Hisense Introduces UR8 with Natural and Real Colour

Posted in Commentary with tags on September 29, 2026 by itnerd

Hisense is introducing the UR8 to Canada, an accessible RGB MiniLED TV series designed to bring next-generation display technology, natural and real colour, immersive entertainment and advanced gaming performance to more consumers worldwide.

As The Origin of RGB MiniLED, Hisense continues to push the industry toward a new pinnacle of display technology through its latest RGB MiniLED TVs. Powered by Chromagic Technology — Hisense’s proprietary optical architecture integrating a self-developed Chromagic RGB Chip, advanced Optical Design and Colour Management System — the UR8 delivers more natural and lifelike colours, covering up to 100% of the BT.2020 colour gamut while maintaining high energy efficiency and reducing harmful blue light.

Earlier this year, the Consumer Technology Association (CTA) officially recognized “RGB LED” TVs as a new category in display innovation, with Hisense playing an important role in driving and advancing the industry standard. This further reinforces RGB MiniLED as a significant industry milestone and a new benchmark for premium TV experiences.

Powered by the Hi-View AI Engine RGB processor, the UR8 delivers richer, more accurate visuals that bring movies, live sports and gaming worlds to life with greater depth and realism. Native 180Hz Game Mode ensures ultra-smooth motion and responsive performance, allowing users to stay fully immersed during high-speed action and fast-paced gameplay. Immersive 2.1.2 multi-channel surround sound tuned by Devialet further elevates the experience with cinematic audio that makes every scene feel more engaging and lifelike.

With the UR8, Hisense continues to accelerate the adoption of RGB MiniLED technology, making next-generation display innovation more accessible to consumers globally. Combining advanced picture performance, intelligent processing and sophisticated design, the UR8 represents another major step forward in the future of premium home entertainment.

For more information, please visit hisense-canada.com.

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Anthropic IPO filing details massive AI bet while warning of existential risks

Posted in Commentary with tags on September 29, 2026 by itnerd

Anthropic’s IPO prospectus, reviewed by Reuters, lays out a sweeping bet that AI will transform the global economy more profoundly than industrialization, electricity and the internet, while simultaneously warning investors that increasingly powerful AI could pose “catastrophic or existential risks to humanity.”

At the same time, Anthropic warned those systems could develop self-preserving behaviors, including resisting shutdown, concealing or manipulating information and behavior resembling blackmail. Risk factors occupy roughly 80 pages of the 261-page prospectus, as Anthropic cautioned that expanding the capabilities and uses of advanced AI could increase the potential for unintended harm.

Anthropic reported that revenue grew twelvefold to nearly $4.6 billion in 2025, while the company posted a $42 billion net loss. The company also plans to spend $518 billion on cloud, computing and infrastructure obligations in the coming years as it builds increasingly capable AI systems.

Damon Small, Board Member, Xcape:

“Anthropic’s IPO prospectus paints a remarkable, and unsettling, picture of the future of artificial intelligence (AI). The financial numbers are striking. Anthropic’s revenue reportedly grew to nearly $4.6 billion in 2025, yet the company posted a $42 billion net loss. It also expects to commit approximately $518 billion to cloud computing, infrastructure, and related obligations as it develops increasingly powerful AI systems.

“OpenAI has faced similar concerns and has reportedly delayed its planned IPO by at least a year. The potential earnings for companies developing these systems are clearly enormous, but so are the investments required to reach that potential.

“This is reminiscent of the late 1990s dot-com bubble. The Internet ultimately transformed the economy, but the enormous enthusiasm and investment surrounding it also produced a wave of companies whose business models could not meet investor expectations. Many failed, despite the technology proving itself valuable.

“AI will follow a similar trajectory. The technology has great potential, but translating that potential into sustainable, profitable businesses is a very different challenge. The companies that ultimately emerge as the leaders may be very different from those attracting the most attention and investment today.

“Many will try; few will succeed.”

