Skip to main content

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

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...

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

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...