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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

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

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

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...