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Relyance AI builds ‘x-ray vision’ for company data: Cuts AI compliance time by 80% while solving trust crisis

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Relyance AI, a data governance platform provider that secured $32.1 million in Series B funding last October, is launching a new solution aimed at solving one of the most pressing challenges in enterprise AI adoption: understanding […]

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Relyance AI, a data governance platform provider that secured $32.1 million in Series B funding last October, is launching a new solution aimed at solving one of the most pressing challenges in enterprise AI adoption: understanding exactly how data moves through complex systems.

The company’s new Data Journeys platform, announced today, addresses a critical blind spot for organizations implementing AI — tracking not just where data resides, but how and why it’s being used across applications, cloud services, and third-party systems.

“The fundamental premise is making sure that our customers have this AI native, context-aware view, very visual view of the entire journey of data across their applications, services, infrastructures, third parties,” said Abhi Sharma, CEO and co-founder of Relyance AI, in an exclusive interview with VentureBeat. “You can really get at the heart of the why of data processing, which is the most foundational layer needed for general AI governance.”

The launch comes at a pivotal moment for enterprise AI governance. As companies accelerate AI implementation, they face mounting pressure from regulators worldwide. More than a quarter of Fortune 500 companies have identified AI regulation as a risk in SEC filings, and GDPR-related fines reached €1.2 billion in 2024 alone (approximately $1.26 billion at current exchange rates).

How Data Journeys tracks information flow where others fall short

The platform represents a significant evolution from conventional data lineage approaches, which typically track data movement on a table-to-table or column-to-column basis within specific systems.

“The status quo for data lineage is basically table to table and column level lineage. I can see how data moved within my Snowflake instance or within my S3 buckets,” Sharma explained. “But nobody can answer: Where did it come from originally? What nuanced transformation happened between data pipelines, third-party vendors, API calls, RAG architectures, to finally land up here?”

Data Journeys aims to provide this comprehensive view, showing the complete data lifecycle from original collection through every transformation and use case. The system starts with code analysis rather than simply connecting to data repositories, giving it context about why data is being processed in specific ways.

Lawrence Schoeb, senior director and DPO at Samsara, one of Relyance’s customers, said in a statement, “The automated, context-aware data lineage capabilities would address our most pressing challenges. It represents exactly what we’ve been looking for to support our global AI governance framework.”

Four business problems that data visibility promises to solve

According to Sharma, Data Journeys delivers value in four critical areas:

First, compliance and risk management: “Today, you kind of are required to vouch for integrity of data processing, but you can’t see inside. It’s basically blind governance,” Sharma said. The platform enables organizations to prove the integrity of their data practices when facing regulatory scrutiny.

Second, precise bias detection: Rather than just examining the immediate dataset used to train a model, companies can trace potential bias to its source. “Bias often happens at inference time, not because you had bias in the dataset,” Sharma noted. “The point is, it’s actually not that dataset. It’s the journey it took.”

Third, explainability and accountability: For high-stakes AI decisions like loan approvals or medical diagnoses, understanding the complete data provenance becomes essential. “The why behind that is super important, and many times, the incorrect behavior of the model is completely dependent on the multiple steps it took before the inference time,” Sharma explained.

Finally, regulatory compliance: The platform provides what Sharma calls a “mathematical proof point” that companies are using data appropriately, helping them navigate increasingly complex global regulations.

From hours to minutes: Measurable returns on better data oversight

Relyance claims the platform delivers measurable returns on investment. According to Sharma, customers have seen 70-80% time savings in compliance documentation and evidence gathering. What he calls “time to certainty”—the ability to quickly answer questions about how specific data is being used—has been reduced from hours to minutes.

In one example Sharma shared, a direct-to-consumer company was switching payment processors from Braintree to Stripe. An engineer working on the project inadvertently created code that stored credit card information in plain text under the wrong column name in Snowflake.

“We caught that at the time the code was checked in,” Sharma said. Without Data Journeys’ visual representation of data flows, this potential security incident might have gone undetected until much later.

