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Getting AI-ready while building your data infrastructure is like learning to drive a manual transmission on the wrong side of the road. It’s complicated and requires potentially dangerous multitasking. Organizations with immature data-handling processes that are adopting AI are trying to solve multiple technology problems at once, and risk stalling out.Unsurprisingly, 48% of enterprises cited data-related issues as their top challenge to AI adoption in NVIDIA's 2026 State of AI report. Most enterprise AI programs don't fail because of the model or solution selected. They fail because underlying data is fragmented, inconsistent and poorly governed. Get Your Data Foundation in OrderEnterprise data is messy in layers. It’s scattered across many systems, making it hard to pull together into a coherent picture. Even when you can consolidate it, you often will run into granularity or identifier mismatches. One application may store account numbers as plain digits, while another adds “ACCT” as a prefix. That small inconsistency creates an extra reconciliation step every time you join those data sets.Data governance compounds the problem. Without a system intentionally designed to control who accesses data, where it moves and what protections are in place, gaps emerge fast. PII exposure is the most obvious risk: an email address that ends up in the wrong hands can trigger a serious breach. Raw, unstructured data also yields mediocre AI outputs and is more expensive to process. Clean, structured data yields better results at lower cost. A third gap, explainability, is quickly becoming a legal requirement. Many countries and several U.S. states now require organizations to demonstrate how AI-driven decisions were reached. Cut corners on the data foundation and you may not be able to show that chain of reasoning. At that point, you’re either in compliance violation territory or your model is producing outputs you can’t defend.Three Steps to Get Your Data AI-ReadyDefine governance before you deploy. Classify your data: what is it, where did it come from and who can touch it. Separate the roles of technical decision-making and compliance oversight. Keeping those responsibilities with different people prevents a compromising situation where the same person sets the rules and monitors compliance.Run cross-functional AI governance as a standing function. Assign a representative from every department and meet monthly to discuss what teams are working on, what concerns have surfaced and what support they need from one another.Approach larger AI-readiness initiatives like any other business project: assign a project manager, designate an executive owner, set a weekly cadence, build a task list and work through it. Collect behavioral data even before you need it. The outcomes you get from AI vary enormously depending on how skilled the operator is, ranging from using it as an expensive search engine to developing autonomous workflows. Without visibility, you might be pouring money into AI licenses and getting Google-level output in return. You don’t know who needs training, whether they have the right tool in front of them or what outcomes they’re achieving. The risk is that you make the wrong strategic call as a result—abandoning a rollout, for example, when the real fix was better training or a different tool.Further considerationsHere’s another layer to consider. When an experienced worker completes a task, with AI assistance, they leave more skilled than when they started. The output and the learning happen together. That's what behavioral data should demonstrate over time – not just task completion, but upward skill trajectories. When someone at the beginning of the learning curve accepts whatever AI produces without critically engaging with it, you get the output but not the growth. Behavioral data is how you catch that gap early, before it becomes a long-term cost you can't unwind.Stay curious and look for the easy wins. Focus your data readiness efforts on the workflows where work actually happens, and prioritize tools that let you get at that data. A recent example illustrates the payoff. A product manager ran an AI-powered analysis of quarterly bug patterns using data from the department’s most commonly used tools. The results were unexpected. One team carried a disproportionate share of incoming tickets, most of them requests for manual workarounds to a missing product feature. While other teams split their time roughly 75% on new work and 25% on incoming bugs, that team was closer to 50-50. By not building a single feature, the organization was effectively operating 1.5 people below capacity.The entire analysis took about 45 minutes. None of it would have been possible without data that was organized, tagged by team, connected to individual contributors, accessible via existing AI connectors and protected by role-based access controls.The organizations that get the most from AI are the ones that empower their people to ask "I wonder if there's something here" — and have data to diagnose in an afternoon. That only happens when the foundation is already in place.Make your data safe with the best backup software.This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. 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Researchers have demonstrated that a computer worm powered by artificial intelligence (AI) can autonomously spread across a network by identifying and exploiting vulnerabilities on different devices, raising fresh concerns about how the technology could change the future of cyberattacks.The proof-of-concept malware, developed by researchers at the University of Toronto and cybersecurity firm CleverHans, combines a locally running large language model (LLM) with an autonomous software agent that can scan networks, assess potential attack paths, and decide how to compromise new targets without human intervention. The researchers say the work shows how AI could enable malware to adapt to unfamiliar environments rather than relying on a single preprogrammed exploit.In experiments described in a new study uploaded June 2 to the arXiv preprint server, the worm was tested against a simulated corporate network containing 33 hosts, including Linux servers, Windows workstation computers and other internet-connected (IoT) devices. The researchers found that the system identified vulnerabilities, compromised new machines, and replicated itself across roughly 62% of the network over the course of a week."The main finding is that this type of system can do more than run a fixed exploit; it can examine the target environment, reason about possible vulnerabilities, use tools to attempt attacks, and then replicate itself after a successful compromise," Michael Agee, an adjunct professor of information technology at Trinity Washington University in Washington, D.C., who was not involved in the research, told Live Science.How does the AI worm work?The setup was relatively straightforward. The researchers took an open-weight LLM (for which training data is