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Managing Cloud Sovereignty and Security in a Multi-Cloud World

Todd Moore
Thales

In 2023, cloud computing has become commonplace across tech and non-tech organizations around the world. Over the past two years, it has cut out the middleman — expensive local infrastructure needed for computer applications — and has accelerated the growth of some of the most significant recent technological advances, including driving major improvements in artificial intelligence (AI), the Internet of Things (IoT), and remote and hybrid working.

But are organizations prepared for public clouds?


According to the 2023 Thales Cloud Security Study, which surveyed nearly 3000 respondents across 18 countries, over three-quarters of respondents now rely on more than one cloud provider. The average organization uses 2.26 infrastructure providers, a 35% increase since 2021. In 2021, 16% of respondents' organizations used 51-100 SaaS applications; by 2023, this percentage increased to 22% for 97 average SaaS applications.

So what's the problem?

Multi-cloud adoption is becoming increasingly complex — and most organizations are failing to keep up.

Cloud complexity

Multicloud adoption has brought operational complexity, making the management and security of data in the cloud more difficult. Between 2021–2023, the number of respondents feeling the effects of this complexity arose from 46% to 55%.

This makes more sense when you consider the way organizations choose to store encryption keys. Only 14% of respondents said they controlled all their encryption keys in their cloud environment, while nearly two-thirds said they used five or more key management systems, which is difficult to believe from a complexity perspective. It's no wonder organizations are struggling to keep track of their data.

Cloud data concerns

There is more data stored in the cloud than ever. However, more interesting is how much more sensitive data organizations are storing in the cloud. Three-quarters of respondents report that 40% of their data is sensitive, a 16% increase since 2021.

However, many organizations still aren't giving cloud security the necessary attention. Less than half of their overall cloud data is encrypted, and only 22% of respondents reported that more than 60% of their cloud data is encrypted. It's undeniable that organizations need to improve their cloud security.

The cloud threat landscape

The cloud threat landscape is not immune to today's cybersecurity concerns. The number of organizations that experienced a breach in the past grew 4% from 2021 (35%) to now (39%). Even more concerning, nearly half of respondents said they had experienced a data breach in their cloud environment.

Data sovereignty challenges

Cloud data sovereignty is the concept that data stored in the cloud is subject to the laws and regulations of the country or other jurisdiction. While data sovereignty represents an opportunity for organizations to undergo digital transformation, it also brings significant cloud security challenges. More than 80% of respondents said they were "somewhat" or "very" concerned about data sovereignty impacts on their cloud deployments.

Looking Forward

Organizations shifting to cloud computing must move away from complexity and embrace easy-to-manage cloud services and data protection. It is also imperative that they account for the human factor, i.e., human errors and misconfigurations that risk cloud and company security. The cloud is an extension of your infrastructure and must be treated this way if organizations intend to optimize its security and value. As companies use more cloud services and store more sensitive data in the cloud, they will gain operational and financial advantages and thrive in a competitive market.

Todd Moore is VP Data Security Products at Thales

Hot Topics

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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 ...

Managing Cloud Sovereignty and Security in a Multi-Cloud World

Todd Moore
Thales

In 2023, cloud computing has become commonplace across tech and non-tech organizations around the world. Over the past two years, it has cut out the middleman — expensive local infrastructure needed for computer applications — and has accelerated the growth of some of the most significant recent technological advances, including driving major improvements in artificial intelligence (AI), the Internet of Things (IoT), and remote and hybrid working.

But are organizations prepared for public clouds?


According to the 2023 Thales Cloud Security Study, which surveyed nearly 3000 respondents across 18 countries, over three-quarters of respondents now rely on more than one cloud provider. The average organization uses 2.26 infrastructure providers, a 35% increase since 2021. In 2021, 16% of respondents' organizations used 51-100 SaaS applications; by 2023, this percentage increased to 22% for 97 average SaaS applications.

So what's the problem?

Multi-cloud adoption is becoming increasingly complex — and most organizations are failing to keep up.

Cloud complexity

Multicloud adoption has brought operational complexity, making the management and security of data in the cloud more difficult. Between 2021–2023, the number of respondents feeling the effects of this complexity arose from 46% to 55%.

This makes more sense when you consider the way organizations choose to store encryption keys. Only 14% of respondents said they controlled all their encryption keys in their cloud environment, while nearly two-thirds said they used five or more key management systems, which is difficult to believe from a complexity perspective. It's no wonder organizations are struggling to keep track of their data.

Cloud data concerns

There is more data stored in the cloud than ever. However, more interesting is how much more sensitive data organizations are storing in the cloud. Three-quarters of respondents report that 40% of their data is sensitive, a 16% increase since 2021.

However, many organizations still aren't giving cloud security the necessary attention. Less than half of their overall cloud data is encrypted, and only 22% of respondents reported that more than 60% of their cloud data is encrypted. It's undeniable that organizations need to improve their cloud security.

The cloud threat landscape

The cloud threat landscape is not immune to today's cybersecurity concerns. The number of organizations that experienced a breach in the past grew 4% from 2021 (35%) to now (39%). Even more concerning, nearly half of respondents said they had experienced a data breach in their cloud environment.

Data sovereignty challenges

Cloud data sovereignty is the concept that data stored in the cloud is subject to the laws and regulations of the country or other jurisdiction. While data sovereignty represents an opportunity for organizations to undergo digital transformation, it also brings significant cloud security challenges. More than 80% of respondents said they were "somewhat" or "very" concerned about data sovereignty impacts on their cloud deployments.

Looking Forward

Organizations shifting to cloud computing must move away from complexity and embrace easy-to-manage cloud services and data protection. It is also imperative that they account for the human factor, i.e., human errors and misconfigurations that risk cloud and company security. The cloud is an extension of your infrastructure and must be treated this way if organizations intend to optimize its security and value. As companies use more cloud services and store more sensitive data in the cloud, they will gain operational and financial advantages and thrive in a competitive market.

Todd Moore is VP Data Security Products at Thales

Hot Topics

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 ...