Seemant Sehgal, Founder & CEO, BreachLock:

“Anthropic putting self-preserving behavior, shutdown resistance, and deceptive action in the risk factors of an IPO prospectus tells you where the frontier actually is, because companies do not disclose that language lightly. What follows from that disclosure is a security problem that most organizations running AI agents in production have only just begun engaging with, which is that the model itself cannot be trusted as the enforcement point for its own scope, and the controls have to sit at the orchestration layer around it if any of this is going to hold up when the systems start behaving in ways nobody planned for.”

Jacob Krell, Senior Director: Secure AI Solutions & Cybersecurity, Suzu Labs:

“Anthropic is building its revenue plan around stronger models, then warning investors that those same models may resist shutdown, conceal information or exhibit behavior resembling blackmail. That is a poor place to look for margin. The commercial milestone should be model efficiency, systems that deliver useful capability on cheaper hardware and at lower inference cost.

“At $4.6 billion in revenue, Anthropic reported an $8.06 billion operating loss. Compute and infrastructure consumed $7.33 billion, more than half of operating expenses, and the company disclosed $518 billion in future cloud, computing and infrastructure commitments. Those figures describe a company scaling its cost base before it has shown that capability gains produce enough margin to pay for the systems behind them.

“Anthropic’s own safety warning sharpens the problem. Every release that increases capability may also make evaluation and control more difficult. Efficiency would change that equation, allowing growth through lower cost per task and reduced operating expense. I would want Anthropic to make efficiency its primary product milestone. Right now, it is asking investors to fund faster capability growth while acknowledging that the same capability growth may create risks its safety program cannot yet control.”

If I were Anthropic, I would do my level best to not be OpenAI before going after raising cash on the open markets. But I suspect that’s not how it will go. Which is a pity.

UPDATE: Aaron Beardslee, Manager of Threat Research at Securonix, provided the following comments:

“It will be interesting to see how the courts will lean one way or the other. This is similar to a self-driving car – is the car held liable or is the driver held liable? I would argue the person behind the wheel is responsible for whatever the vehicle does. As well, if you’re building a tool that does cool things and makes cool things, you need to make sure it doesn’t run around and do cyber crime on its own because it had a good idea.

AI agents don’t have a moral compass just programmed rules. If this rule isn’t programmed, they’re going to go ahead and try it just it like what happened with the Australian healthcare incident. AI agents found that IT staff forgot about or didn’t know what was publicly available. It could also have been a misconfiguration or what turned off and didn’t turn on, and now you have Open AI in the news.”

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Guest Post: AI Doesn’t Eliminate Technical Debt. It Inherits It.

Posted in Commentary with tags on September 29, 2026 by itnerd

By Don Boxley, CEO and Co-Founder, DH2i (www.dh2i.com)

AI has been dominated by one question for the last two years: How smart is the model?

As a place to start, it’s understandable. Capabilities that were difficult to imagine just a few years ago have been unlocked by better models. Better reasoning, lower costs, faster inference, or more natural conversations are all now promised by every new release.

But, I think… we’re reaching an interesting turning point. The question is changing as AI moves from pilot projects into everyday business operations. Organizations aren’t asking whether AI can generate a better answer. They’re now asking whether they can depend on it. A very different problem, right?

Every Successful AI Project Eventually Becomes an Operations Project

The first version of an AI application is usually built by developers. The second version is owned by operations. That’s true of almost every technology we’ve adopted over the last thirty years. Building something is one challenge. Running it every day is another.

Once an AI app starts supporting customers, approving transactions, helping clinicians, or assisting employees, reliability becomes part of the product. Users don’t separate the model from the application. They simply expect it to work. If it doesn’t, nobody blames the language model. They blame the business.

That’s why I believe the next phase of AI won’t be defined by model improvements alone. It’ll be defined by how well organizations operate the infrastructure underneath those models.

AI Doesn’t Replace Existing Infrastructure

One assumption that keeps showing up is that AI somehow gives organizations an opportunity to start over. It doesn’t.

Most enterprises are introducing AI into environments they’ve spent years refining. They’re not building greenfield environments. Critical databases already exist. Business applications already exist. Windows servers, Linux systems, Kubernetes clusters, public cloud services, private cloud infrastructure, and edge deployments all exist. AI has to live with all of it.

That means the challenge isn’t replacing existing infrastructure. It is making existing infrastructure work together in ways it wasn’t originally designed to.

