“These innovations mark a step forward for the industry,” said Art Tyszka, VP & Segment Leader, Lien Solutions, Financial & Corporate Compliance at Wolters Kluwer. “By embedding Expert AI directly into FCC products, we are delivering first in market capabilities that help customers move faster, improve accuracy, and modernize operations without sacrificing governance or control.”

The iLien Search Insights Report leverages the power of AI to turn complicated public documents into valuable information. Rather than having to manually piece together filings and lien history, the user gets a comprehensive overview of borrower and collateral risks. Not only does this save time but it also streamlines the decision-making process, allowing for consistency across the board without the lengthy research phase. To dig further into the data, the report seamlessly links with current Borrower Analytics functions for enhanced results and optional human analysis.

Also Read: Aptean Brings AI to On-Premises Business Central Users with New AppCentral Platform

Supporting this is the iLien Filing Assistant, which brings the power of AI to the UCC filing process. From loan and security documents, the system pulls all necessary data such as debtor information, collateral information, and jurisdiction and generates the filings automatically. It also alerts users of missing or ambiguous data and suggests jurisdiction.

These capabilities, when combined, gradually transform traditional workflows into automated ones, yet human oversight is kept. By streamlining and error-proofing their practices, organizations can expand the scale of their operations without the need for extra staff or complicated system integrations.

These features are a part of Wolters Kluwer’s continuously evolving AI-led product suite which mixes proprietary data, natural language processing and agentic AI to offer comprehensive support for lending operations._It is a solutions designed with an emphasis on openness and regulatory compliance and its mission is to help financial institutions working efficiently, maintaining their confidence in their decision-making processes.

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Silobreaker Mimir is an embedded agentic capability for intelligence operations, working directly within the analyst workflow. It helps teams retrieve evidence, deepen analysis, and turn validated findings into native assets and stakeholder ready outputs – such as creating dashboards and reports – without leaving the platform. Intelligence teams can scale their coverage of complex evolving stakeholder requirements while maintaining full oversight, with all outputs grounded in verifiable source material and transparent reasoning.

“For intelligence teams, the real challenge is accelerating analysis without losing context, accountability, or trust,” said Silobreaker CEO, Geoffrey Brown. “Silobreaker Mimir applies AI directly within the intelligence workflow, helping teams deliver research and reporting faster, while preserving the context and control that decision-makers rely on.”

Also Read: Kleene.ai Launches KAI Assistant, A Native AI Interface for Its Data and Analytics Platform

In parallel, Silobreaker has introduced an integration layer, using an MCP‑based approach, that allows Silobreaker intelligence to be securely accessed by customer‑owned AI assistants and workflow tools. This enables executives, risk teams and operational users to work with evidence‑backed intelligence in systems they already use, without exposing sensitive data or compromising analytical standards.

Combined, these capabilities bridge the distance between the decision maker and the analyst, embedding AI within the analyst workflow to drive depth and speed, while extending trusted intelligence into broader enterprise workflows to increase reach. This provides executives with the confidence needed to navigate an increasingly complex global operational environment.

Silobreaker’s agentic AI capabilities are purpose‑built for intelligence use cases, ensuring outputs remain explainable and auditable. By working from Priority Intelligence Requirements (PIRs), analysis stays aligned to real intelligence priorities, with clear source attribution and defined analytical guardrails. This allows organizations to adopt AI with confidence across both analyst‑led and stakeholder‑facing workflows.

Source: BusinessWire

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As data teams face growing pressure to deliver insights faster with fewer resources, KAI replaces the manual workflow of switching between SQL consoles, logs, documentation, and messaging threads with a single conversational interface inside the platform. It is now enabled for all existing customers.

In early access, teams achieved 70% faster SQL generation and saved over 20 hours per month previously spent on debugging, report building, and query optimisation.

“Data platforms have always demanded technical fluency to extract full value. KAI Assistant changes that,” said Ian Liddicoat, CPO at Kleene.ai. “By embedding natural language processing directly into our transform and pipeline layer, we make day-to-day data work accessible to anyone on the team not just engineers.”

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

What KAI Assistant Does

KAI is powered by Google Gemini via Vertex AI. When data previews are used, all data is converted to synthetic form via AWS Bedrock before any LLM processing ensuring raw customer data is never exposed.

Capabilities at launch include natural language SQL generation and optimisation, pipeline search and inspection, table schema previews with synthetic data, log-based debugging, and interactive documentation access. Later this year, Kleene.ai will extend KAI to full pipeline creation and natural language querying of its machine-learned models for pricing, forecasting, segmentation, and media optimisation.

Source: GlobeNewswire

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This initiative assists in overcoming these hurdles by allowing companies to use AI systems with more control over data location, governance, and infrastructure options. This is a new perspective in the enterprise world where, along with compliance to regulations, the flexibility and performance of the company must be equally ensured through transparency in operations.

“Enterprise AI is colliding with a world that is more fragmented, more regulated, and less forgiving of architectural shortcuts,” said Sam Pierson, Chief Technology Officer, Qlik. “Companies still need to move quickly. They still need value. They still need flexibility. The challenge is making that possible while preserving control, trust, and the ability to adapt as the environment changes. That is the problem the Qlik AI Sovereignty Initiative is built to address.”

Also Read: Domo Enhances Data Governance and AI Agent Control with New Admin Capabilities

Through the initiative, Qlik has further enhanced its cloud region footprint in AWS by rolling out more deployments in strategic locations across the globe, which include Europe, the Middle East, and South America. Further, the company will provide the support of the AWS European Sovereign Cloud to give customers the flexibility to choose among different jurisdictions and locality requirements.

Not only has the initiative put an emphasis on data and application governance in addition to data traceability and traceability in the context of both the data pipelines and the artificial intelligence workflows but also Qlik has been focusing on becoming more compliant. Qlik will be working towards certification such as German C5 as well as EU NIS2.

The initiative offers agentic AI capabilities but also maintains strict control over data usage. Furthermore, Qlik has offered a scalable and modular solution through an open data architecture and interoperable protocols. Finally, unveiled at Qlik Connect 2026, the initiative puts sovereignty at the core of enterprise AI.

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“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.

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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

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