“AI agents are evolving from simple chatbots into autonomous digital workers,” said Steve Wilson, Chief AI and Product Officer at Exabeam. “They authenticate, access systems, and execute real business processes. When compromised, their activity will often look legitimate. Guardrails designed to catch prompt injection or hallucinations do not address that risk. Securing digital workers requires deep visibility into baseline behavior and the ability to detect subtle deviations before they become material incidents.”

The platform introduces five core capabilities to secure AI-driven environments. AI Behavior Baselining dynamically profiles users and agents, flagging anomalies in token usage, request volumes, tool invocations, and web sessions. Prompt and Model Abuse Detection identifies injection attacks, model manipulation, and shadow AI activity at the point of entry. Identity and Privilege Monitoring governs agent roles and permissions, while Agent Lifecycle Monitoring tracks creation, modification, and usage to close visibility gaps. Coverage aligned to the OWASP Top 10 for Agentic AI establishes a framework for defending AI agents against emerging threats.

Also Read: Scamnetic and VanishID Join Forces to Strengthen Enterprise Identity Protection and Scam Prevention

“AI is rapidly reshaping how organizations operate, compete and grow, creating a new, digital workforce that helps them move faster and at scale,” said Pete Harteveld, CEO at Exabeam. “Our expansion of Agent Behavior Analytics helps organizations stay protected from emerging risks while adopting AI with confidence and maintaining the oversight and accountability required to proliferate these capabilities across an enterprise.”

Early adopters, such as Dayforce, report that Exabeam provides critical clarity in monitoring both human and autonomous interactions, allowing security teams to prioritize real threats while supporting accelerated AI adoption. The expansion is complemented by enhancements across Exabeam’s New-Scale and LogRhythm platforms, streamlining workflows, reducing alert fatigue, and improving automated threat detection across the enterprise.

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As enterprises increasingly rely on data products and AI-powered applications in daily operations, administrators are playing a more strategic role. Beyond managing permissions, they are now responsible for ensuring that users receive the right data in the right context.

“The hardest part of managing data at scale isn’t access. It’s making sure people see the right thing, in the right way, at the right time,” said Ben Schein, Chief Analytics Officer and SVP of Product at Domo. “That depends on governance being built into how data is delivered, not layered on afterward. Administrators are responsible for delivering data that is secure and tailored to how people actually work, and these updates give them the tools to do that with more precision and less friction.”

Also Read: Domo Bridges the Gap Between Spreadsheets and AI: A New Era for Data Exploration

One of the most important new features is User Impersonation, which was a highly requested feature that allows the administrator to view the platform as another user sees it. This allows for a much more streamlined experience in debugging the platform by quickly identifying the problem without having to go back and forth.

Domo has also taken the experience of consuming the data outside of the platform with the new Native App Distribution feature. This allows the organization to distribute custom applications to the end user, giving them the ability to consume the relevant information without having to go through the analytics environment.

To address the governance challenges that come with a complex data environment, Domo has also rolled out the new Global PDP Policies, as well as new column masking capabilities.

These new features, including Nav Configs and new Workspaces, continue the evolution of the administrator’s role in the Domo experience, moving the administrator from a “gatekeeper” role to a more “orchestrator” role, allowing the organization to scale the experience without compromising control, consistency, and usability in the increasingly AI-driven world.

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The News: Bringing Familiarity to Frontier Tech

At the core of this announcement is the introduction of a spreadsheet-native interface within Domo’s platform. While Business Intelligence (BI) tools have traditionally forced users into rigid dashboards, Domo is now allowing users to interact with live, governed data using the familiar cell-and-formula logic of a spreadsheet.

Coupled with this is a new “Semantic Layer.” This layer acts as a universal translator, ensuring that when an AI agent or a human user asks for “revenue,” the system pulls the exact same filtered metric every time, regardless of the department. By combining this governed layer with an agent-building platform, Domo is enabling companies to build custom AI agents that can “chat” with data, perform complex calculations, and automate reporting—all while staying within the guardrails of corporate security.

Also Read: Oracle Introduces Fusion Agentic Applications to Redefine Enterprise AI Execution

Impact on the IT Industry

For the IT sector, this marks a shift away from “Shadow IT.” Historically, when IT departments provided tools that were too complex, business users would export data into insecure, offline Excel files to do their own analysis. Domo’s new features effectively bring those users back into the governed fold.

Data architects will see a reduction in the “ticket backlog.” Instead of requesting a new dashboard for every minor pivot in data, business users can now explore data themselves in a spreadsheet-like environment that remains synced to the master data source. Furthermore, the integration of a semantic layer into agent-building simplifies the deployment of Large Language Models (LLMs). It reduces the risk of AI “hallucinations” because the AI is restricted to the definitions and logic defined in the semantic layer, making AI deployment faster and safer for IT teams.