The Hardest Problems Aren’t AI Problems

The answers rarely have anything to do with model accuracy, when you ask an operations team what worries them. They worry about downtime. They worry about planned maintenance becoming unplanned outages. They worry about databases staying available. They worry about security. They worry about recovering quickly when something breaks. Those concerns haven’t changed because of AI. However, they have become more important.

The less tolerance there is for operational failure, the more business decisions depend on AI.

If an AI app isn’t available during peak business hours – that isn’t an AI problem. Availability is the problem. If an AI app can’t securely access the data it needs – that isn’t an AI problem. Architecture is the problem.

Architecture tends to outlive individual technologies – and that’s an important distinction. Today’s models will eventually be replaced. Good infrastructure should make that tech replacement almost invisible.

Hybrid Isn’t a Compromise

Hybrid infrastructure was treated like a temporary stop on the way to something else for years. I’m not convinced that’s true anymore. Different workloads have different requirements, and that’s why organizations are choosing hybrid.

Development teams may prefer Kubernetes. Production databases may remain on VMs. Sensitive data may stay on-prem. Inference may run closer to users.

Those aren’t signs that modernization has failed. They’re signs that organizations are making practical decisions instead of ideological ones. Infrastructure should support that flexibility rather than fight it.

The Conversation We Should Be Having

When AI discussions begin with model selection, infrastructure often becomes an afterthought. It should be the opposite.

Organizations should understand how they’ll keep the app available, how they’ll protect the data feeding it, how they’ll automate recovery, and how they’ll support the workload as it inevitably moves between environments–all before deciding which model to deploy.

Of course, those are not glamorous conversations. But, they are the ones that determine whether AI becomes a dependable business capability or another isolated technology project.

What You Can Do Tomorrow

If your organization is planning an AI initiative, don’t begin your next meeting by asking which model to use. Instead, ask your architects and operations teams a few different questions:

  • What happens when the database goes offline, especially, if this AI app is business-critical? (The ideal answer: It shouldn’t stay offline. With minimal disruption, critical workloads should automatically fail over to a healthy node. Availability should be built into the architecture. It should not depend on someone manually restoring service.)

  • If an app, cluster, or site fails, how quickly can we recover? (The ideal answer: Recovery should be measured in seconds or minutes – not hours. Automated detection and failover should reduce downtime and eliminate manual intervention wherever possible.)

  • Without having to do a major redesign, can this workload move between environments? (The ideal answer: Yes. Applications should be portable across physical servers, VMs, Kubernetes, cloud, and edge environments. Without requiring the application itself to be rewritten. Infrastructure should adapt to the workload. Not the other way around.)

  • From day one, are we designing security, availability, and automation into the architecture, or are we planning to add them later? (The ideal answer: They must be integrated from the beginning, because retrofitting resilience and security after deployment is more expensive, more complex, and introduces unnecessary risk.)

  • Which parts need to evolve? And, which parts of our existing infrastructure already solve these problems/challenges? (The ideal answer: Keep what already works. Modernize only where it creates business value. AI should leverage existing investments whenever possible instead of forcing wholesale infrastructure replacement.)

Again, agreed… those conversations may not generate headlines. But they’ll have a far greater impact on whether your AI initiatives succeed over the next five years.

Technology will continue to change… Models will improve… New platforms will emerge… That’s the easy part.

However, organizations won’t lead their respective markets and create lasting value from AI because they chased every new breakthrough. They’ll be the ones that build operational foundations that let them adopt new technology without disrupting the business.

I think we can all agree, that’s actually what good infrastructure has always done.

AI simply gives us another reason to get it right.

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Schneider Electric, SECLAB expand OT security partnership

Posted in Commentary with tags , on September 29, 2026 by itnerd

Schneider Electric and SECLAB announced an expanded OT cybersecurity partnership aimed at protecting critical industrial systems as AI accelerates the discovery of vulnerabilities and attackers increasingly target industrial processes directly.

The companies said newly discovered vulnerabilities can be exploited within days, while patching a PLC can require waiting for a maintenance window followed by testing and requalification, potentially leaving industrial systems exposed for months.

