In Hardware and Network news this week…

Innodisk Launches DDR5-8000 RDIMM for Edge AI and High-Performance Computing

Innodisk is expanding its memory portfolio with a DDR5-8000 RDIMM designed to support edge AI and high-performance computing workloads. As enterprises deploy increasingly compute-intensive applications closer to where data is generated, high-speed and reliable memory is becoming an important infrastructure component. The new memory solution is positioned to support demanding workloads that require greater processing performance, helping organizations build infrastructure capable of handling AI, analytics, and other data-intensive applications.

In Business Technology news this week…

Arcadis Deepens Autodesk Collaboration to Accelerate AI-Led Infrastructure Delivery

Arcadis is deepening its collaboration with Autodesk to advance AI-led infrastructure delivery. The partnership highlights how engineering and infrastructure organizations are incorporating AI and digital technologies into design and project workflows. By combining industry expertise with Autodesk’s technology capabilities, the collaboration aims to support more efficient infrastructure development and help organizations manage increasingly complex projects through data-driven and AI-enabled processes.

In Cloud Computing & Mobility news this week…

Thales and Google Cloud Partner to Secure Agentic AI Workflows and Safeguard Autonomous Execution

Thales and Google Cloud are partnering to address security requirements surrounding agentic AI workflows and autonomous execution. As AI agents gain the ability to perform tasks and interact with enterprise systems, organizations need stronger safeguards around access, data, and automated actions. The collaboration focuses on helping enterprises deploy agentic AI while maintaining security and control, reinforcing the importance of trusted infrastructure as autonomous AI becomes more widely adopted.

In Artificial Intelligence news this week…

IBM Introduces Self-Hosted Deployment for IBM Bob to Advance Enterprise AI Sovereignty

IBM is introducing a self-hosted deployment option for IBM Bob to give enterprises greater control over their AI environments. The move addresses growing demand for AI sovereignty, particularly among organizations that need to maintain control over sensitive data, infrastructure, and AI workloads. Self-hosted deployments can provide businesses with greater flexibility around data governance and operational control as they integrate AI into critical enterprise processes.

In FinTech news this week…

Oracle Introduces Agentic AI Solutions to Fight Financial Crime

Oracle is introducing agentic AI solutions aimed at helping financial institutions combat financial crime. AI agents can support processes such as monitoring, investigation, and analysis by handling complex workflows and identifying relevant patterns across large volumes of financial information. The development reflects the financial sector’s growing interest in applying agentic AI to strengthen compliance operations while improving the speed and efficiency of financial crime detection and response.

In HealthTech news this week…

Oracle Launches Oncology EHR to Accelerate AI-Driven Cancer Care

Oracle is introducing an oncology-focused electronic health record designed to support AI-driven cancer care. The platform aims to provide healthcare organizations with specialized digital capabilities for managing oncology workflows and clinical information. As healthcare providers increasingly explore AI for clinical support and care coordination, purpose-built health technology can help make relevant patient information more accessible while supporting more connected approaches to cancer treatment and management.

In Enterprise Software news this week…

Omnissa Launches Omnissa Elara to Govern AI and Enterprise Actions

Omnissa is launching Omnissa Elara to provide governance over AI-driven and enterprise actions. As organizations introduce AI agents capable of interacting with applications and executing tasks, governance becomes increasingly important for maintaining visibility, control, and accountability. Elara is designed to address this emerging requirement by helping enterprises manage AI activity and automated actions across their digital environments while supporting more controlled adoption of agentic technologies.

Article of the Week

Data Science in Enterprise Business: How Organizations Turn Data Into Smarter Decisions and Growth

Data science is becoming an increasingly important part of how enterprises turn large volumes of information into actionable business insights. From predictive analytics and operational optimization to customer intelligence and strategic planning, organizations can use data science to identify patterns and make more informed decisions. This guide explores how businesses are applying data-driven approaches to improve operations, uncover opportunities, and support sustainable growth in an increasingly data-centric enterprise environment.

The post ITDigest’s Weekly News Roundup Featuring Autodesk, Thales, Google Cloud, IBM, Oracle, Omnissa and more appeared first on ITDigest.

The harder question is what enterprises do with all that information once it enters the organization. Customer records, financial data, operational signals and unstructured content often remain scattered across systems. This article examines how data science in enterprise business connects those fragments through data architecture, predictive analytics, machine learning and NLP, while also addressing the roadblocks that stand between an impressive pilot and a system that actually works in the business.