Broader Effects on Business Operations

The implications for general business operations are profound. We are on the threshold of the era of “Self-Service AI,” where the threshold for sophisticated data analysis has effectively been erased.

Accelerated Decision-Making: With the ability for AI agents to explore data through the semantic layer, executives can get instant answers to complex questions such as, “How has the shipping delay in Asia affected our Q3 margins?”

Increased Data Literacy: With the spreadsheet-native interface, Domo takes advantage of the skills base of the millions of workers. Companies can improve the skills base of their employees to interact with AI and big data without the need for them to know SQL or complex coding languages.

Operational Consistency: The use of the semantic layer provides the concept of “Single Source of Truth.” When the sales team in London and the finance team in New York use an AI agent to access the data on “profitability,” they are no longer looking at conflicting data.

Domo’s latest release is a pivot towards the concept of pragmatic AI, where the tools are not only useful but also integrate well with the way people work. For businesses, it means more time for decision-making and less time for wrestling with data.

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Reveal360 Hub offers role-based workspaces for key stakeholders like CISOs, threat analysts, and compliance leaders. Each role can access insights that matter to them. The platform gathers data from different sources. These include endpoint systems, SIEM tools, identity platforms, and threat intelligence feeds. This creates a unified view of the security posture.

A key feature of the platform is its defense coverage analysis. This maps active threat actors, campaigns, and attack techniques to an organization’s detection and response abilities. Security teams can spot coverage gaps and prioritize remediation efforts better. Reveal360 Hub turns fragmented data into useful insights. This helps organizations make faster strategic and operational decisions. This also boosts overall SOC performance.

The platform is available as a standalone solution on big cloud marketplaces like Microsoft Azure and Google Cloud. It can also be integrated into CyberProof’s managed security services.

Implications for the IT Industry

The launch of Reveal360 Hub is revealing a major shift in the IT industry toward integrated, result-oriented cybersecurity platforms. In the past, security operations have frequently been a patchwork of disconnected tools which results in poor visibility and inefficiencies in both detecting and responding to threats. Given the rising complexity of cyber threats, particularly those enhanced by AI, IT departments are expected to evolve from reliance on reactive security models to adopting proactive, intelligence-based ones. Solutions such as Reveal360 are indicative of the evolution of security observability by which enterprises obtain an all-encompassing, real-time view of their security exposures.

Besides, the emphasis on role-based insights also goes to show how cross-functional collaboration in cybersecurity is gaining even more significance. Actually, security is no longer just the IT teams’ concern only; it extends to executives, compliance officers, and business leaders who requiring plain and dependable information for making decisions.

The incorporation of AI-powered analytics and a single data model is yet another reason that highlights the industry’s transition towards cloud-native and AI-driven security systems which are scalable and sufficiently automated to cope with the ever-changing threat landscape.

Broader Business Impact

Through Reveal360 Hub, businesses can completely transform risk management, operational efficiency, and strategic decision-making. After integrating all security data into one platform, organizations get to cut down their efforts in correlating information across multiple tools and primarily focus on solving critical threats.

The increase in cyber risk metrics visibility equips business executives with better understanding of how security incidents might affect the financial and operational performance of their businesses. Consequently, this enables them to make better investment decisions and align cybersecurity programs with the overall objectives of the business.

Besides that, Reveal360 Hub also improves incident response proficiency, giving organizations an edge over threat detection and remediation. The speed of response can have an enormous effect on how long businesses are down, the exposure of sensitive data, and the loss of revenue after cyberattacks.

In addition, Reveal360 Hub reveals shortcomings in the defense line allowing organizations to make smart use of their resources which in turn, guarantees that the value of security investment is at peak level. This stands to be even more compelling in a scenario where cyber threats are changing rapidly and pieces have to be placed on the chessboard of budget optimization.

The Future of Security Intelligence

CyberProof‘s Reveal360 Hub highlights a significant trend that is shaping the future of IT – the merging of security operations, data analytics and business intelligence into a single framework. With the growing complexity and interconnection of cyber-threats, organizations will be looking for platforms that can not only detect risks but also convert them into understandable business insights.

Tools that connect technical security data with higher management’s decision-making will be instrumental for the creation of resilient, AI-ready enterprises. The takeaway for IT leaders and companies is straightforward – cybersecurity today is as much about visibility, context, and business impacts as it is about protection.

