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GDPR and the Need for a Smart Approach to Service Assurance

Michael Segal

Following the introduction of the EU General Data Protection Regulation, or GDPR, on May 25 this year, organizations across the globe with customers and suppliers in the European Union have been working to ensure they are compliant, and bringing the subject of data projection to the front of everyone's mind.

It's little surprise that network security and information assurance are key to complying with the GDPR; the regulation includes a requirement for measures to be put in place that will mitigate the risk associated with assuring the availability and integrity of an organization's information in the event of an attack or outage, for example.

Article 32 is concerned with confidentiality, integrity, availability and resilience of processing systems and data, and with the speed at which availability and access to personal data can be restored in the event of downtime resulting for a breach or network outage. Of course, as the information protected by the GDPR and other similar regulations constantly traverses the network, it's important to assure its availability, reliability and responsiveness. Indeed, not only is this important for regulatory compliance, it should be high on the list of priorities for any business.

Given the size and complexity of today's IT networks, however, it can be almost impossible to detect just when and where a security breach or network failure might occur. It's critical, therefore, that businesses have complete visibility over their IT networks, and any applications and services that run on those networks, in order to protect their customers' information, assure uninterrupted service delivery and, of course, comply with the GDPR.

Insight and Intelligence

The volume of data being produced has exploded in recent years and this is only set to continue, with analysts predicting a tenfold increase within the next decade, 60 percent of which will be generated by enterprises.

Much of this will comprise what the GDPR, and other regulations such as PCI-DSS and HIPAA, define as personal data: the personal email addresses, phone numbers, IP addresses and credit card information that may be collected and recorded by a business. For compliance purposes, it's important that networking teams are able to understand how this data traverses their organization's networks, the paths it will take and where it will be stored.

Keeping track of this information requires full visibility across the entire network, including data centers, applications and the cloud. To comply with regulatory requirements around the processing of data, as well as for service and security assurance, businesses should consider a smart approach to the way they handle data. Such an approach would involve monitoring all "wire data" information, that is every action and transaction that traverses an organization's service delivery infrastructure, and continuously analyzing it and compressing it into metadata at its source. This "smart data" is normalized, organized, and structured in a service and security contextual fashion in real time. The inherent intelligence of the metadata enables analytics tools to clearly understand application performance, infrastructure complexities, service dependencies and, importantly for GDPR compliance, any threats or anomalies.

Essentially, continuous monitoring of this wire data means that businesses can have access to contextualized data that will provide them with the real-time, actionable insights they need for assurance of effective, resilient and secure infrastructure, crucial for complying with the GDPR, not to mention for much of modern business activity.

More at Stake than Ever

The recent implementation of the GDPR means that any organization that processes the personal data of UK citizens, regardless of where in the world that organization is located, is now within the scope of the law. Much has been written over the past year on the eye-watering financial penalties that could be imposed on any company found to be neglectful in fulfilling its duty to protect the privacy of that data. The privacy and protection of personal data have always been considerations for a business, but with the prospect of facing fines of up to €20 million or four percent of annual turnover, there is more at stake for businesses than ever before.

With robust protection in place, and with visibility, insight and intelligence delivering assurance of complete network availability, businesses across the world breathe a little easier that the reliability of their networks, and of the applications that run on those networks, meet the requirements of the GDPR.

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In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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 ...

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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 ...

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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.

GDPR and the Need for a Smart Approach to Service Assurance

Michael Segal

Following the introduction of the EU General Data Protection Regulation, or GDPR, on May 25 this year, organizations across the globe with customers and suppliers in the European Union have been working to ensure they are compliant, and bringing the subject of data projection to the front of everyone's mind.

It's little surprise that network security and information assurance are key to complying with the GDPR; the regulation includes a requirement for measures to be put in place that will mitigate the risk associated with assuring the availability and integrity of an organization's information in the event of an attack or outage, for example.

Article 32 is concerned with confidentiality, integrity, availability and resilience of processing systems and data, and with the speed at which availability and access to personal data can be restored in the event of downtime resulting for a breach or network outage. Of course, as the information protected by the GDPR and other similar regulations constantly traverses the network, it's important to assure its availability, reliability and responsiveness. Indeed, not only is this important for regulatory compliance, it should be high on the list of priorities for any business.

Given the size and complexity of today's IT networks, however, it can be almost impossible to detect just when and where a security breach or network failure might occur. It's critical, therefore, that businesses have complete visibility over their IT networks, and any applications and services that run on those networks, in order to protect their customers' information, assure uninterrupted service delivery and, of course, comply with the GDPR.

Insight and Intelligence

The volume of data being produced has exploded in recent years and this is only set to continue, with analysts predicting a tenfold increase within the next decade, 60 percent of which will be generated by enterprises.

Much of this will comprise what the GDPR, and other regulations such as PCI-DSS and HIPAA, define as personal data: the personal email addresses, phone numbers, IP addresses and credit card information that may be collected and recorded by a business. For compliance purposes, it's important that networking teams are able to understand how this data traverses their organization's networks, the paths it will take and where it will be stored.

Keeping track of this information requires full visibility across the entire network, including data centers, applications and the cloud. To comply with regulatory requirements around the processing of data, as well as for service and security assurance, businesses should consider a smart approach to the way they handle data. Such an approach would involve monitoring all "wire data" information, that is every action and transaction that traverses an organization's service delivery infrastructure, and continuously analyzing it and compressing it into metadata at its source. This "smart data" is normalized, organized, and structured in a service and security contextual fashion in real time. The inherent intelligence of the metadata enables analytics tools to clearly understand application performance, infrastructure complexities, service dependencies and, importantly for GDPR compliance, any threats or anomalies.

Essentially, continuous monitoring of this wire data means that businesses can have access to contextualized data that will provide them with the real-time, actionable insights they need for assurance of effective, resilient and secure infrastructure, crucial for complying with the GDPR, not to mention for much of modern business activity.

More at Stake than Ever

The recent implementation of the GDPR means that any organization that processes the personal data of UK citizens, regardless of where in the world that organization is located, is now within the scope of the law. Much has been written over the past year on the eye-watering financial penalties that could be imposed on any company found to be neglectful in fulfilling its duty to protect the privacy of that data. The privacy and protection of personal data have always been considerations for a business, but with the prospect of facing fines of up to €20 million or four percent of annual turnover, there is more at stake for businesses than ever before.

With robust protection in place, and with visibility, insight and intelligence delivering assurance of complete network availability, businesses across the world breathe a little easier that the reliability of their networks, and of the applications that run on those networks, meet the requirements of the GDPR.

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.