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The Secure UX Enterprise - Part 1

Gabriel Lowy

In an age where a deeper understanding of customers is a competitive necessity, the inability to effectively correlate, analyze and act on all operational data represents a significant missed opportunity to improve decision outcomes and financial performance. Whether it is customers visiting your website, your employees engaging with them through a SaaS CRM application, an authorized executive accessing sensitive corporate data remotely, or an investor researching your investor relations site, secure UX (user experience) that meets the user's expectations is the hallmark of the secure UX enterprise.

It's time for IT Ops and security monitoring across the entire application delivery chain to converge around unified secure UX. As UX has become the primary driver of business performance and as security shifts from prevention to early detection and incident response, newer storage, processing and advanced analytics technologies not only make this convergence feasible, but essential.

Next to database, no technology investment is more strategic to the enterprise than a unified secure UX platform. IT Ops and security teams will need to cooperate and collaborate more effectively, leveraging operational data that achieves their respective and shared objectives.

Secure UX Drives ROI

Cloud, mobile and social megatrends show that secure UX is vital to the enterprise. As enterprises become increasingly software-defined and seek to get better returns on their digital assets with advanced analytics, more are recognizing that this is not attainable without secure UX.

Our study of the S&P 500 two years ago revealed that companies taking a strategic approach to application performance and UX outperform their peers in both financial and stock market performance. And they use 30% fewer tools to get the job done.

Secure UX directly correlates to the three components of ROI (return on investment):

1. Cost reduction

2. Productivity enhancement

3. Incremental revenue from new channels

Secure UX creates a virtuous cycle of improvement. The better your employees' user experience is the more engaged and productive they are. The better your customers' user experience is the stronger their satisfaction and loyalty to the company. The more secure the user experience is the higher user confidence is in the data they are accessing and sharing.

Secure UX will remain a key determinant and differentiator in the Internet of Things (IoT) era, as 40Gbps and 100Gbps networks become the norm and the definition of "user" expands to include machines. Gartner estimates a 35% CAGR of non-consumer IoT devices from 2013 to 2020, reaching an installed base of 25 billion.

Not Just ROI, But GRC as Well

But secure UX is also germane to meeting GRC (governance, regulatory, compliance) requirements and risk management objectives. Not having end-to-end visibility and intelligence into application and network performance and early detection of abnormal behavior puts companies at a competitive disadvantage.

Extended mean time to repair/resolution (MTTR) undermines employee engagement and customer satisfaction and loyalty. The cost of downtime for a business critical application can be upwards of $1 million per hour, depending on the industry.

It also exposes the company to cyber attacks. Rapid incident response becomes impossible, exacerbating risks while potentially costing the company extensive losses of sensitive data, fines for compliance violations and reputational damage.

The underbelly of modern distributed computing environments is growing regulatory oversight pertaining to systems efficacy and security. For example, industries such as healthcare and financial services are under increasing regulatory pressure to demonstrate systems efficacy and security for protecting sensitive personal information and resilience against market disruptions.

While regulations are nuanced to specific industries, the connectivity and interdependencies of systems are similar across all sectors. Regulators are increasingly focused on these relationships – and the underlying systems and applications – that comprise application delivery chains.

Secure UX can address a host of issues, including:

■ Verifying and troubleshooting questionable transactions

■ Analyzing overall application and network performance

■ Identifying vulnerable users, applications and network segments

■ Verifying performance of low-latency apps such as VoIP and video

■ Ensuring systems efficacy and compliance with GRC mandates

Increasing Complexity is the Principal Obstacle

The greatest barrier to consistently high performance is complexity. Because modern applications have so many connection points between the end user and the data center – often with multiple permission levels – performance issues can arise anywhere along the application delivery chain. Meanwhile, the traditional network perimeter has been erased as more on-premises workloads migrate to private and public clouds, and as more services, applications and data are born in the cloud.

The growth of containers and microservices, as well as the emerging IoT creates new risks that further test security governance with myriad connected devices and the associated big data challenges of machine-to-machine communication. This evolution combined with the unprecedented growth in the number of data sources – both internally and from outside the enterprise – significantly increases systems complexity. And as more users are engaging with these apps with their own mobile devices, often over public WiFi networks, complexity and risk rises further.