The post CyberProof Launches Reveal360 Hub to Bridge Security Insights and Business Outcomes appeared first on ITDigest.

Also Read: Noibu Expands into Unified Ecommerce Analytics and Monitoring Platform

“We built Kubit to help organizations move faster with analytics they can trust,” said Alex Li, Founder and CEO, Kubit. “By running directly in the Snowflake AI Data Cloud, we deliver warehouse-native digital analytics and explainable AI on governed data.” The platform adds AI agents to create and run SQL queries in Snowflake. This helps with anomaly detection, root cause analysis, and natural language reporting. It also ensures governance and auditability. This integration helps businesses grow their AI-driven analytics. It offers better transparency, consistency, and efficiency.

Read More: Kubit Integrates with Snowflake to Deliver Warehouse-Native Product Analytics in the Snowflake AI Data Cloud

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As Cloudflare adoption continues to grow across enterprises, many organizations struggle to maintain full visibility across their zones, DNS configurations, security rules, and performance settings. Industry estimates show that while approximately 38% of companies use Cloudflare, many organizations only utilize 40–60% of the platform’s capabilities, leaving significant security and performance improvements unrealized.

CloudSight addresses this challenge by delivering a centralized platform that continuously audits Cloudflare environments, identifies configuration gaps, and provides actionable recommendations for optimization.

“Cloudflare is one of the most powerful security and performance platforms available today,” said Jason Caparoso, CTO and Co-Founder of BlackHawk Data. “But like any powerful platform, it requires visibility and ongoing optimization to deliver its full value. CloudSight was built to give organizations continuous insight into their Cloudflare environment so they can stay secure, performant, and fully optimized.”

Also Read: TabPFN AI Speeds Business Transformation with Databricks

CloudSight analyzes Cloudflare deployments across multiple dimensions, including security posture, DNS configuration, traffic behavior, and developer platform usage. The platform automatically identifies misconfigurations, configuration drift, and underutilized features that could impact security or performance.

Key capabilities of CloudSight include:

• Continuous Security Auditing – Review WAF rules, bot management effectiveness, Zero Trust posture, SSL/TLS configuration, and potential attack exposure.
• DNS & Asset Intelligence – Analyze DNS records, validate DNSSEC and CAA records, and identify exposed or misconfigured assets.
• Traffic & Performance Insights – Monitor real-time traffic patterns, detect anomalies, and identify optimization opportunities for caching, compression, and delivery performance.
• Configuration Drift Detection – Automatically detect changes across zones and configurations to ensure environments remain aligned with best practices.
• Security Scoring & Reporting – Generate a clear A+–F security score along with executive-ready audit reports for stakeholders and compliance documentation.

CloudSight provides organizations with a single pane of glass for managing their Cloudflare deployment, enabling security, DevOps, and infrastructure teams to monitor zones, services, and configurations across their entire environment.

“Many teams rely on Cloudflare for critical security and performance services, but they don’t always have an easy way to continuously audit how it’s configured,” added Caparoso. “CloudSight turns Cloudflare from a powerful tool into a continuously optimized platform.”

The platform includes more than 21 automated security and configuration checks across six audit categories and operates through a continuous monitoring engine designed to detect risks before they can be exploited.

Source: PRNewswire

The post BlackHawk Data Launches CloudSight, a Continuous Auditing Platform for Cloudflare Environments appeared first on ITDigest.

Also Read: MindBridge & Genpact Unite to Advance AI-Driven Audit Analytics and Risk Consulting

The new platform links technical insights to real-user behavior and dollar value impact, enabling teams to not only understand what is happening but also understand how it is affecting conversion and revenue. The platform is organized into three main pillars: protect revenue and reduce risk, unlock conversion growth, and unite teams with a single console. “The honest answer for why we’ve evolved is that our customers changed the game for us,” said Kailin Noivo, President & Co-founder of Noibu. “We watched our most successful users stop treating Noibu as a reactive debugging tool and start treating it as their daily ecommerce command center.”

Read More: Noibu Evolves Beyond Error Monitoring to Launch Unified Ecommerce Analytics & Monitoring Platform

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A New Approach to AI for Structured Data

Most enterprise data exists in tabular form spreadsheets, databases, financial records, customer information, and operational metrics. Despite this, applying advanced machine learning to structured data has historically required extensive model training, feature engineering, and expert data science resources.

TabPFN addresses these challenges by using a pre-trained foundation model specifically designed for tabular data. Instead of requiring organizations to repeatedly train models on new datasets, the system is trained in advance using millions of synthetic datasets. As a result, it can analyze new structured datasets almost instantly and generate accurate predictions with minimal configuration.

