Skip to main content

The Secure UX Enterprise - Part 2

Gabriel Lowy

Start with The Secure UX Enterprise - Part 1

A Unified Approach Begets Convergence and Collaboration

Unfortunately, most enterprise IT teams still monitor and manage user experience from traditional technology domain silos, such as server, network, application, device, operating system and security. As workloads continue to shift to new architecture, this approach only perpetuates an ineffective, costly and politically-charged environment. 

A unified approach allows IT teams to help their companies leverage technology investments to discover, interpret and respond to the myriad events that impact their operations, competitiveness, security and compliance.

IT Ops teams must understand their users and prioritize the performance of their apps and websites accordingly. They can make sure the apps that drive the business have the highest availability and reliability. In concert with the security team, they can take a balanced approach to prioritizing risks across the enterprise.

As opposed to conventional security layering by infrastructure, application, device and user, a prioritized risk approach allows the security team to dedicate more resources and attention to the assets that are most important to the organization. This strategy is more proactive and intelligence-based, enabling the security team to better defend the organization's most valuable data assets, respond to and remediate incidents in a timely fashion and meet GRC requirements.

Automated continuous monitoring, advanced behavioral analytics, incident response automation and software-defined perimeter provide transaction-level insights into the IT environment that UX and security teams need to better ensure performance, while protecting against risks and improving incident response. Correlations, machine learning engines, and advanced behavioral analytics and data visualization create context based on granularity about users, applications, and endpoints.

The intelligence they provide establishes benchmarks against key performance indicators (KPIs) for what is normal activity and identify anomalous behavior in real time. UX teams can triage the root cause of poor performance to speed MTTR.

Monitoring that is more pervasive, automated and intelligent allows security teams to better understand risks and prioritize threats. Policies and enforcement can be applied automatically to specific applications, user groups or roles so that security teams can use this intelligence to isolate and contain an attack before intruders can cause substantial damage.

A unified approach facilitates mapping resource and application dependencies through a single view of all components that support a service to ensure transaction completion. For security teams, it provides visibility and intelligence into the validity of the transaction and the users involved. They can see the data going into these environments, whether users are authorized to work with this data and when data is attempting to leave.

Automation provides speed and scale to keep up with new architectures and traffic growth. It improves agility and governance, reduces costs, and helps UX and security teams mitigate human error and remediate more effectively.

Next-generation solutions are all capable of collecting vast amounts of transaction data. They can then run advanced analytics against this data for a variety of secure UX use cases. To enable this type of collaboration, data also has to be assimilated from network service providers and cloud service providers in addition to data from within the enterprise.

Data Integration is Key

The better integrated these technologies are, the more intelligence UX and security teams derive from them and the more efficiently they can prioritize risks and remediation. Greater efficiency with IT Ops and security data can drive sustained competitive advantage and reduce risk at lower total cost of ownership (TCO).

Data integration is labor intensive and time consuming. IT teams get bogged down trying to integrate data from different tools. The proliferation of tools for both performance and security monitoring has resulted in a patchwork quilt of incompatible consoles and data. Teams end up spending more time writing scripts preparing data for analysis than gaining real-time insights into secure UX. And they often ignore the barrage of false positives these different tools generate.

Modern integration tools automate much of the cleansing, matching, error handling and performance monitoring that IT Ops and security teams often struggle with manually. Application governance allows teams to take a standardized approach to integrating diverse data sets, including those from SaaS applications and IaaS or PaaS clouds. Unifying disparate data points provides both IT Ops and security teams with more actionable intelligence to speed MTTR and incident response.

Conclusion

Secure UX has a domino effect across all functional areas of the organization. Users from sales, marketing and product development through manufacturing and supply chain management have more confidence in the data they are working with. The result is improved modeling and decision outcomes. At the same time, companies strengthen financial management, reduce risk and ensure adherence with governance, regulatory and compliance requirements.

IT teams must evolve toward a unified approach that promotes collaboration and efficiency to better align with corporate ROI and risk management objectives. Nearly three years ago, we introduced the PADS (Performance Analytics and Decision Support) Framework as a more strategic approach to integrating next-generation performance management and security with big data analytics technologies. It established best practices for assuring user experience, reducing risk and improving decision making enabling IT Ops and security teams to rapidly respond to the myriad events that impact their operations, security, compliance and competitiveness.

Leading and next-generation vendors in adjoining spaces such as application delivery controllers (ADCs), network and application performance monitoring and management (NAPM) and security information and event management (SIEM) have been coalescing around a unified approach to secure UX.