What Is Enterprise Data Science?

Enterprise data science is the use of statistics, machine learning and advanced analytics across an organization’s data to solve business problems at scale. It goes beyond reporting by connecting models with business systems, workflows and decisions while accounting for governance, deployment and ongoing monitoring.

That last part is what separates enterprise data science from a small analytics project.

A data scientist can build an impressive model on a laptop. But an enterprise needs that model to work with large datasets, connect with existing systems, follow access and privacy rules, and remain useful after deployment. It also needs business teams to understand what the model is telling them and when they should act on it.

In that sense, data science in enterprise business is not just a technical function. It sits between data, technology and decision-making. The model matters, but so do the systems around it.

The Four Core Pillars of an Enterprise Data Science Strategy

Intelligent Data Integration and Architecture

Before an organization can find patterns in its data, it needs to bring that data together in a usable form.

That sounds obvious, but enterprise environments make it difficult. A company may have separate CRM, ERP, finance, marketing and operations systems, often built at different times and managed by different teams. Some information may also sit in older applications that were never designed to work with modern analytics platforms.

Google reported that 43% of IT leaders cited difficulty integrating legacy APIs and data sources as their biggest agentic AI infrastructure gap. While the figure relates specifically to agentic AI infrastructure, it points to a familiar enterprise problem. Valuable information can remain locked inside systems that do not easily communicate with each other.

This makes data architecture a core part of data science in enterprise business. Data lakes and warehouses can bring information into common analytical environments, but the work does not stop there. Data also needs clear definitions, quality checks, access controls and governance. Otherwise, the organization simply creates a larger place to store inconsistent information.

Predictive Analytics

Most business reports explain the past. Predictive analytics asks a more useful question. What might happen next?

That change can affect everything from demand planning to customer churn and equipment maintenance. Instead of waiting for a problem to appear in a monthly report, an enterprise can use historical and current data to identify patterns that point toward a future outcome.

Google’s September 2026 introduction of TabFM provides a useful example of where this is heading. Google says the pretrained model can make predictions through a single SQL statement, removing separate training and deployment steps, and can process inference tables containing millions of rows in minutes.

The significance is bigger than the technology itself. Predictive capabilities are moving closer to the environments where business data already lives. That can reduce the distance between analysis and action, which is one of the central promises of data science in enterprise business.

Prescriptive Intelligence

Prediction is useful, but prediction alone does not tell a manager what to do.

Suppose a model indicates that demand for a product is likely to increase. The next question is obvious. How much inventory should the business carry? Should production increase? Should marketing spend change? Should the company adjust pricing?

Prescriptive intelligence tries to support those decisions. Instead of stopping at a forecast, machine learning models can evaluate different conditions and recommend actions based on the desired business outcome.

This is an important step in the development of data science in enterprise business. The goal is not to replace the person making the decision. It is to give that person a stronger basis for making it. The model can process a level of information that would be difficult for a human team to examine manually, while the final decision can still account for context that a model may not understand.

Also Read: Enterprise Metaverse Applications: How Businesses Are Using Immersive Technology to Transform Operations

Natural Language Processing

Enterprise data is not limited to spreadsheets and databases. Some of the most useful information may be sitting inside a customer complaint, sales email, contract or support ticket.

Natural language processing, or NLP, allows organizations to extract patterns and meaning from this kind of unstructured information. A support team can identify recurring complaints. A sales organization can spot common objections. A legal team can search large collections of contracts for specific clauses or risks.

The real value appears when this information is connected to structured business data. A customer complaint becomes far more useful when it can be viewed alongside purchase history, service interactions and product usage.

That is where data science in enterprise business becomes broader than traditional business intelligence. It can bring together information that was previously too fragmented or difficult to analyze at scale.

High-Impact Use Cases Driving Enterprise Growth

Supply Chain and Operational Efficiency

Supply chains generate a huge volume of data, however, high volume does not automatically mean intelligence of the supply chain.

The science of data helps companies analyze demand trends, inventory flow, transportation data, and other signals to determine what can go wrong. This is especially true for anomaly detection since disruptions do not necessarily happen in any particular way.

A sudden demand change, unusual equipment behavior or delivery delay can look insignificant when viewed alone. A model examining multiple signals at once may identify that something has changed and trigger an earlier response.