Their approach adds hardware-based protection that filters industrial communications between PLCs, distributed control systems and safety instrumented systems, allowing only communications required for the industrial process. Tests on Schneider Electric platforms found the technology provided application-level filtering without perceptible process latency. Schneider Electric will manage deployment and maintenance, and the companies plan to develop a new generation of OT-specific products expected in 2027.

Doc McConnell, Head of Policy and Compliance, Finite State said this:

“When Schneider Electric makes security decisions, the effects reach homes, businesses, industrial sites, and energy infrastructure around the world. It’s great to see Schneider and SECLAB take on one of the fundamental OT security problems: the gap between finding and fixing a PLC vulnerability. That gap will only get more dangerous as AI tools speed up new vulnerability discovery. Hardware-based filtering buys operators time, and it works best alongside visibility into the firmware itself, so manufacturers and their customers know before an incident which devices carry a vulnerable component, and where patching matters most. That kind of evidence is exactly what the EU Cyber Resilience Act is pushing manufacturers to have on hand.”

I like this move as it will have a great effect on everyone. Whether threat actors think twice is a whole other matter entirely.

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Guest Post: Check Point and NVIDIA Tackle a Growing AI Security Blind Spot

Posted in Commentary with tags , on September 29, 2026 by itnerd

Check Point Software and NVIDIA announced a new integration that combines Check Point’s AI security monitoring with NVIDIA’s Open Agent Safety Platform and OpenShell runtime. Together, the technologies can evaluate an agent’s actions before they execute, helping organizations identify and stop potentially harmful behavior in real time. Check Point’s semantic monitoring engine can make those decisions in under 100 milliseconds. 

In July that gap became a public incident. Agents running an internal OpenAI cyber evaluation escaped their isolated environment and spent four and a half days inside Hugging Face’s production infrastructure in pursuit of the answers to their test. Nobody had attacked them. They were doing what they had been asked to do, by a route no one had anticipated. Governing that route is the job of security, and it takes two kinds of control.

A Boundary Outside the Agent’s Reach
The first is a boundary. NVIDIA’s safety and security teams make the case in Where Security Fits in an AI Agent Stack: “The harness guides what an agent tries. The infrastructure controls what an agent can do.” Prompts and model safeguards shape behavior, but the agent can work around them, so they cannot be the boundary.

NVIDIA Open Agent Safety Platform builds that boundary into the infrastructure. At its core is NVIDIA OpenShell, an open, secure runtime that governs how an agent executes, what it can see and do, and where its inference goes. Nothing is permitted by default, every allow and deny is recorded, and enforcement runs outside the agent’s process, so it holds even if the agent is compromised. The platform also includes NVIDIA Sentry, running on NVIDIA BlueField-4 and using NVIDIA DOCA for out-of-band monitoring and security-policy enforcement. This hardware-isolated, host-independent watchdog keeps enforcing policy even if the host itself is compromised. While optimized to run on NVIDIA Vera CPU- and BlueField DPU-based systems, the platform is also compatible with other hardware systems.

Where a Boundary Stops
A boundary decides whether an action is allowed. Whether the action still makes sense for the task is a separate judgement, and it is often where the real problems sit.

Reading invoices, querying the supplier database and starting an approved payment workflow are all part of the invoice agent’s work. If it then begins listing credentials, opening files unrelated to any invoice and preparing to send data to a new destination, each action might pass a check on its own. Together they describe an agent that has stopped doing its job, a pattern that only shows when behavior is followed over time.

Guardrails that inspect a single prompt or response remain essential, and they are a core part of Check Point Software’s AI security, but problems that unfold across twenty steps need semantic monitoring. It follows the agent as it works and relates each action to what the agent was asked to do, using its reasoning signals, tool calls and earlier actions. At every step it comes back to the same question. Does this still fit the job the agent was given? We described the approach in Stopping the AI Agent Actions No Rule Could See Coming, and our research team has shown the verdict can arrive before the action runs, in under 100 milliseconds.