Expect these platforms to evolve further toward operational intelligence by expanding the types of data sources they collect and correlate. They will also drive deeper into analytics, including predictive capabilities, to allow IT – and eventually, line of business users – to monitor secure UX with greater granularly.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

The Secure UX Enterprise - Part 2

Gabriel Lowy

Start with The Secure UX Enterprise - Part 1

A Unified Approach Begets Convergence and Collaboration

Unfortunately, most enterprise IT teams still monitor and manage user experience from traditional technology domain silos, such as server, network, application, device, operating system and security. As workloads continue to shift to new architecture, this approach only perpetuates an ineffective, costly and politically-charged environment. 

A unified approach allows IT teams to help their companies leverage technology investments to discover, interpret and respond to the myriad events that impact their operations, competitiveness, security and compliance.

IT Ops teams must understand their users and prioritize the performance of their apps and websites accordingly. They can make sure the apps that drive the business have the highest availability and reliability. In concert with the security team, they can take a balanced approach to prioritizing risks across the enterprise.

As opposed to conventional security layering by infrastructure, application, device and user, a prioritized risk approach allows the security team to dedicate more resources and attention to the assets that are most important to the organization. This strategy is more proactive and intelligence-based, enabling the security team to better defend the organization's most valuable data assets, respond to and remediate incidents in a timely fashion and meet GRC requirements.

Automated continuous monitoring, advanced behavioral analytics, incident response automation and software-defined perimeter provide transaction-level insights into the IT environment that UX and security teams need to better ensure performance, while protecting against risks and improving incident response. Correlations, machine learning engines, and advanced behavioral analytics and data visualization create context based on granularity about users, applications, and endpoints.

The intelligence they provide establishes benchmarks against key performance indicators (KPIs) for what is normal activity and identify anomalous behavior in real time. UX teams can triage the root cause of poor performance to speed MTTR.

Monitoring that is more pervasive, automated and intelligent allows security teams to better understand risks and prioritize threats. Policies and enforcement can be applied automatically to specific applications, user groups or roles so that security teams can use this intelligence to isolate and contain an attack before intruders can cause substantial damage.

A unified approach facilitates mapping resource and application dependencies through a single view of all components that support a service to ensure transaction completion. For security teams, it provides visibility and intelligence into the validity of the transaction and the users involved. They can see the data going into these environments, whether users are authorized to work with this data and when data is attempting to leave.

Automation provides speed and scale to keep up with new architectures and traffic growth. It improves agility and governance, reduces costs, and helps UX and security teams mitigate human error and remediate more effectively.

Next-generation solutions are all capable of collecting vast amounts of transaction data. They can then run advanced analytics against this data for a variety of secure UX use cases. To enable this type of collaboration, data also has to be assimilated from network service providers and cloud service providers in addition to data from within the enterprise.

Data Integration is Key

The better integrated these technologies are, the more intelligence UX and security teams derive from them and the more efficiently they can prioritize risks and remediation. Greater efficiency with IT Ops and security data can drive sustained competitive advantage and reduce risk at lower total cost of ownership (TCO).

Data integration is labor intensive and time consuming. IT teams get bogged down trying to integrate data from different tools. The proliferation of tools for both performance and security monitoring has resulted in a patchwork quilt of incompatible consoles and data. Teams end up spending more time writing scripts preparing data for analysis than gaining real-time insights into secure UX. And they often ignore the barrage of false positives these different tools generate.

Modern integration tools automate much of the cleansing, matching, error handling and performance monitoring that IT Ops and security teams often struggle with manually. Application governance allows teams to take a standardized approach to integrating diverse data sets, including those from SaaS applications and IaaS or PaaS clouds. Unifying disparate data points provides both IT Ops and security teams with more actionable intelligence to speed MTTR and incident response.

Conclusion

Secure UX has a domino effect across all functional areas of the organization. Users from sales, marketing and product development through manufacturing and supply chain management have more confidence in the data they are working with. The result is improved modeling and decision outcomes. At the same time, companies strengthen financial management, reduce risk and ensure adherence with governance, regulatory and compliance requirements.

IT teams must evolve toward a unified approach that promotes collaboration and efficiency to better align with corporate ROI and risk management objectives. Nearly three years ago, we introduced the PADS (Performance Analytics and Decision Support) Framework as a more strategic approach to integrating next-generation performance management and security with big data analytics technologies. It established best practices for assuring user experience, reducing risk and improving decision making enabling IT Ops and security teams to rapidly respond to the myriad events that impact their operations, security, compliance and competitiveness.

Leading and next-generation vendors in adjoining spaces such as application delivery controllers (ADCs), network and application performance monitoring and management (NAPM) and security information and event management (SIEM) have been coalescing around a unified approach to secure UX.

Expect these platforms to evolve further toward operational intelligence by expanding the types of data sources they collect and correlate. They will also drive deeper into analytics, including predictive capabilities, to allow IT – and eventually, line of business users – to monitor secure UX with greater granularly.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...