This is one of the clearest examples of data science in enterprise business creating operational value. The aim is not simply better forecasting. It is giving teams more time to respond before a small deviation becomes an expensive disruption.

Hyper-Personalization at Scale

Personalization becomes difficult when the customer base grows beyond what a marketing team can reasonably study one customer at a time.

Traditional segmentation may group people by location, age or purchase history. Data science can examine behavior in much greater detail. Browsing patterns, engagement, buying frequency and product preferences can reveal groups that would otherwise remain hidden.

Those insights can support recommendation engines, churn prediction and next-best-action strategies. More importantly, the process can run continuously rather than relying on a segmentation exercise that gets updated every few months.

The business value of data science in enterprise business here is scale. People still decide what the brand should offer and how it should communicate. Models help them understand customer behavior across a much larger population.

Risk Mitigation and Fraud Detection

Risk often starts quietly. A transaction looks slightly unusual. A customer suddenly changes behavior. A series of small events begins to form a pattern.

By using data science, it will be easy to detect such signals through the comparison between what is going on now and the previous history. This may be achieved through fraud detection system and risk models.

The advantage is speed. Instead of discovering a problem after losses have already accumulated, organizations can monitor changing patterns and investigate potential risks earlier.

That makes data science in enterprise business particularly valuable in environments where the cost of delayed detection can be significant.

Overcoming Roadblocks from Pilot to Production

There is a gap that many enterprise AI discussions gloss over. Building a model and running a model inside a business are two very different things.

A January 2026 World Economic Forum published analysis found that less than one in five organizations considered themselves data-ready, with integration, data quality and governance among the major challenges. That finding gets to the heart of the problem. An organization cannot expect reliable intelligence from data that it cannot reliably access, understand or govern.

Privacy creates another layer of complexity. Customer and employee information may be subject to rules around collection, storage, access and processing. Governance therefore cannot be something added after a model has already been built. It needs to be part of the process.

There is also the production gap. AWS reports that 88% of enterprise AI agent pilots never reach production. This figure specifically concerns AI agent pilots, but it highlights a wider lesson for organizations investing in data science in enterprise business. A successful demonstration is not the same as a dependable business system.

That is why MLOps matters. It brings deployment, testing, monitoring, version control and retraining into a structured process. Models change as business conditions change, so they need attention after launch too. Enterprise data science becomes useful only when the organization can operate that intelligence reliably over time.

The Future of Generative AI and Data Science

Generative AI is changing the way people interact with enterprise information. An executive who does not know SQL can potentially ask a question about sales, forecasts or customer behavior using ordinary language.

But the difficult part is not generating the sentence. It is making sure the answer is based on the right data, definitions and business context.

That is why generative AI is more likely to augment data science than replace it. Data scientists still need to build reliable analytical systems and ensure that the underlying data can support trustworthy conclusions. Generative AI can then make those capabilities easier for business teams to access.

The result could be a broader role for data science in enterprise business, where analytical intelligence is no longer restricted to specialist teams.

Becoming a Data-Driven Enterprise

Calling data an asset has become easy. Making it useful is the difficult part.

A company may purchase analysis platforms, develop machine learning algorithms, and incorporate AI interfaces into its operations but fail because the data is fragmented or ungoverned. This is the harsh reality that most companies have to face. Technology may be moving much faster than its foundation.

The better starting point is therefore not another flashy AI pilot. It is an honest audit of the data architecture already in place. Which systems are connected? Which information can team trust? Who owns the data? Can models be monitored after deployment? And can their output actually influence a business decision?

The organizations that answer those questions well will have a much stronger foundation for data science in enterprise business. The competitive advantage will not come from having more data. It will come from making better use of the data already available.

Frequently Asked Questions

How does data science improve enterprise decision-making?

Data science minimizes uncertainties by identifying patterns from huge sets of business data and then interpreting these patterns for explanation or prediction of outcomes. This makes decision-making more credible when the managers are analyzing risks, allocating resources, or making decisions.

Do enterprises need to hire in-house data scientists?

Not all companies require having a big team of data scientists within the organization. The use of AI consultancy and citizen data science and AutoML can depend on how complex the requirements of the organizations are; but then again, they also require having people who have knowledge of what lies behind those requirements.

What is the difference between data analytics and data science?

Data analytics generally focuses on understanding existing information and explaining what happened. Data science goes further by combining statistics, machine learning and advanced analytical methods to identify patterns, predict outcomes and support more complex decisions.

Why does data quality matter in enterprise data science?

A sophisticated model cannot compensate for unreliable or disconnected data. Strong data quality gives model a better foundation and makes their outputs easier for business teams to understand, trust and use.

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Resolving this data residency and sovereignty conflict, technology leader IBM announced a self-hosted deployment option for IBM Bob, its agentic AI software development platform.

Engineered to run natively within client-controlled environments including on-premises data centers, private cloud infrastructure, and air-gapped systems the update allows enterprise engineering teams to execute full-lifecycle software development and legacy modernization directly behind their own security perimeters.

The News: In-Place Execution, Air-Gapped Flexibility, and OpenShift Integration

The technical foundation of IBM’s announcement is bringing agentic Software Development Lifecycle (SDLC) automation directly to where enterprise data natively resides. Rather than requiring code repositories to be mirrored or pushed to external cloud runtimes, self-hosted IBM Bob operates inside the enterprise’s private control plane.

Also Read: Autonomous Execution with Governance: Oracle Unveils Fusion Claw to Power Complex Enterprise Workflows

Key architectural highlights and operational capabilities delivered by the launch include:

Complete Environment Control: Deploys natively on Red Hat OpenShift, enabling seamless operation across on-premises servers, air-gapped environments, private cloud perimeters, or sovereign cloud regions.

Full-Lifecycle Agentic SDLC Orchestration: Coordinates specialized role-based AI agents across the entire software lifecycle—spanning architecture planning, code generation, automated testing, pipeline deployment, and legacy application refactoring.

In-Place Intellectual Property Security: Guarantees that sensitive source code, internal APIs, and trade secrets never leave company-controlled boundaries, preventing data leakage into public model training sets.

Integrated Auditability & Guardrails: Features built-in prompt normalization, sensitive data redaction, real-time policy enforcement, and self-documenting CLI logs (BobShell) to deliver transparent audit trails for compliance leads.

Transforming the AI Application Development & Enterprise Software Engineering Industry

IBM’s rollout of self-hosted IBM Bob signals a decisive structural turning point across the AI Application Development, Developer Tooling, and Enterprise DevOps sectors.

The Sunset of “Data-Exfiltrating” Cloud Coding Tools
For the past three years, developer tooling vendors focused on speed, launching cloud-hosted copilot extensions that ingested developer code blocks to return automated completions. However, enterprise CISOs and risk officers frequently blocked company-wide deployment over fears of third-party data tracking, copyright risk, and cross-border data transfer violations.

IBM’s announcement accelerates the shift toward sovereign developer platforms. The AI developer tooling industry is entering an on-premises execution era. Vendors will no longer be evaluated merely on code suggestion speed, but on whether their platforms can run fully isolated within client environments without losing model accuracy or agentic reasoning capabilities.

Establishing Sovereign AI as an Engineering Baseline
As nations introduce strict data sovereignty laws (e.g., European AI Act, national data localization mandates), enterprises can no longer rely on centralized, single-region cloud AI providers for mission-critical software refactoring.

By decoupling agentic code execution from multi-tenant cloud APIs, IBM sets AI sovereignty as a core software engineering benchmark. Developer platforms must now offer flexible deployment topologies that respect jurisdictional boundaries, private key management, and air-gapped operational constraints.

Broad Operational Impact on Enterprise Businesses Operating in Regulated Sectors

For CIOs, CTOs, and CISOs in financial services healthcare defense, and the public sector, self-hosted agentic developer platforms will immediately deliver commercial and strategic value to the markets, like:

Unsecured modernizing of legacy core systems: highly regulated organizations can entrepreneur agentic Artificial Intelligence to freshen up twenty-year-old COBOL, Java, or mainframe codebases in without contravening regulatory guidelines.

Avoided Cloud Egress and Third-Party Compliance Risks: Transferring sensitive code and proprietary logic within the enterprise eliminates the possibility of cross-border data leakage fines.

Accelerated Developer Productivity in Secure Environments: Air-gapped engineering teams in defense, banking, and Government Now Enable the use of modern agentic code workflows that were once available only in unclassified Public-Cloud Settings.

Automated Governance & Audit Transparency: Creation of unchangeable, locally contained audit trails for each AI-assisted commit means that the engineering leads will breeze through demanding SOC 2, ISO 27001 and national security audits.