publicly available) running on local hardware and connected it to a software framework that could scan networks, collect information about target systems, and carry out attacks. The AI's role was to interpret what it found and decide where to go next."The AI-driven part of the attack is mainly the reasoning and decision-making," Agee said. "The LLM is not magically hacking the system; it is being used to reason about what the information means, suggest possible attack strategies, decide which tool or action should be tried next, and help adjust the approach when something fails."Intelligence does not exist in discovering new vulnerabilities; rather, intelligence exists in determining how quickly an attacker can choose and sequence attacks against previously identified vulnerabilities.Bob Hutchins, adjunct faculty at Lipscomb UniversityIn other words, the worm isn't inventing new ways to break into systems. Instead, it's taking information about a machine, matching it against known vulnerabilities and weaknesses, and deciding which avenue is most likely to succeed.Bob Hutchins, who teaches AI strategy courses at Lipscomb University in Nashville, Tennessee, said the innovation lies in the system's ability to adapt."Traditional worms follow a scripted sequence: Once a vulnerability is identified, the worm replicates," Hutchins told Live Science. "In contrast, the researchers demonstrated that an easily downloaded AI model could be used as the decision-making component of the worm. The worm would analyze each device it encountered to determine its most effective strategy to breach that particular system.""Intelligence does not exist in discovering new vulnerabilities; rather, intelligence exists in determining how quickly an attacker can choose and sequence attacks against previously identified vulnerabilities," he added.What makes this AI worm different from conventional malware?The researchers also designed the worm to work across devices with different levels of computing power. More capable compromised machines equipped with graphics processing units (GPUs) could provide reasoning services for lightweight agents running on less-powerful devices elsewhere on the network."What made it particularly dangerous was a clever tiered design," Tom Vazdar, a professor of AI and cybersecurity at the Open Institute of Technology, told Live Science. "GPU-equipped compromised machines provided reasoning capacity for lightweight agents running on low-power IoT devices that couldn't run an AI model locally. A camera becomes a thinking node in the attack network, not just another door."The research, which has not been peer-reviewed yet, was published as governments, security experts and AI companies continue to debate whether generative AI will make sophisticated cyberattacks easier to carry out. One reason the study has attracted attention is that the researchers did not rely on a frontier model from a major AI company, like OpenAI's ChatGPT or Anthropic's Claude. Instead, they used a much smaller open-weight model that can be downloaded and run offline on normal computers.The researchers did not use leading AI models like ChatGPT and Claude. (Image credit: Jaque Silva/NurPhoto via Getty Images)"The researchers employed lightweight open-weight models during their demonstration, which are relatively easy to download, remove guardrail components from, and utilize," Hutchins told Live Science. "By using these types of models, the researchers challenged a long-standing assumption that only advanced/edge-type models present risks related to misuse."Vazdar argued that the work highlights how attackers could increasingly automate tasks that currently require skilled human operators, telling Live Science, "The attacker's marginal cost drops to essentially zero. And you can't patch your way out of it, because it doesn't rely on a single vulnerability class. It reasons. Patch one hole, and it finds another."Could attackers use this AI worm in the real world?Not all experts agree with that assessment, however. Although researchers described the system as capable of targeting a wide range of devices, some cautioned that the demonstration took place in a highly controlled environment designed to showcase the concept."This is at best a lab-based proof of concept in a target-rich test environment," Agee said. The test network contained many intentionally vulnerable systems and lacked active endpoint defenses. "The paper shows that the approach is possible, not necessarily that this attack would work reliably in a normally, or even minimally, defended enterprise network," he added.Any internet-connected device running vulnerable versions of software is theoretically susceptible to being exploited via a similar mechanism. This has been a truism of malicious code for decades.Bob Hutchins, adjunct faculty at Lipscomb UniversityThe worm also generated activity that security teams could potentially detect, he noted, including network scanning, repeated exploitation attempts and privilege-escalation behavior."Even a basic monitoring setup could flag some of that behavior," Agee said.Hutchins likewise warned against overstating the findings. "'Could potentially target almost any device' is technically correct and emotionally misleading," he said. "Any internet-connected device running vulnerable versions of software is theoretically susceptible to being exploited via a similar mechanism. This has been a truism of malicious code for decades."Organizations can still defend themselves by using many of the same measures recommended against conventional cyberattacks, Hutchins added, including prompt patching, strong passwords and multifactor authentication (using multiple forms of identification to log in to systems, like a password sent via text message on top of your password).Even so, experts broadly agree that the study could mark a shift in how malware could operate in the future. Rather than relying on fixed instructions written by human attackers, future malicious software may be able to make many tactical decisions on its own.Related stories'I violated every principle I was given': AI agent deletes company's entire database in 9 seconds, then confessesAI self-replication hacks 'no longer purely theoretical,' study finds —‬ ‪but experts say it's too soon to panicClaude Mythos explained: Is Anthropic's most powerful AI model really too dangerous to release to the public?"The attack is important because it shows that an LLM-based agent can reason through different targets and adapt its approach," Agee said.For Hutchins, the study ultimately represents exactly the kind of work academic researchers should be doing. The study authors "are performing precisely what academia should perform ‪—‬ researching a legitimate threat within a controlled environment before malicious actors begin building it outside of that controlled environment," he said.Whether attackers adopt similar techniques remains to be seen. What the researchers have shown is that a relatively small AI model can already play a meaningful role in planning and directing a cyberattack.