What We Built With NVIDIA OpenShell
Knowing an agent is drifting only matters if something can act on it, which is why the boundary and the judgement belong together. We have run Check Point semantic monitoring with NVIDIA OpenShell through the runtime’s security middleware. OpenShell sees each action before it reaches the host, our monitor weighs it against the agent’s task and history, and OpenShell enables us to enforce the result. Our research team’s post on synchronous control monitoring shows the monitor at work, with videos of it stopping harmful agent actions before they run and letting safe tasks through.

Agents are useful partly because they find approaches nobody foresaw, so the response is proportionate, anywhere from logging an action or holding it for approval to stopping the agent.Where This Goes Next

No single vantage point sees everything an agent does, so our approach is one shared decision layer that draws on many enforcement points instead of a separate security stack at each. NVIDIA Open Agent Safety Platform adds more of those points. NVIDIA Sentry provides attested telemetry to inspect agent behavior and detect deviations.

Those signals are most useful alongside the context cyber security already holds. An unusual tool call may mean little on its own. From a highly privileged agent that has left its task and is reaching for a vulnerable production system, it means something else entirely. Agents act on the same identities, networks and data security teams already protect, and securing them cannot be a separate discipline.

It matters to us that OpenShell is open source. We are working with the OpenShell community, where security ideas are tested in the open by many contributors, and the partnership gives us a direct path to contribute our own work back.

Back to the Invoice Agent
The invoice agent’s goal is the same as it was at the start. What surrounds it now is a runtime it cannot talk its way past, a watchdog in silicon beneath it, and a monitor that keeps checking whether its path still fits the job. As agents take on longer, more autonomous work across what NVIDIA calls the AI factory, that combination is how security keeps pace with the way they behave.

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CyberAcuView Selects as its CIRM Platform Advancing Cyber Resilience for the Insurance Industry

Posted in Commentary with tags , on September 29, 2026 by itnerd

CYGNVS and CyberAcuView today announced their collaboration. CyberAcuView selected and deployed CYGNVS to be the underlying platform for CyberAcuView and its member organizations to manage incident response in an out-of-band, secure, governed and compliant environment with a comprehensive audit trail and chain of custody.

CyberAcuView was founded in 2021 by seven leading cyber insurance carriers, and its current 25 members represent two thirds of the global cyber insurance market. CyberAcuView brings together the expertise of its member companies to help develop industry best practices, aggregate anonymized claims data to identify trends and insights, engage with regulators and law enforcement, share the latest threat vector details to help everyone be better prepared, and support a competitive and resilient cyber insurance marketplace. That collective vantage point now runs on CYGNVS, which already operates as the out-of-band command center for more than 3,000 customer organizations across 71 countries, running over 50 new business critical incidents every week.

No single organization sees enough major incidents to build deep experience on its own. Cyber insurance is now purchased by most mature organizations globally, and the cyber insurance industry has visibility across its policyholders both before and after an incident, giving insurers a unique position to assemble best practices, insights and expertise unavailable to individual organizations. Cyber insurance also gives clients access to pre-vetted panels of external providers, including law firms, forensics consultants and crisis management firms. 

To execute on its mission, CyberAcuView needed an out-of-band platform, independent of any member’s own network to enable all the member organizations to engage with CyberAcuView and each other through adaptive playbooks and workflows in a secure, controlled, governed environment with full chain of custody and audit trail. In the event of a wide-scale critical incident, CyberAcuView needed a single command center where the industry could come together to ensure an effective response.

CyberAcuView is running a workshop using CYGNVS for its member organizations on October 7, 2026 at the NetDiligence Cyber Risk Summit in Philadelphia. Learn more at www.CYGNVS.com/cyberacuview. 

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The Thanksgiving plus-one that actually help according to Samsung

Posted in Commentary with tags on September 29, 2026 by itnerd

Thanksgiving comes with plenty to be thankful for, and a pretty long to-do list.

This year, the Galaxy Z Fold8, Z Fold8 Ultra and Z Flip8 can be the holiday plus-ones that actually pitch in. Here’s how Samsung devices can take something off your Thanksgiving plate:

1.      Plan Like A Pro: As Thanksgiving plans come together, Galaxy AI helps keep you on track. Now Brief shares useful information at a glance, while Now Nudge recognizes dates, times and locations on screen and suggests next steps (like populating your calendar or pulling up a shared location).