Via the deployment of fully-lifecycle agentic AI agents behind the enterprise firewall, self-hosted IBM Bob offers a credible sovereign path forward for the software engineering enterprise: a panoply of missions that has the power to modernize all applications at the rate of AI, in the absolute control of even the most highly regulated entities.

The post IBM Introduces Self-Hosted Deployment for IBM Bob to Advance Enterprise AI Sovereignty appeared first on ITDigest.

Also Read: Citi Becomes First Bank to Launch Multi-Market Instant Payments on Swift

Meanwhile, Oracle Nexus Reach is a browser-based extension that works with existing financial institution applications and can conduct adverse media scans, assist with investigative analysis and narrative creation, and execute autonomous AI Reviews and AI Investigations. “Investigators need a clearer picture of potential risk early enough to act with confidence,” said Jason Somrak, global head of financial crime products, Oracle Financial Services. “Oracle Nexus Case Flow and Oracle Nexus Reach put relevant context and AI capabilities into the flow of work, helping teams focus their expertise where it matters most. Investigators remain in control of the decisions, so institutions can scale their response to financial crime without compromising rigor or accountability.” The launch highlights Oracle’s focus on embedding agentic AI into established financial crime workflows while retaining human oversight for investigative decisions and supporting regulatory accountability.

Read More: Oracle Brings New Agentic Capabilities to the Fight Against Financial Crime

The post Oracle Introduces Agentic AI Solutions to Fight Financial Crime appeared first on ITDigest.

Also Read: Dun & Bradstreet Brings Verified Business Context to IBM watsonx Orchestrate

The technology’s predictive capabilities were validated through a blinded assessment involving EY US’ 2025 Global Wealth Management study, in which Aaru replicated six months of fieldwork involving 3,600 investors across more than 30 markets in a single day. “Too often, organizations invest in transformation without clear visibility into the outcomes they can expect. The EY-Aaru Alliance changes that by helping leaders to simulate, validate and optimize decisions before committing resources. Together, we help organizations move from insight to action with greater confidence, speed and measurable impact.” said Justin Singer, EY-Aaru Alliance Leader, EY US. Ned Koh, President and Co-Founder of Aaru, said, “Ultimately, every business exists to monetize behavior, and our mission is to help enable the enterprise to make better decisions. We’re excited to share this vision with EY US and expand its aperture to provide for clients at the same time.” The partnership reflects growing enterprise interest in AI-driven simulation as organizations seek to test scenarios, understand behavioral responses, and make strategic decisions with greater speed and evidence.

Read More: EY announces alliance with Aaru to help organizations drive growth with greater confidence through behavioral simulation

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The rise of generative artificial intelligence and niche automated scanning technologies has revolutionized the nature of the cyber-criminal adversary. Today’s threat actors target application software faster than ever by deploying AI algorithms to disassemble executable applications, identify exploits based on zero-day software flaws, and circumvent licensing constraints at an exponentially increased rate. Thales’s Sentinel Envelope Plus tackles this emerging threat by layer-injecting multi-dimensional anti-tamper strategies into application executables, without need for source-code access or developer build-interruption.

Fortifying Application Binaries Against Automated Threat Vectors

Sentinel Envelope Plus operates as a sophisticated security wrapper around application executables and dynamic libraries. By integrating state-of-the-art code transformation techniques, the platform neutralizes static and dynamic analysis attempts conducted by AI-driven automated decompilers and reverse engineering frameworks.

Also Read: Abnormal AI Expands AI Security Suite Built on Behavioral Security Platform

Key technical capabilities and architectural features delivered by Thales Sentinel Envelope Plus include:

Advanced Code Obfuscation: Transforms executable code and control flow logic into complex structures that resist automated static analysis and AI pattern recognition.

Anti-Debugging & Anti-Hooking: Detects and blocks active debugging tools, memory inspection techniques, and malicious process injection attempts in real time.

Source-Code Independent Deployment: Protects compiled binaries directly, allowing software vendors to secure legacy applications and third-party modules without modifying source code.

Cryptographic Integrity Verification: Continuously verifies application memory states and runtime integrity to prevent unauthorized patching, code modification, or license manipulation.

Cross-Platform Compatibility: Delivers unified binary protection across desktop, mobile, cloud, and embedded software environments with minimal performance overhead.