Operational performance is becoming just as important as investment performance in private credit. As fundraising slows and investor expectations increase, firms are facing growing pressure to modernize the IT infrastructure supporting their portfolios.In fact, transparency and faster reporting are becoming top priorities.Without such capabilities, funds can’t clearly see across their own portfolio, amplifying stress when markets are less forgiving. Especially as private credit scales and operational diligence become more central to allocation decisions, such back-office issues become something more structural. Fortunately, managers that properly invest in their operational foundations now, will be better equipped to manage the increasing demands facing the private credit industry moving forward. Analyzing Operational PressuresPrivate credit is operationally intensive. And many firms never built systems to match the increasing complexity of their portfolios as they grew and evolved. Instead, operations are often spread across legacy servicing platforms, spreadsheets, email-driven workflows, and disconnected internal tools, leaving firms without a single real-time view of portfolio data.Most loan servicing operates on cycles such as monthly reconciliations, quarterly reporting, and batch-based payment processing. This model reflects the constraints of legacy systems and manual workflows. Data must be collected, validated, and processed in stages. Consequently, funds often view their portfolios through periodic snapshots rather than in real-time.Many funds also maintain parallel spreadsheets to verify their servicer's calculations. Known as shadow booking, this redundancy has no other purpose than to increase control. Ultimately though, it’s a sign of mistrust in the data provided, and the underlying calculations are often difficult to inspect. When discrepancies arise, they are discovered after the fact and require manual investigation, often across multiple systems.These realities of the private credit industry are now colliding with a more competitive fundraising environment. Even if their underlying deals aren't catastrophic, private credit funds begin to look fragile if they cannot perform operationally well under increasing pressure. Not surprisingly, many private credit firms are looking to AI to address these issues. But automation software alone cannot repair broken operational foundations. The critical question becomes whether their infrastructure is actually prepared to support the AI model they choose to use.AI Alone Is Not the AnswerAI agents can execute operational work reliably while platforms can provide real-time visibility and full auditability. The pieces are in place. But there’s a pattern in how AI projects fail in the finance industry that's worth naming and it's almost never the model that's the problem.What’s often missing is the infrastructure surrounding the model - the systems, workflows, data access, permissions, and controls that allow AI to operate reliably inside real financial processes. Otherwise known as the harness. Generic AI tools don’t know what a rate notice is. They don't know that a prepayment request triggers a multi-step workflow across every syndicated entity on a facility. They don't know the difference between a funded tranche and a committed-but-undisbursed revolver, or why that distinction matters for how a payoff figure should be calculated.This lack of operational context is also one of the biggest reasons AI hallucinations occur in finance. The majority of hallucinations aren't random malfunctions, they're a context problem. The AI model wants to give an appropriate answer. When it doesn't have access to the specific data it needs, it reasons from what it does know and produces something plausible. Private credit portfolio data isn't embedded in any public language model. If the harness doesn't provide it, through tools, memory or real-time data access, the model will fill the gap with something that sounds right. Which, in a financial operations context, is a real problem.The fix isn’t a better model. It’s a harness that gives the agent access to the right data, at the right moment, with the right tools and controls to retrieve it.The firms that focus on building operational systems that provide context, transparency, audibility and human oversight will receive the greatest value from its AI investments. In private credit, the long-term advantage may not come from adopting AI faster than competitors, but from building the infrastructure, or the harness, capable of supporting it responsibly and at scale.Establishing New Competitive DifferentiatorsAs these operational systems mature and advance, they will also reshape what excellence actually looks like inside private credit firms. Responsiveness won't be a differentiator. It will be assumed. Real-time and instant delivery will be the new baseline.This is because most routine interactions will no longer require human involvement. With real-time systems, shared data layers, and automated workflows, information will be directly accessible and continuously updated. What previously required a request and response cycle will be resolved at the source.As a result, the role of the servicer shifts. They are no longer measured by how quickly they process or reply, but by how well they handle what cannot be automated - exceptions, edge cases, and judgment calls.This is why the next generation of private credit leaders may look fundamentally different from the firms that defined the industry’s earlier growth period. Capital access and underwriting expertise will remain essential, but operational execution is becoming increasingly strategic.Most funds are using general-purpose AI for ad-hoc analysis or are in a holding pattern. A small number are starting to build their own and discovering how much harder it is than first expected. The funds that move first on specialized operational infrastructure will have an advantage that compounds. Not because they picked the right model (the model will keep getting better and cheaper regardless), but because they built or adopted the right harness, trained it on the right context, and gave it the controls that make it trustworthy at scale.In many ways, private credit firms are evolving into operational organizations as much as financial organizations. The ability to manage workflows, data, oversight, and execution will become a defining part of a firm’s competitive performance.We feature the best personal finance software.This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit