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Gartner Says Organizations More Likely to Use SaaS for Sensitive Data Than Mission-Critical Data

Avoiding the use of software as a service (SaaS) for critical or sensitive data remains a significant form of risk control for many organizations, according to Gartner, Inc. But those that do use SaaS for such data are more likely to use it for sensitive data than for mission-critical data.

These findings are based on Gartner's latest annual survey of the state of risk management programs globally, which questioned 425 respondents from IT risk management disciplines in the U.S., U.K., Germany and Canada from December 2011 to January 2012.

The survey results show that organizations take different approaches to risk management when confronted with a need or opportunity to share data with different types of external party.

Assessment Practices for External Parties

Survey respondents were asked if they had processes in place to assess external party security, risk management, compliance, privacy and BCP/DR for four different situations. Respondents answered: “Do not allow use for sensitive data or processes" almost twice as often in the case of business partners (38 percent) as for platform as a service (PaaS) and infrastructure as a service (IaaS) (20 percent).

Compared with PaaS/IaaS, organizations are about 30 percent more likely to have a policy against putting sensitive data into SaaS (26 percent), and about 45 percent more likely to have a policy against putting it into outsourced data centers (29 percent).

"These results make sense, given that sharing data with a partner almost certainly means that one or more of its employees will be accessing the data, while in a SaaS scenario, the data is typically only accessible to the primary customer," said Jay Heiser, Research VP at Gartner. "This year we asked about both data availability and data confidentiality policies. Survey respondents indicated 10 percent less willingness to place mission-critical data into a SaaS offering than to place sensitive data into it. They were even less willing to place mission-critical data into outsourced data centers, with over one-third of respondents saying that they do not allow it."

Platform-as-a-Service/Infrastructure-as-a-Service Risk Assessment Practices

Only 57 percent of IaaS/PaaS buyers are using a questionnaire to support their risk assessment, and unlike for SaaS, the questionnaire is more likely to be a proprietary one, unique to the buyer's organization, and less likely to be based on standards. As in the case of SaaS, 26 percent are also evaluating information from the provider. The most dramatic change over the past three years is the increased willingness to use IaaS and PaaS for sensitive processes.

Outsourced Data Center Risk Assessment Practices

Thirty-six percent of respondents said they had a policy against putting mission-critical data into an outsourced data center, making avoidance the most chosen mechanism for dealing with data center risk. The level of response for this choice is significantly higher than for either of the other two service models. Twenty-nine percent said this policy applied to SaaS, and only 22 percent said it applied to IaaS/PaaS.

"One of the biggest drivers is probably an expectation that the packaged service offerings, which typically claim to be based on cloud computing, are more reliable," said Mr Heiser. "While fault tolerance is a feature of many such offerings, we consider it premature to assume that mission-critical data is safer in a cloud than in a traditional data center in which buyers usually make very specific choices about how data will be backed up."

The most significant reduction in the use of risk assessment practices has been in the practice of sending company staff to evaluate a partner's controls on-site, which has dropped by over 40 percent over three years. Use of standards-based questionnaires has increased, while the use of proprietary surveys has dropped by the same degree, leaving the prevalence of questionnaires virtually the same.

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Gartner Says Organizations More Likely to Use SaaS for Sensitive Data Than Mission-Critical Data

Avoiding the use of software as a service (SaaS) for critical or sensitive data remains a significant form of risk control for many organizations, according to Gartner, Inc. But those that do use SaaS for such data are more likely to use it for sensitive data than for mission-critical data.

These findings are based on Gartner's latest annual survey of the state of risk management programs globally, which questioned 425 respondents from IT risk management disciplines in the U.S., U.K., Germany and Canada from December 2011 to January 2012.

The survey results show that organizations take different approaches to risk management when confronted with a need or opportunity to share data with different types of external party.

Assessment Practices for External Parties

Survey respondents were asked if they had processes in place to assess external party security, risk management, compliance, privacy and BCP/DR for four different situations. Respondents answered: “Do not allow use for sensitive data or processes" almost twice as often in the case of business partners (38 percent) as for platform as a service (PaaS) and infrastructure as a service (IaaS) (20 percent).

Compared with PaaS/IaaS, organizations are about 30 percent more likely to have a policy against putting sensitive data into SaaS (26 percent), and about 45 percent more likely to have a policy against putting it into outsourced data centers (29 percent).

"These results make sense, given that sharing data with a partner almost certainly means that one or more of its employees will be accessing the data, while in a SaaS scenario, the data is typically only accessible to the primary customer," said Jay Heiser, Research VP at Gartner. "This year we asked about both data availability and data confidentiality policies. Survey respondents indicated 10 percent less willingness to place mission-critical data into a SaaS offering than to place sensitive data into it. They were even less willing to place mission-critical data into outsourced data centers, with over one-third of respondents saying that they do not allow it."

Platform-as-a-Service/Infrastructure-as-a-Service Risk Assessment Practices

Only 57 percent of IaaS/PaaS buyers are using a questionnaire to support their risk assessment, and unlike for SaaS, the questionnaire is more likely to be a proprietary one, unique to the buyer's organization, and less likely to be based on standards. As in the case of SaaS, 26 percent are also evaluating information from the provider. The most dramatic change over the past three years is the increased willingness to use IaaS and PaaS for sensitive processes.

Outsourced Data Center Risk Assessment Practices

Thirty-six percent of respondents said they had a policy against putting mission-critical data into an outsourced data center, making avoidance the most chosen mechanism for dealing with data center risk. The level of response for this choice is significantly higher than for either of the other two service models. Twenty-nine percent said this policy applied to SaaS, and only 22 percent said it applied to IaaS/PaaS.

"One of the biggest drivers is probably an expectation that the packaged service offerings, which typically claim to be based on cloud computing, are more reliable," said Mr Heiser. "While fault tolerance is a feature of many such offerings, we consider it premature to assume that mission-critical data is safer in a cloud than in a traditional data center in which buyers usually make very specific choices about how data will be backed up."

The most significant reduction in the use of risk assessment practices has been in the practice of sending company staff to evaluate a partner's controls on-site, which has dropped by over 40 percent over three years. Use of standards-based questionnaires has increased, while the use of proprietary surveys has dropped by the same degree, leaving the prevalence of questionnaires virtually the same.

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Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

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Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...