When deployed within the Databricks platform, TabPFN enables companies to perform tasks such as forecasting demand, detecting fraud, optimizing operations, or predicting customer behavior with significantly reduced development time. The integration also ensures that predictions can be executed within enterprise data pipelines, maintaining governance, scalability, and security.

Accelerating AI Adoption in Data Analytics

The integration of TabPFN into the Databricks ecosystem reflects a broader industry shift toward “AI-ready data platforms.” Databricks itself has become one of the fastest-growing companies in the data analytics sector, providing a unified lakehouse architecture that combines data warehousing, analytics, and machine learning on a single platform.

By embedding pre-trained AI capabilities directly into data infrastructure, organizations can eliminate many of the technical barriers that previously slowed AI adoption. Instead of spending weeks or months designing models, teams can quickly run predictive analysis on operational datasets.

For data analysts and engineers, this represents a significant evolution in workflow. Tasks that once required specialized machine learning expertise can increasingly be performed through automated or semi-automated systems. This democratization of AI may allow business analysts, data engineers, and operational teams to build predictive solutions without relying heavily on dedicated data science teams.

Also Read: Microsoft Introduces Zero-Copy Access to OneLake Data in Azure Databricks

Implications for the Data Analytics Industry

The introduction of foundation models for tabular data has the potential to reshape the broader data analytics industry in several ways.

First, it could significantly reduce the time required to deploy predictive analytics solutions. Traditional machine learning pipelines often involve multiple stages data preparation, feature engineering, model training, hyperparameter tuning, and evaluation. Pre-trained tabular models shorten this process dramatically, enabling faster experimentation and deployment.

Second, the innovation may increase competition among analytics platform providers. Vendors that offer integrated AI capabilities will likely gain an advantage as organizations seek unified platforms that combine data engineering, analytics, and AI. Companies competing with Databricks in the analytics ecosystem such as cloud data warehouses and business intelligence vendors may need to expand their AI functionality to remain competitive.

Third, the rise of tabular foundation models could change the role of AutoML tools. While AutoML platforms automate model selection and tuning, TabPFN-like approaches aim to eliminate these steps entirely by applying a pre-trained model that works effectively across many datasets.

Business Impact Across Industries

For enterprises, the real value of this development lies in faster data-driven decision-making. Organizations across sectors from financial services to retail and healthcare depend heavily on tabular datasets for forecasting, risk assessment, and operational optimization.

With tools like TabPFN integrated into enterprise data platforms, companies can run predictive analyses more frequently and at larger scales. This can lead to more responsive supply chains, better customer targeting, and improved operational efficiency.

For example, financial institutions could rapidly test risk models using transactional data, while retailers could forecast demand patterns across thousands of products without building new models for each dataset. Manufacturing companies may also use such capabilities to predict equipment failures or optimize production workflows.

The Future of AI-Driven Data Platforms

The introduction of TabPFN on the Databricks platform highlights a growing trend toward foundation models designed for specific data modalities text, images, code, and now structured data. These models aim to make advanced AI capabilities accessible directly within business workflows.

As organizations continue to accumulate vast amounts of structured data, tools that simplify predictive analytics will become increasingly valuable. The convergence of pre-trained AI models and unified data platforms may ultimately transform how enterprises approach analytics, turning predictive intelligence into a standard capability rather than a specialized project.

In this context, Databricks’ integration of TabPFN represents more than a technical upgrade it signals a broader shift toward faster, more accessible, and scalable data analytics across the global business landscape.

The post TabPFN AI Speeds Business Transformation with Databricks appeared first on ITDigest.

The new feature is aimed at making it easier for organizations to access and share data without having to create multiple pipelines or duplicate data sets. The new feature will allow Databricks workloads to access OneLake data without having to go through multiple processes.

What the New Capability Enables

In the traditional data infrastructure, to provide access to different tools or platforms, teams tend to create multiple copies of the data. This increases storage costs, synchronization, and governance. With the new feature in preview, Azure Databricks is able to query or analyze data that already exists in OneLake without having to move or duplicate the data.

This enables multiple analytics teams and tools to work with the same set of curated data products in OneLake without having to set up parallel pipelines or storage layers.

OneLake is a single logical data lake for the entire organization. It brings analytics data into a single centralized environment that can be accessed by different engines and services.

With this integration of Azure Databricks and OneLake, Microsoft is positioning its ecosystem as a more interoperable analytics platform, enabling organizations to harness the power of Databricks’ advanced data engineering and AI capabilities together with Fabric’s unified analytics environment.

