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IT Leaders Take Steps to Improve Visibility into Their Attack Surface

Arthur Lozinski
Oomnitza

Hybrid and remote work environments have been growing significantly in the past few years. As individuals move away from traditional office settings in today's new remote and hybrid environments, many operational issues such as poor visibility into asset status and refreshes, unaccounted assets, and overspending on software are becoming a bigger challenge for IT departments. Due to the fact that individuals are utilizing their own devices, such as mobile phones, the attack surface has expanded as a result of the rise in popularity of remote work. With this happening throughout organizations, conventional IT asset discovery, lifecycle management, and security controls are challenged.

Oomnitza's Managing Enterprise Technology Blindspots survey examines how enterprises are managing their technology, what operational issues they are facing, and how the business is impacted by those. The report found that solely relying on siloed and diverse systems to manage different technologies, from endpoints and applications to network and cloud infrastructure, does not provide the integrated visibility, lifecycle control, or automation necessary to optimize resources and manage risk.

In fact, nearly 76% of businesses use multiple technologies to monitor business services, while at the same time, 71% anticipate more security breaches and increased operating expenditures.

Business Impacts of Siloed Systems

It is not uncommon for organizations to have a decentralized management system for different technologies and IT functions. However, as technologies evolve, so must IT departments by changing or adding to how they manage their technology systems to reduce the risk of security breaches and associated costs. Just under half (45%) of IT departments' wasted spend is on software and cloud services.

When digital enterprises consolidate technology assets from siloed systems into a single integrated view, it allows for optimization of technology spending, automation of governance processes to meet compliance and auditing requirements, and visibility of security risks. In this context, 43% of wasted time is spent tracking down technology assets, 32% have experienced slow onboarding, and 23% of enterprises highlighted compliance audit fines as one of the major burdens they face. With a disjointed technology management strategy, leaders are experiencing a significant financial impact on business operations.

Problems with Current Technology Management Recognized

Along with focusing on the management of technology assets, visibility, and operational blind spots, over half of the IT leaders surveyed (57%) are seeking unified and simplified technology visibility and a single source of truth. Having the ability to gain a holistic view of all assets through one reliable source is important to securing endpoints and gaining detailed information about the lifecycle of a device.

Often, IT staff do not have the systems in place to monitor employees' interactions with systems, the location of specific assets, and other key details in one centralized location. As a result, organizations are at a severe disadvantage, not only losing money on assets but also losing their competitive edge.

Additionally, lack of visibility, automation, and other limitations within today's current enterprise technology management landscape are recognized in the survey. More than half of respondents (52%) in the industry have plans to progress from conventional asset management to more modern approaches, and 11% of respondents already have projects underway.

Enhancing the Future of IT

Moving forward, traditional, disjointed, and unaligned systems will not be adequate for the leaders of the future.

Existing legacy IT Asset Management (ITAM) systems were designed for a vastly different working environment than the ones that exist currently. When IT can provide a single, integrated, and real-time source of truth across all technology assets, the benefits associated with it help the user and enterprise achieve measurable results. All of these factors result in improved business results, for example, cost reduction, risk mitigation, enhanced visibility, and increased productivity.

Arthur Lozinski is Co-Founder and CEO of Oomnitza

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

IT Leaders Take Steps to Improve Visibility into Their Attack Surface

Arthur Lozinski
Oomnitza

Hybrid and remote work environments have been growing significantly in the past few years. As individuals move away from traditional office settings in today's new remote and hybrid environments, many operational issues such as poor visibility into asset status and refreshes, unaccounted assets, and overspending on software are becoming a bigger challenge for IT departments. Due to the fact that individuals are utilizing their own devices, such as mobile phones, the attack surface has expanded as a result of the rise in popularity of remote work. With this happening throughout organizations, conventional IT asset discovery, lifecycle management, and security controls are challenged.

Oomnitza's Managing Enterprise Technology Blindspots survey examines how enterprises are managing their technology, what operational issues they are facing, and how the business is impacted by those. The report found that solely relying on siloed and diverse systems to manage different technologies, from endpoints and applications to network and cloud infrastructure, does not provide the integrated visibility, lifecycle control, or automation necessary to optimize resources and manage risk.

In fact, nearly 76% of businesses use multiple technologies to monitor business services, while at the same time, 71% anticipate more security breaches and increased operating expenditures.

Business Impacts of Siloed Systems

It is not uncommon for organizations to have a decentralized management system for different technologies and IT functions. However, as technologies evolve, so must IT departments by changing or adding to how they manage their technology systems to reduce the risk of security breaches and associated costs. Just under half (45%) of IT departments' wasted spend is on software and cloud services.

When digital enterprises consolidate technology assets from siloed systems into a single integrated view, it allows for optimization of technology spending, automation of governance processes to meet compliance and auditing requirements, and visibility of security risks. In this context, 43% of wasted time is spent tracking down technology assets, 32% have experienced slow onboarding, and 23% of enterprises highlighted compliance audit fines as one of the major burdens they face. With a disjointed technology management strategy, leaders are experiencing a significant financial impact on business operations.

Problems with Current Technology Management Recognized

Along with focusing on the management of technology assets, visibility, and operational blind spots, over half of the IT leaders surveyed (57%) are seeking unified and simplified technology visibility and a single source of truth. Having the ability to gain a holistic view of all assets through one reliable source is important to securing endpoints and gaining detailed information about the lifecycle of a device.

Often, IT staff do not have the systems in place to monitor employees' interactions with systems, the location of specific assets, and other key details in one centralized location. As a result, organizations are at a severe disadvantage, not only losing money on assets but also losing their competitive edge.

Additionally, lack of visibility, automation, and other limitations within today's current enterprise technology management landscape are recognized in the survey. More than half of respondents (52%) in the industry have plans to progress from conventional asset management to more modern approaches, and 11% of respondents already have projects underway.

Enhancing the Future of IT

Moving forward, traditional, disjointed, and unaligned systems will not be adequate for the leaders of the future.

Existing legacy IT Asset Management (ITAM) systems were designed for a vastly different working environment than the ones that exist currently. When IT can provide a single, integrated, and real-time source of truth across all technology assets, the benefits associated with it help the user and enterprise achieve measurable results. All of these factors result in improved business results, for example, cost reduction, risk mitigation, enhanced visibility, and increased productivity.

Arthur Lozinski is Co-Founder and CEO of Oomnitza

The Latest

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

 

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...