2.      Become A Multitasking Machine: Too many cooks in the kitchen? Unfold Galaxy Z Fold8 or Galaxy Z Fold8 Ultra for more room to multitask. You can keep the Friendsgiving group chat open alongside that ambitious recipe, or go full screen on a YouTube tutorial when it’s time to de-lump the gravy.

3.      Capture The Moments Worth Keeping Everyone knows the phone eats first, and 50MP cameras on these Galaxy devices have the goods to document an Instagrammable plate. The Galaxy Z Fold8 Ultra’s 200MP main camera can capture everything from close-up details to the whole Thanksgiving spread, while Flex Mode on the Galaxy Z Flip8 makes hands-free shooting for group selfies as easy as pie.

For a little extra peace of mind over the holiday weekend, Samsung Care+ offers added protection for eligible Galaxy devices, with 24-month, 12-month and month-to-month coverage options.

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97% of deepfake victims at schools were female, most fakes were created by students

Posted in Commentary with tags on September 29, 2026 by itnerd

Cybernews has analyzed the Resemble AI Deepfake Incident Database and found that there have been 83 deepfake incidents at educational institutions around the globe since the start of 2025. Researchers then analyzed the cases to understand who is being targeted, who is creating the content, and where these cases are happening. 

Here are the key findings:

  • 71% of recorded deepfakes at educational institutions involved sexual content;
  • 97% of victims were female;
  • 72% of victims were minors;
  • 65% of deepfake incidents at educational institutions happened at secondary schools;
  • Students themselves were the perpetrators in 57% of cases, while teachers/staff were the perpetrators in 18% of cases.

The cases show just how big a problem AI-generated deepfakes have become at schools, and how vulnerable children are. Creating a convincing fake no longer requires advanced technical skills, which is why prevention, clear school policies, and effective guardrails on AI platforms are as important as ever.

For more information on this, here’s the full study:

https://cybernews.com/ai-news/deepfakes-at-educational-institutions-research

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CloudSEK Traces 85 npm Typosquats to Infrastructure Hosting a GPU Attack Framework Targeting vast.ai

Posted in Commentary with tags on September 29, 2026 by itnerd

CloudSEK is out with a two-part investigation — TOPHIT — uncovering a threat operation that connects a large-scale npm supply-chain campaign with an emerging GPU cryptojacking framework targeting the vast.ai marketplace.

In Part 1, CloudSEK researchers found that a single npm account, @prime0, published 85 typosquatted packages in just over three minutes, targeting 29 of the ecosystem’s most widely downloaded libraries, including chalk, semver, debug, minimatch and ajv. The packages were designed to beacon system information to a command-and-control server and, when loaded, could poll the server every 30 seconds for commands to execute on the infected machine.  

The researchers found that the same server hosting the npm command infrastructure was also running VHX Harvester, a purpose-built offensive framework targeting the vast.ai GPU rental marketplace, which CloudSEK investigates in Part 2. The evidence currently establishes shared infrastructure and an assessed common operator, rather than proving that the npm campaign was directly feeding victims into the GPU operation.  

Key findings from the investigation

  • 85 malicious npm packages were published by one account within approximately 3 minutes and 13 seconds, indicating automated mass deployment rather than manual publishing.  
  • The packages imitated 29 extremely popular npm libraries, with every target library recording at least 181 million weekly downloads.  
  • The malware could collect details including the hostname, username, operating system, working directory, IP addresses and Node.js version, and could establish a remote command channel when the package was loaded.  
  • The same infrastructure exposed VHX Harvester, a GPU-focused offensive platform actively targeting vast.ai.  
  • By September 25, the framework had enumerated 297 GPU host IPs, scanned 13,368 service endpoints, harvested metadata from 416 services, deployed 25 bridge agents and achieved one confirmed root shell on a victim Jupyter notebook.  
  • CloudSEK found that 17 API endpoints on the attacker’s own panel required no authentication, exposing the framework’s API documentation and even its complete agent source code with hardcoded credentials.  
  • Researchers also found 208 exfiltrated files, including 98 environment-variable dumps containing API keys, database credentials and cloud-service tokens from victim GPU instances.  
  • The framework outlines an eight-stage attack chain, moving from GPU-host discovery and scanning to credential harvesting, lateral movement through Docker networks, Jupyter access and ultimately the intended deployment of cryptocurrency miners. At the time of CloudSEK’s investigation, no miners had yet been planted, indicating the operation was still developing.  