These trends create a multiplier effect for the amount of data available to attackers. They also marginalize the effectiveness of traditional network and perimeter security solutions, which were designed to prevent earlier generations of malware. Security teams recognize that the perimeter is too porous due to the explosion in devices, applications and data. They also recognize that people inside the perimeter are often the cause of security breaches. Insiders, unknowingly – or knowingly – either create holes that attackers can exploit or improperly release sensitive information.

Read The Secure UX Enterprise - Part 2

The Latest

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...

The Secure UX Enterprise - Part 1

Gabriel Lowy

In an age where a deeper understanding of customers is a competitive necessity, the inability to effectively correlate, analyze and act on all operational data represents a significant missed opportunity to improve decision outcomes and financial performance. Whether it is customers visiting your website, your employees engaging with them through a SaaS CRM application, an authorized executive accessing sensitive corporate data remotely, or an investor researching your investor relations site, secure UX (user experience) that meets the user's expectations is the hallmark of the secure UX enterprise.

It's time for IT Ops and security monitoring across the entire application delivery chain to converge around unified secure UX. As UX has become the primary driver of business performance and as security shifts from prevention to early detection and incident response, newer storage, processing and advanced analytics technologies not only make this convergence feasible, but essential.

Next to database, no technology investment is more strategic to the enterprise than a unified secure UX platform. IT Ops and security teams will need to cooperate and collaborate more effectively, leveraging operational data that achieves their respective and shared objectives.

Secure UX Drives ROI

Cloud, mobile and social megatrends show that secure UX is vital to the enterprise. As enterprises become increasingly software-defined and seek to get better returns on their digital assets with advanced analytics, more are recognizing that this is not attainable without secure UX.

Our study of the S&P 500 two years ago revealed that companies taking a strategic approach to application performance and UX outperform their peers in both financial and stock market performance. And they use 30% fewer tools to get the job done.

Secure UX directly correlates to the three components of ROI (return on investment):

1. Cost reduction

2. Productivity enhancement

3. Incremental revenue from new channels

Secure UX creates a virtuous cycle of improvement. The better your employees' user experience is the more engaged and productive they are. The better your customers' user experience is the stronger their satisfaction and loyalty to the company. The more secure the user experience is the higher user confidence is in the data they are accessing and sharing.

Secure UX will remain a key determinant and differentiator in the Internet of Things (IoT) era, as 40Gbps and 100Gbps networks become the norm and the definition of "user" expands to include machines. Gartner estimates a 35% CAGR of non-consumer IoT devices from 2013 to 2020, reaching an installed base of 25 billion.

Not Just ROI, But GRC as Well

But secure UX is also germane to meeting GRC (governance, regulatory, compliance) requirements and risk management objectives. Not having end-to-end visibility and intelligence into application and network performance and early detection of abnormal behavior puts companies at a competitive disadvantage.

Extended mean time to repair/resolution (MTTR) undermines employee engagement and customer satisfaction and loyalty. The cost of downtime for a business critical application can be upwards of $1 million per hour, depending on the industry.

It also exposes the company to cyber attacks. Rapid incident response becomes impossible, exacerbating risks while potentially costing the company extensive losses of sensitive data, fines for compliance violations and reputational damage.

The underbelly of modern distributed computing environments is growing regulatory oversight pertaining to systems efficacy and security. For example, industries such as healthcare and financial services are under increasing regulatory pressure to demonstrate systems efficacy and security for protecting sensitive personal information and resilience against market disruptions.

While regulations are nuanced to specific industries, the connectivity and interdependencies of systems are similar across all sectors. Regulators are increasingly focused on these relationships – and the underlying systems and applications – that comprise application delivery chains.

Secure UX can address a host of issues, including:

■ Verifying and troubleshooting questionable transactions

■ Analyzing overall application and network performance

■ Identifying vulnerable users, applications and network segments

■ Verifying performance of low-latency apps such as VoIP and video

■ Ensuring systems efficacy and compliance with GRC mandates

Increasing Complexity is the Principal Obstacle

The greatest barrier to consistently high performance is complexity. Because modern applications have so many connection points between the end user and the data center – often with multiple permission levels – performance issues can arise anywhere along the application delivery chain. Meanwhile, the traditional network perimeter has been erased as more on-premises workloads migrate to private and public clouds, and as more services, applications and data are born in the cloud.