Keeping sensitive data inside your walls: The self-hosted option

Alongside Data Journeys, Relyance is introducing InHost, a self-hosted deployment model designed for organizations with strict data sovereignty requirements or those in highly regulated industries.

“The industries that are most interested in the in-host option are more regulated industries — FinTech and healthcare,” said Sharma. This includes banking, fraud detection, credit worthiness applications, genetics, and personal healthcare services.

The flexibility to deploy either in the cloud or within a company’s own infrastructure addresses growing concerns about sensitive data leaving organizational boundaries, particularly for AI applications that might process regulated information.

Relyance AI’s expansion plans point to growing AI governance market

Relyance is positioning Data Journeys as part of a broader strategy to become what Sharma calls “a unified AI-native platform” for global privacy compliance, data security posture management, and AI governance.

“In the second half of this year, I’m launching an AI governance solution which will be a 360-degree management of all AI footprint in your environment,” Sharma revealed, encompassing compliance, real-time ethics monitoring, bias detection, and accountability for both third-party and in-house AI systems.

The company’s long-term vision is ambitious. “AI agents are going to run the world, and we want to be that company that provides the infrastructure for organizations to trust and govern it,” Sharma said. “We want to help improve the data utility index of the world.”

Investors bet big on data governance as competition heats up

Relyance faces competition from established players in adjacent spaces. In an earlier interview with TechCrunch, Sharma acknowledged competitors including OneTrust, Transcend, DataGrail, and Securiti AI, though he emphasized that Relyance’s integrated approach sets it apart.

Investors seem convinced of the company’s potential. Its $32.1 million Series B round in October 2024, led by Thomvest Ventures with participation from Microsoft’s M12 Ventures Fund, brought Relyance’s total funding to $59 million.

Umesh Padval, Managing Director at Thomvest Ventures, highlighted the urgency of the problem Relyance is solving: “Relyance AI empowers Chief Privacy, Security, and Information Officers to manage data privacy and compliance, avoiding costly penalties while driving safe and responsible AI adoption.”

Why data oversight might determine AI success in the enterprise

Sharma framed the company’s mission as part of a broader imperative for organizations implementing AI technologies.

“AI is becoming kind of the default imperative in your organization, and everybody needs to think about that core, foundational pillar in your organization, which is going to be the infrastructure for trust and governance,” he said.

“Whether leaders use Relyance or not, it is an important aspect to think about, because that will really unlock how fast you can get AI adoption in a meaningful way within an organization.”

As enterprises rush to implement AI, the ability to maintain visibility into data processes has evolved from a mere compliance checkbox to a fundamental business necessity. This shift represents one of those quiet but profound changes that doesn’t make headlines but reshapes industries. Companies building these visibility tools are essentially creating the air traffic control systems for AI—not the flashy jets themselves, but the infrastructure that prevents them from crashing into each other. Without it, even the most impressive algorithms become corporate liabilities.

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JF Expands in Southwest with Maverick Acquisition

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AI could cut disaster infrastructure losses by 15%, new research finds

Dive Brief: AI applications such as predictive maintenance and digital twins could prevent 15% of projected natural disaster losses to power grids, water systems and transportation infrastructure, amounting to $70 billion in savings worldwide by 2050, according to a recently released Deloitte Center for Sustainable Progress report. Governments and other stakeholders need to overcome technological limitations, financial constraints, regulatory uncertainty, data availability and security concerns before AI-enabled resilience can be widely adopted for infrastructure systems, according to the report. “Investing in AI can help deliver less frequent or shorter power outages, faster system recovery after storms, or fewer damaged or non-usable roads and bridges,” Jennifer Steinmann, Deloitte Global Sustainability Business leader, said in an email. Dive Insight: Natural disasters have caused nearly $200 billion in average annual losses to infrastructure around the world over the past 15 years, according to Deloitte. The report projects that could increase to approximately $460 billion by 2050. Climate change is expected to increase the frequency and intensity of these events, leading to higher losses, according to the report.   “Investing in AI has the greatest near-term potential to help reduce damages from storms, which include tropical cyclones, tornados, thunderstorms, hailstorms, and blizzards,” Steinmann said. “These natural disasters drive the largest share of infrastructure losses, due to their high frequency, wide geographic reach, and increasing intensity.” The AI for Infrastructure Resilience report uses empirical case studies, probabilistic risk modeling and economic forecasting to show how AI can help leaders fortify infrastructure so they can plan, respond and recover more quickly from natural disasters. “AI technologies can offer preventative, detective and responsive solutions to help address natural disasters — but some interventions are more impactful than others,” Steinmann said. Investing in AI while infrastructure is in planning stages accounts for roughly two-thirds of AI’s potential to prevent