Why This Matters for the Analytics Industry

The move marks an increasing trend in the industry towards “zero-copy” and “zero-ETL” data models. In current enterprise environments, analytics platforms are often dependent on ETL (Extract, Transform, Load) processes to transfer data between systems. While these processes add complexity and latency to operations, they also cause inconsistencies in data between tools.

The zero-copy data model disrupts this approach by allowing analytics tools to directly access and share data storage layers. Rather than transferring data between systems, tools can access the same data while preserving governance and security processes.

This approach is already underway in the cloud data ecosystem. Solutions like Delta Lake, open table formats, and data virtualization platforms are making it possible for analytics tools to collaborate without having to constantly transfer data.

Microsoft’s new preview feature enhances this approach by enabling Databricks, a popular platform for large-scale data engineering and machine learning, to seamlessly integrate with the OneLake system.

Also Read: AWS Unveils Glue 5.1 – What It Means for Data Integration and Analytics

Impact on Enterprise Data Teams

For enterprises operating in the analytics and data engineering space, the integration offers several strategic advantages:

1. Reduced Data Duplication

Data teams often replicate datasets across multiple warehouses or analytics environments. Zero-copy access allows organizations to maintain a single source of truth, reducing storage overhead and eliminating version inconsistencies.

2. Faster Analytics Workflows

Without the need to build additional pipelines or data synchronization processes, analysts and data scientists can access datasets faster. This can accelerate experimentation, model training, and reporting.

3. Simplified Data Architecture

Complex data stacks often consisting of multiple lakes, warehouses, and ETL tools can be simplified. By centralizing data in OneLake and enabling cross-platform access, organizations can reduce infrastructure complexity.

4. Improved Data Governance

When data is duplicated across multiple environments, enforcing governance policies becomes challenging. With a single shared dataset, organizations can apply consistent security policies, access controls, and compliance frameworks.

Business Implications Across the Analytics Ecosystem

The general implications of this emerging trend go beyond the convenience of technology. For companies that are highly dependent on analytics, the ability to have unified data access can greatly impact the efficiency of business operations and decision-making.

For companies in sectors such as finance, retail, healthcare, and manufacturing, real-time data insights have become essential in informing business decisions. By making it easier to access data and analytics platforms, companies can make decisions faster.

In addition, companies that implement AI and machine learning processes can also benefit from the ability to have unified data access. Data scientists who work with Databricks to build machine learning models can easily access enterprise data stored in OneLake without having to duplicate data into other environments.

In terms of cost savings, companies can also benefit from reduced infrastructure costs due to reduced data duplication and pipeline maintenance.

The Competitive Landscape

The announcement also reflects Microsoft’s approach to more aggressively compete in the new data platform space. Cloud analytics vendors are also focusing on open data models and interoperability.

Microsoft’s goal of more closely integrating Fabric, OneLake, and Databricks is to build an ecosystem where analytics, data engineering, and AI can run on multiple platforms seamlessly. This will make Microsoft’s stack a single ecosystem for enterprise analytics, which could compete with other lakehouse and data platform offerings.

Looking Ahead

The preview release of zero, copy access between OneLake and Azure Databricks is giving another spin to open, integrated analytics ecosystems. As companies keep growing data operations and embedding AI at the center of decision, making, they will need to have a very good data interoperability

In case that feature is massively used by the market, it could be the fastest way to go to simplified, shared, data architectures, where analytics tools rely less on data movement and more on getting insights. For the analytics industry, the change implies a time when platform borders are no more and the data is really accessible throughout the enterprise.

The post Microsoft Introduces Zero-Copy Access to OneLake Data in Azure Databricks appeared first on ITDigest.

Also Read: Xero Empowers Small Businesses with Enterprise-Grade AI Analytics 

“As a market leader in bringing AI-driven transformation to finance and operations, Genpact is the ideal partner to help scale the impact of MindBridge’s AI across global enterprises,” said Stephen DeWitt, CEO of MindBridge. “By joining forces, we can bring more powerful transformation to finance and accounting organizations that are looking to deepen their risk insight, enhance assurance, and unlock greater value from their financial data.” “Partnering with MindBridge allows us to embed its AI-driven financial intelligence into our client engagements,” added Vivek Saxena, SVP, Finance & Risk service line leader at Genpact, highlighting how the combined expertise enhances capabilities such as full population risk scoring, data-driven exception testing, AI-led internal audits, and continuous controls monitoring, reinforcing both firms’ commitment to transform audit analytics and risk consulting for enterprises navigating increasingly complex financial and compliance landscapes.

Read More: MindBridge Announces Global Partnership with Genpact to Elevate Audit Analytics and Risk Consulting

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