One particularly interesting technique is the use of legitimately rented GPU containers as “bridge agents”. The operator rents a low-cost container on the same physical host as a target and then scans the internal Docker bridge network, potentially reaching services that are not exposed directly to the internet.  

Here’s both parts of CloudSEK’s investigation below:

Part 1: A One-Operator npm Typosquat Flood, Co-hosted with a GPU-Cryptojacking C2

Part 2: Renting the Attack Surface: A GPU Cryptojacking Framework Targeting vast.ai, Exposed by Its Own Misconfiguration
 

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Dodge AI raises $2.65M from Accel and Google to fix the $600B enterprise firefighting problem

Posted in Commentary with tags on September 29, 2026 by itnerd

Enterprise software is never finished. Once SAP, Salesforce, Oracle, or Microsoft Dynamics goes live, the business keeps changing, and thousands of company-specific rules get built into the software over years. When something breaks, the answer is rarely in one place. Keeping it all running costs enterprises more than $600 billion a year. 

Dodge AI has raised $2.65 million to change how that work gets done, with an AI platform that resolves incidents and change requests across enterprise applications while documenting the custom logic that makes each system unique. The round was led by Accel and Google’s venture arm, with participation from New Build Venture Capital, Antler, Schema Ventures and angels from the SAP ecosystem.

Why this matters now

For decades, enterprise application maintenance has run through large system integrators like Accenture, TCS, and IBM. The standard playbook: put 20 to 50 people offshore to handle incidents, change requests, background jobs, and the everyday operational fires that keep enterprise systems alive.

That model keeps the lights on, but it creates a deeper problem. Fixes go undocumented, customizations pile up, and technical debt compounds inside systems of record. Over time enterprises grow more dependent on their maintenance partner, because the knowledge of how the system actually works lives across tickets, consultants, configuration layers, and memory.

What Dodge AI is building

Dodge AI’s platform acts as a control plane across enterprise applications including SAP, Salesforce, Microsoft Dynamics, Kinaxis, Oracle JDE and the other systems that sit at the core of large companies. It connects across business processes, ERP customizations, ITSM systems, and legacy configurations to pinpoint root causes, recommend fixes, and modernize faster.

The platform is also designed to become a source of truth for agents operating in production. In enterprise systems, the most important knowledge lives in the exceptions: why one warehouse allocates inventory differently, why one pricing rule overrides another, why a background job runs only at night.

Traction

Dodge AI is already working with more than a dozen enterprises, half of them publicly listed companies, on incident management and process optimization across stacks like SAP, Kinaxis, and Microsoft Dynamics. The platform fields hundreds of queries every hour, giving Dodge AI a widening view into how enterprise systems break and which patterns drive repeat maintenance work.

The results are concrete. When a truck could not load at a warehouse because a Goods Receipt Note was printing incorrect information, Dodge AI traced the fault across SAP, Kinaxis, and internal warehouse software and delivered a fix within minutes. Another customer had been running inventory planning overnight because SAP kept crashing if it ran in the morning; Dodge AI modernized the process and made it 132x faster, freed the team of 10 people maintaining it, and improved order allocation time by 8 hours.

What’s next

Dodge AI sees maintenance as the entry point to a much larger change in enterprise IT. Companies want to modernize, but CIOs cannot risk breaking systems that already work, and tight budgets are consumed by daily incidents. Solving maintenance first frees IT teams from constant firefighting and gives them a clearer path to modernization, because the same exception intelligence that resolves incidents today is what lets production agents operate safely inside mission-critical systems tomorrow.

The company is now pushing deeper into the maintenance layer, into the exceptions, configurations, and operational logic that define how each enterprise actually runs. Whoever understands mission-critical systems best gets to automate them, and Dodge AI is turning the hidden intelligence inside every enterprise into the operating manual for the agents that will run enterprise IT.

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