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Lawmaker, AARP call for nationwide utility commission reforms to stop rising electric bills

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Crude Steadies After Volatile Session

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SLB, AIQ Join Forces to Boost ADNOC’s Energy Efficiency with Agentic AI

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Diamondback Energy Narrows Production Guidance as Net Income Dips in Q2

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Blackstone to Buy Enverus

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Stargate’s slow start reveals the real bottlenecks in scaling AI infrastructure

The CFO emphasized that SoftBank remains committed to its original target of $346 billion (JPY 500 billion) over 4 years for the Stargate project, noting that major sites have been selected in the US and preparations are taking place simultaneously across multiple fronts. Requests for comment to Stargate partners Nvidia, OpenAI, and Oracle remain unanswered. Infrastructure reality check for CIOs These challenges offer important lessons for enterprise IT leaders facing similar AI infrastructure decisions. Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research, said that Goto’s confirmation of delays “reflects a challenge CIOs see repeatedly” in partner onboarding delays, service activation slips, and revised delivery commitments from cloud and datacenter providers. Oishi Mazumder, senior analyst at Everest Group, noted that “SoftBank’s Stargate delays show that AI infrastructure is not constrained by compute or capital, but by land, energy, and stakeholder alignment.” The analyst emphasized that CIOs must treat AI infrastructure “as a cross-functional transformation, not an IT upgrade, demanding long-term, ecosystem-wide planning.” “Scaling AI infrastructure depends less on the technical readiness of servers or GPUs and more on the orchestration of distributed stakeholders — utilities, regulators, construction partners, hardware suppliers, and service providers — each with their own cadence and constraints,” Gogia said.

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Incentivizing the Digital Future: Inside America’s Race to Attract Data Centers

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AI Supercharges Hyperscale: Capacity, Geography, and Design Are Being Redrawn

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In crowded observability market, Gartner calls out AI capabilities, cost optimization, DevOps integration

Support for OpenTelemetry and open standards is another differentiator for Gartner. Vendors that embrace these frameworks are better positioned to offer extensibility, avoid vendor lock-in, and enable broader ecosystem integration. This openness is paired with a growing focus on cost optimization—an increasingly important concern as telemetry data volumes increase. Leaders offer granular data retention controls, tiered storage, and usage-based pricing models to help customers Gartner also highlights the importance of the developer experience and DevOps integration. Observability leaders provide “integration with other operations, service management, and software development technologies, such as IT service management (ITSM), configuration management databases (CMDB), event and incident response management, orchestration and automation, and DevOps tools.” On the automation front, observability platforms should support initiating changes to application and infrastructure code to optimize cost, capacity or performance—or to take corrective action to mitigate failures, Gartner says. Leaders must also include application security functionality to identify known vulnerabilities and block attempts to exploit them. Gartner identifies observability leaders This year’s report highlights eight vendors in the leaders category, all of which have demonstrated strong product capabilities, solid technology execution, and innovative strategic vision. Read on to learn what Gartner thinks makes these eight vendors (listed in alphabetical order) stand out as leaders in observability: Chronosphere: Strengths include cost optimization capabilities with its control plane that closely manages the ingestion, storage, and retention of incoming telemetry using granular policy controls. The platform requires no agents and relies largely on open protocols such as OpenTelemetry and Prometheus. Gartner cautions that Chronosphere has not emphasized AI capabilities in its observability platform and currently offers digital experience monitoring via partnerships. Datadog: Strengths include extensive capabilities for managing service-level objectives across data types and providing deep visibility into system and application behavior without the need for instrumentation. Gartner notes the vendor’s licensing