The growth of containers and microservices, as well as the emerging IoT creates new risks that further test security governance with myriad connected devices and the associated big data challenges of machine-to-machine communication. This evolution combined with the unprecedented growth in the number of data sources – both internally and from outside the enterprise – significantly increases systems complexity. And as more users are engaging with these apps with their own mobile devices, often over public WiFi networks, complexity and risk rises further.

These trends create a multiplier effect for the amount of data available to attackers. They also marginalize the effectiveness of traditional network and perimeter security solutions, which were designed to prevent earlier generations of malware. Security teams recognize that the perimeter is too porous due to the explosion in devices, applications and data. They also recognize that people inside the perimeter are often the cause of security breaches. Insiders, unknowingly – or knowingly – either create holes that attackers can exploit or improperly release sensitive information.

Read The Secure UX Enterprise - Part 2

The Latest

Pilots are everywhere, stakeholders are seeking results, businesses are pushing for new tools, and IT teams are being asked to make AI secure, reliable, and useful at scale. But as organizations move from testing AI to operationalizing it, many are discovering that the biggest barrier is not the model, the use case, or even the budget. It is the file data foundation within ...

Fast or cheap? For most of my career in engineering, speed and quality sat on opposite ends of a seesaw. The "OR" in "fast or cheap" was non-negotiable. It was expected that pushing for faster releases meant that something in quality would give way. Tightening quality controls meant the schedule slipped. Every engineering leader I know has lived some version of that tradeoff ... The seesaw is starting to level out ...

I have been building enterprise software for more than 20 years ... One thing stays true across all of it: You do not find out your foundation is wrong during the crisis. You find out when the debt comes due. For a lot of organizations, that bill is arriving now. New research ... puts hard numbers on something practitioners have been sensing for a while. The telemetry problem isn't coming. It's already here ...

The rapid growth of AI workloads is pushing traditional log management approaches to their limits, according to The State of Log Management 2026 report from Dynatrace. Modern logs have become critical to understanding, validating, and securing AI-driven decisions, helping organizations ensure reliability, compliance, and performance at scale. However, the volume and complexity of AI telemetry are overwhelming legacy tools ...

For years, secure connectivity has relied on a familiar pattern: route traffic back to centralized gateways, inspect it, and then allow access. This model worked when applications lived in a handful of data centers and users were largely confined to offices. That model is now under strain. Applications are distributed across clouds, users connect from everywhere, and real-time workloads demand performance that centralized inspection points struggle to deliver. As traffic volumes grow and latency expectations shrink, routing everything through a small number of control points has become both a performance bottleneck and a resilience risk. The future of secure connectivity requires a different approach ...

The AI experimentation phase is over, and the private cloud is where enterprise AI workloads are being deployed for security and scale, according to Private Cloud Outlook 2026, a new report from Broadcom ... 2026 marks an acceleration into a full AI tipping point. The shift is being shaped by three forces — costs, complexity, and control — that public cloud environments are increasingly failing to address for production AI at scale. Key findings from the report include ...

44% of organizations have reported an outage in the past year tied to suppressed or ignored alerts, and 78% had at least one incident where no alert was fired at all ... Engineers learned about failures from customers. That gap between what our tools report and what our customers experience is the problem DevOps teams have been quietly solving with GenAI tooling, even as most enterprises continue to run their NOCs on manual alert triage ...

Cloud outages are usually described as technical failures. When a service goes down, a dependency breaks, or a region has issues, the focus immediately shifts to infrastructure. But if you look closely at how these incidents actually unfold, the root cause is rarely the technology itself. It is almost always tied to decisions made earlier, during design, implementation, or day-to-day operations. The system behaves the way it was built. The real question is how it was built ...

77% of leaders say their teams need AI skills urgently. 64% say their organization plans to train current employees rather than hire new ones. So far, so reasonable. The part that surprised me is who's been put in charge: 34% of those leaders say IT and engineering own the AI skills mandate. Learning and Development or HR own it at 7% of organizations. That's roughly five-to-one in favor of the people who understand the tools, over the people whose actual job is teaching adults how to learn new ones ...

In the ever-evolving digital landscape, enterprises are increasingly focused on enhancing their observability stacks to gain deeper insights into their IT environments. Observability has become a cornerstone of modern IT operations, enabling organizations to monitor, diagnose, and optimize their systems with unprecedented precision. However, a critical piece of the puzzle often goes unnoticed in this transformation: IBM i ...