Safeguarding Commercial IP and Software Monetization Streams

As strong safeguards are built against AI-accelerated reverse engineering, Thales makes it possible for CISOs and CTOs, with commercial software vendors, to defend information that matters most. Sentinel Envelope plus guarantees protection of secret algorithms, data structures and licensing techniques intact, reliably holding down corporate revenues and reputation in the ever more contaminated virtual world.

The post Thales Debuts Sentinel Envelope Plus to Defend Software Against AI Threats appeared first on ITDigest.

New generation enterprise autonomous software agents executing long-lived multi-step business processes quickly overwhelm traditional legacy API gateways and network management tools. Autonomous agents produce a rapid growth of on-the-fly API calls, distributed database queries, and fast-moving messages to external software applications, flood the network, and enlarge security vulnerabilities. Kong’s Volcano platform resolves these infrastructure hurdles by establishing a dedicated control plane built specifically for agentic traffic orchestration.

Purpose-Built Connectivity and Governance for Autonomous Agents

Volcano extends Kong’s high-performance API management architecture into the AI runtime layer. By decoupling underlying infrastructure complexity from agent execution, the platform enables software developers and platform engineers to build, deploy, and govern agentic workflows without creating fragmented security policies or custom network integration code.

Also Read: Anaconda Kilo Partners with OpenAI for Sign in with ChatGPT 

Key technical capabilities and architectural features delivered by Kong Volcano include:

Agentic API Traffic Routing: Intelligently routes high-volume inter-agent messages and tool execution requests with sub-millisecond latency.

Granular Identity & Access Governance: Enforces zero-trust security controls, dynamic access permissions, and role-based policy guardrails for autonomous software agents.

Real-Time Cost & Resource Telemetry: Monitors model context window usage, API token consumption, and cloud infrastructure costs across multi-agent workflows.

Unified Observability & Audit Logging: Captures end-to-end trace data for agent actions, tool calls, and model decisions to ensure compliance and auditability.

Cross-Cloud & Multi-Model Interoperability: Connects agents across heterogeneous cloud environments, private data centers, and leading foundation AI models seamlessly.

Accelerating Safe Agentic Innovation across Enterprise Infrastructure

Through the introduction of deterministic governance and efficient routing in agentic ecosystems, Kong empowers CIOs, CTOs, and enterprise platform architects to safely scale their AI efforts. Volcano serves as the necessary infrastructure layer that ensures rogue agent activity is prevented, cloud resources are optimized, and resilience is achieved as companies transition into fully autonomous digital businesses.

The post Kong Unveils Volcano Agentic Infrastructure for Enterprise AI appeared first on ITDigest.

Pairing High Bandwidth with Reliability for Faster AI Computing
As AI models grow in scale and complexity, the efficient movement of massive datasets becomes increasingly critical to system performance. The DDR5 8000 RDIMM raises data transfer speeds to 8000 MT/s and offers capacities ranging from 16GB to 64GB, supporting applications such as large language models (LLMs), generative AI, digital twins, and telehealth. Additionally, it improves data throughput for demanding workloads, while its registered ECC architecture enhances signal quality and error correction for more reliable AI processing.

Built for Harsh Industrial Environments and System Resilience
Innodisk’s DDR5 8000 RDIMM is engineered with rigorous validation and built-in protection features to support continuous operation. Integrated Transient Voltage Suppressor (TVS) diodes and eFuse protection help mitigate the risk of system failure caused by voltage fluctuations and abnormal power conditions. Designed for prolonged industrial use, the modules also feature 30μ” gold fingers and anti-sulfuration protection, enhancing contact durability and corrosion resistance for extended service life.

Also Read: Rohde & Schwarz Adds Multi-Channel Pulse Analysis to FSWX

Advancing Memory Performance for the AI Era
“The rapid expansion of AI workloads is reshaping system performance requirements, with the ability to efficiently handle vast data volumes becoming just as important as compute power,” said Samson Chang, GM of Embedded DRAM Division at Innodisk. “The DDR5 8000 series helps customers overcome memory bandwidth bottlenecks while offering a more flexible path to system upgrades through enhanced memory performance, serving as a keystone for edge AI applications.”

Expanding the DDR5 8000 Portfolio
Innodisk will roll out additional DDR5 8000 modules in Q1 2027, including CUDIMM, CSODIMM, ECC CUDIMM, and ECC CSODIMM, offering broader options across diverse system designs. Through continued innovation, Innodisk advances memory technology to deliver the performance, reliability, and durability required for next-generation intelligent computing.

