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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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

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

As businesses increasingly rely on high-performance applications to deliver seamless user experiences, the demand for fast, reliable, and scalable data storage systems has never been greater. Redis — an open-source, in-memory data structure store — has emerged as a popular choice for use cases ranging from caching to real-time analytics. But with great performance comes the need for vigilant monitoring ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...