousePG is an open source, Postgres-based MPP data warehouse designed for petabyte-scale analytics and high-concurrency workloads. EDB PG AI adds the capabilities required to operate that open foundation in production, including unified observability, built-in high availability, real-time ingestion, lakehouse interoperability, in-database AI and machine learning, vector search, and 24x7 enterprise support. It can run on-premises, in a private VPC, or across clouds, giving enterprises control over data residency, infrastructure, and long-term economics. Disaster Recovery adds another mission-critical control to that foundation: a supported path to protect analytical data and restore operations across infrastructure boundaries that also preserves the deployment flexibility and sovereignty that enterprises chose EDB PG AI to achieve. Availability Disaster Recovery for WarehousePG is generally available on EDB Postgres AI today. For more information, visit www.enterprisedb.com/products/warehousepg and read the announcement blog. EDB Postgres(R) AI (EDB PG AI) is the sovereign data and AI platform for the agentic enterprise. Built on Postgres, the world's leading open source database, EDB PG AI unifies transactional, analytical, and AI workloads in a single architecture, eliminating the data movement, ETL, and operational fragmentation that slow enterprises down. With governance enforced at the data layer and the flexibility to deploy on-premises, in hybrid environments, or across clouds, enterprises operationalize their data and AI on infrastructure they own and control, reaching production-ready sovereign AI in weeks, not months. As one of the most active contributors to the PostgreSQL project, EDB is deeply invested in the vitality of the global open source community. To learn more, visit www.enterprisedb.com. Media Contact Anwar Ahmad 347-206-5651 EnterpriseDB and EDB are registered trademarks of EnterpriseDB Corporation. Postgres and PostgreSQL are registered trademarks of the PostgreSQL Community Association of Canada and used with their permission. All other trademarks are owned by their respective owners.