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Remediation is harder in a hospital than almost anywhere else. A vulnerable medical device cannot be pulled offline the way a laptop can, so patching one means working around clinical schedules and backup equipment. Every change also has to carry an approval and audit trail that will hold up to a regulator. Faced with those constraints, the industry built capable tools for identifying potential vulnerabilities and left the harder half of remediating them to spreadsheets, email threads, and manual ticket entry.

That manual process is losing ground. CVE submissions rose 263% between 2020 and 2025, according to NIST, outpacing the public vulnerability infrastructure hospitals rely on to turn a disclosure into a device-specific alert. The backlog now shows up in the fleet itself: the average connected medical device carries 6.2 known vulnerabilities, roughly 75% of infusion pumps carry one listed in CISA’s Known Exploited Vulnerabilities catalog, and roughly 60% of the installed base runs components no longer receiving manufacturer support at all, according to Ordr’s 2026 medical device research. Meanwhile, healthcare has remained the costliest industry for data breaches for more than a decade, averaging $7.42 million per incident in IBM’s 2025 Cost of a Data Breach Report.

A proven model, applied to a new device class

Gluware IoMT Exposure Management is built on the same foundational automation engine Gluware runs across enterprise network infrastructure, and on DIAL (Device Interaction and Automation Layer), the semantic translation layer proven across 56 operating systems and 22 vendors. This is not another point solution for hospitals to integrate and maintain. IoMT Exposure Management is that platform extended to a new class of devices the same platform, the same change-control model, and the same team already automating the network.

“Most hospitals can tell you what’s on their network. Far fewer can tell you which of those devices are actually exposed today, when each one was fixed, and who approved the change,” said Jeff Gray, CEO and Co-Founder of Gluware, Inc. “Hospitals shouldn’t have to stand up a separate platform and a separate team to protect the devices closest to patient care. We’ve spent nearly two decades closing that gap on enterprise networks, under the kind of change control that clinical environments demand.”

Also Read: Movemedical Launches Ask Move AI, the First AI Assistant Purpose-Built for Med Device Field Operations

The solution connects five stages into a single pipeline:

• Clinical-grade discovery. Gluware leverages Claroty xDome as the source of truth for IoMT inventory, pulling in device details and the vulnerability relationships tied to each device rather than standing up a second inventory for hospitals to reconcile.

• Component-level vulnerability matching. CVEs are enriched against data from the MITRE CVE Program and matched against the specific components and configurations present on each device to reduce the false positives and false negatives that erode staff confidence in alerts.

• From advisory to applicable patch. Knowing a CVE exists does not tell a team what to install. Gluware’s integration with the Microsoft Update Catalog retrieves the specific Knowledge Base update for each supported platform and flags components that no longer receive manufacturer support, where a compensating control is the best available option.

• A shared operational record. A new IoT Device Manager gives clinical engineering and IT the same continuously updated view of the device fleet, closing a longstanding gap between two teams that use different tools and different vocabularies.

• Change-controlled execution. Gluware’s Network RPA and ServiceNow integration open a change ticket for every patch action, route it through the hospital’s existing approval process, and write the completed action back as a system of record.

Most of these stages already exist in a large hospital, spread across separate tools owned by separate teams. The delay accumulates in the handoffs between them: the export from one system into another, the cross-reference done by hand, the ticket opened manually and closed without a link back to the device it covered. Running the stages as one pipeline removes those handoffs, transforming the change record into a product of the work itself rather than something reconstructed afterward for an auditor.

Built from a customer requirement

The solution grew out of work with The Ohio State University Wexner Medical Center. The academic medical center was already running Gluware to automate its network and had built visibility into its IoMT fleet. However, remediation was still being coordinated by hand across teams and systems that were never designed to work together. These workflow constraints meant known vulnerabilities stayed open longer than anyone wanted.

Siji Atekoja, Deputy CIO and CTO, saw that the platform his teams already used to change the network safely could carry a medical device the rest of the way from a published CVE to a patch applied, approved, and on the record. Gluware and Ohio State built that path together by connecting the medical center’s existing device inventory to automated vulnerability matching, patch identification, and change-controlled execution. That work became Gluware IoMT Exposure Management.

The post Gluware Extends Its Automation Platform Beyond the Network to Connected Medical Devices appeared first on ITDigest.