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LiquidStack CEO Joe Capes on GigaModular, Direct-to-Chip Cooling, and AI’s Thermal Future

In this episode of the Data Center Frontier Show, Editor-in-Chief Matt Vincent speaks with LiquidStack CEO Joe Capes about the company’s breakthrough GigaModular platform — the industry’s first scalable, modular Coolant Distribution Unit (CDU) purpose-built for direct-to-chip liquid cooling. With rack densities accelerating beyond 120 kW and headed toward 600 kW, LiquidStack is targeting the real-world requirements of AI data centers while streamlining complexity and future-proofing thermal design. “AI will keep pushing thermal output to new extremes,” Capes tells DCF. “Data centers need cooling systems that can be easily deployed, managed, and scaled to match heat rejection demands as they rise.” LiquidStack’s new GigaModular CDU, unveiled at the 2025 Datacloud Global Congress in Cannes, delivers up to 10 MW of scalable cooling capacity. It’s designed to support single-phase direct-to-chip liquid cooling — a shift from the company’s earlier two-phase immersion roots — via a skidded modular design with a pay-as-you-grow approach. The platform’s flexibility enables deployments at N, N+1, or N+2 resiliency. “We designed it to be the only CDU our customers will ever need,” Capes says. From Immersion to Direct-to-Chip LiquidStack first built its reputation on two-phase immersion cooling, which Joe Capes describes as “the highest performing, most sustainable cooling technology on Earth.” But with the launch of GigaModular, the company is now expanding into high-density, direct-to-chip cooling, helping hyperscale and colocation providers upgrade their thermal strategies without overhauling entire facilities. “What we’re trying to do with GigaModular is simplify the deployment of liquid cooling at scale — especially for direct-to-chip,” Capes explains. “It’s not just about immersion anymore. The flexibility to support future AI workloads and grow from 2.5 MW to 10 MW of capacity in a modular way is absolutely critical.” GigaModular’s components — including IE5 pump modules, dual BPHx heat exchangers, and intelligent control systems —

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Oracle’s Global AI Infrastructure Strategy Takes Shape with Bloom Energy and Digital Realty

Bloom Energy: A Leading Force in On-Site Power As of mid‑2025, Bloom Energy has deployed over 400 MW of capacity at data centers worldwide, working with partners including Equinix, American Electric Power (AEP), and Quanta Computing. In total, Bloom has delivered more than 1.5 GW of power across 1,200+ global installations, a tripling of its customer base in recent years. Several key partnerships have driven this rapid adoption. A decade-long collaboration with Equinix, for instance, began with a 1 MW pilot in 2015 and has since expanded to more than 100 MW deployed across 19 IBX data centers in six U.S. states, providing supplemental power at scale. Even public utilities are leaning in: in late 2024, AEP signed a deal to procure up to 1 GW of Bloom’s solid oxide fuel cell (SOFC) systems for fast-track deployments aimed at large data centers and commercial users facing grid connection delays. More recently, on July 24, 2025, Bloom and Oracle Cloud Infrastructure (OCI) announced a strategic partnership to deploy SOFC systems at select U.S. Oracle data centers. The deployments are designed to support OCI’s gigawatt-scale AI infrastructure, delivering clean, uninterrupted electricity for high-density compute workloads. Bloom has committed to providing sufficient on-site power to fully support an entire data center within 90 days of contract signing. With scalable, modular, and low-emissions energy solutions, Bloom Energy has emerged as a key enabler of next-generation data center growth. Through its strategic partnerships with Oracle, Equinix, and AEP, and backed by a rapidly expanding global footprint, Bloom is well-positioned to meet the escalating demand for multi-gigawatt on-site generation as the AI era accelerates. Oracle and Digital Realty: Accelerating the AI Stack Oracle, which continues to trail hyperscale cloud providers like Google, AWS, and Microsoft in overall market share, is clearly betting big on AI to drive its next phase of infrastructure growth.

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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