Addressing this structural execution bottleneck, enterprise software leader Oracle announced Oracle Fusion Claw a governed agentic execution runtime engineered to power Oracle Fusion Agentic Applications.

Launching alongside 25 new Claw-powered applications expanding Oracle’s suite to 75 total job-finishing agentic tools Fusion Claw separates probabilistic AI reasoning from deterministic enterprise computation. This architectural decoupling allows enterprises to deploy autonomous workflows at scale while maintaining strict governance guardrails.

The News: Decoupled Architecture, Enterprise Operating Envelopes, and Outcome Receipts

The primary innovation behind Oracle Fusion Claw lies in its runtime architecture, which restricts LLMs (such as Google Gemini and OpenAI models) to reasoning and planning inside isolated sandboxes while delegating actual data transactions to deterministic compute engines.

Also Read: Safeguarding Autonomous Execution: Thales and Google Cloud Partner to Secure Agentic AI Workflows 

Key technical highlights and operational features introduced in the launch include:

Separation of Intelligence and Computation: Utilizes frontier models strictly to analyze business contexts and synthesize execution plans. Once a plan is formulated, Fusion Claw routes high-volume mathematical, scheduling, or posting operations through efficient deterministic code, driving down token consumption costs.

Enterprise Operating Envelopes: Governs all agentic workflows through customizable operational guardrails. Organizations define specific standard operating procedures, permissions, risk thresholds, approval rules, and escalation boundaries before an agentic run begins.

Immutable Outcome Receipts: Generates auditable digital receipts upon task completion. Each receipt provides an immutable log detailing the governing authority applied, underlying evidence evaluated, decisions made, and specific transactions posted.

Expanded Agentic Application Portfolio: Introduces 25 new specialized Claw applications across ERP, HCM, SCM, and CX including Ledger for month-end close exception clearing, Workforce Staffing for dynamic labor schedule adjustments, and Shipping Consolidation for automated logistics optimization.

Transforming the Enterprise Software, ERP, and Cloud Application Industry

Oracle’s release of Fusion Claw marks a decisive paradigm shift across the Enterprise Software, ERP, and Business Application landscape.

The Phase-Out of “Chat-Wrapper” Copilots
For the past three years, software vendors competed by embedding conversational sidebars that answered user questions or drafted simple content. However, enterprise buyers found that conversational co-pilots still left the actual administrative burden such as posting ledger lines or reconciling supply chains on human staff.

Oracle’s launch accelerates the sunset of passive chat assistants. The enterprise software market is entering a governed execution era. Platforms will no longer be judged on whether they can summarize data on a dashboard, but on whether their runtime architectures can autonomously complete multi-step business jobs from start to finish.

Decoupling AI Reasoning from System-of-Record Writes
Early enterprise agentic deployments faced severe pushback from compliance and auditing teams due to the risk of AI models directly altering critical financial, payroll, or customer databases.

By creating a structural boundary where LLMs design plans but deterministic engines execute them, Oracle establishes boxed-off agentic governance as an industry baseline. Enterprise application vendors must now prove that their AI platforms enforce strict identity, authority, and data boundaries to satisfy audit requirements.

Broad Operational Impact on Enterprise Businesses Operating in this Sector

For Chief Executive Officers (CEOs), Chief Information Officers (CIOs), and financial operations leads navigating large-scale enterprise suites, deploying governed agentic runtimes yields immediate strategic and commercial advantages:

Predictable, Scalable AI Economics: Restricting LLM usage to initial planning phases while executing high-volume compute deterministically prevents runaway API token costs during peak operational periods.

Automated End-to-End Business Processes: Enabling autonomous software to handle complex workflows such as clearing month-end accounting discrepancies or dynamically rebalancing shift schedules drastically compresses cycle times.

Audit-Ready Compliance and Risk Control: Generating granular Outcome Receipts for every autonomous run ensures complete visibility, giving risk committees total transparency over AI-driven transactions.

Flexible Autonomy Delegation: Allowing organization leads to adjust automation levels ranging from assisted drafting to governed full-auto execution enables companies to adopt agentic workflows at their own pace.

By moving past simple co-pilot prompts to a governed, cost-efficient execution runtime, Oracle Fusion Claw provides a clear blueprint for the next phase of enterprise software enabling organizations to run complex business operations at machine speed without compromising control or compliance.

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