Introduction
Engineering simulations often involve highly sensitive information, from product designs and prototypes to research data that may shape future markets.
For that reason, the question of whether the cloud can be trusted with such workloads is a common one. Concerns typically center on security, compliance, and the protection of intellectual property.
These concerns are understandable. Moving data outside of company-owned infrastructure feels like a shift in control, and organizations want to be certain that their information is handled appropriately.
At the same time, it is useful to recognize that the cloud is not inherently less secure than on-premise systems. In many cases, cloud platforms apply protections in a more standardized and systematic way.
The purpose of this article is to explain what cloud security means in practice, how it compares to traditional on-premise approaches, and what factors organizations should consider when evaluating whether the cloud is suitable for their simulations.
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Survey results: Security and cost top the list of concerns organizations associate with cloud computing (Quanscient Multiphysics Simulation Survey, March 2025).
What security in the cloud means
Cloud security refers to the set of technologies and practices used to protect data, applications, and infrastructure hosted on cloud platforms.
At its core, this involves three main areas: how data is stored and transmitted, how access is controlled, and how compliance with regulations is maintained.
- Data is typically encrypted both when it is saved (“at rest”) and when it moves between systems (“in transit”).
- Access to cloud resources is managed through identity and access management tools, which often include multi-factor authentication and role-based permissions.
- Most major providers also undergo independent audits and maintain certifications such as ISO 27001, SOC 2, and GDPR compliance, which require continuous monitoring and reporting.
This combination of speed and scale means that simulation shifts from validating a few ideas at the end of the cycle to guiding the entire R&D process from the start.
Taken together, these measures form the baseline of what “cloud security” means in practice.
While implementation details vary by provider, the common principle is that protections are standardized and applied at scale, rather than handled ad hoc within individual organizations.How cloud security compares to on-premise
Decisions are based on far broader evidence, risks are reduced, and opportunities that would otherwise remain hidden can be uncovered.
How cloud security compares to on-premise
When evaluating cloud security, it helps to compare it with the kinds of protections typically found in on-premise environments.
In many engineering teams, simulations run on local clusters, shared workstations, or individual machines. Security in these setups often depends on internal IT resources, which may vary widely between organizations in terms of staffing, processes, and investment.
Cloud providers, by contrast, operate at scale. They maintain dedicated security teams, apply consistent policies, and invest in infrastructure that is continuously monitored and updated.
This allows them to offer features such as automated patching, intrusion detection, and region-level redundancy that can be difficult for smaller teams to replicate.
That does not mean on-premise systems are inherently insecure. Many organizations implement strong local protections and value the direct control they provide.
The main difference is that cloud security is standardized and audited across very large environments, while on-premise security is tailored to the resources and priorities of each individual organization.
Protecting intellectual property
For engineering organizations, one of the most important considerations is how the cloud handles intellectual property. Simulation data often represents years of research and development, and safeguarding it is a priority.
In cloud environments, intellectual property is typically protected through several layers of control.
- Data is encrypted both when stored and when transferred, making it difficult to intercept or read without the proper keys.
- Access is managed through identity frameworks that allow administrators to define who can view, modify, or share specific resources.
- Detailed audit logs are often available, which provide transparency into when data is accessed and by whom.
On-premise systems can offer a sense of direct control, since the infrastructure is physically located within the organization. However, they can also be more dependent on local practices.
For example, data might be exchanged using shared drives, email attachments, or portable media, each of which can introduce risk if not carefully managed.
Cloud platforms centralize these processes and provide more standardized safeguards, though they also require trust in the provider’s policies and certifications.
In practice, the level of protection depends not only on the provider’s technology but also on how the organization manages its own access rules and processes.
Reliability and resilience
Security is not only about preventing unauthorized access. It also involves ensuring that systems remain available when they are needed.
For engineering simulations, long runtimes and large-scale computations make reliability especially important. Unexpected downtime can delay projects and increase costs.
Cloud platforms are generally designed with redundancy in mind.
Data and workloads can be replicated across multiple servers or even across different geographic regions. If one component fails, another can take over with minimal disruption.
This kind of built-in resilience is often difficult to achieve with on-premise systems, where redundancy may require significant investment in hardware and staff to maintain it.
On the other hand, cloud services are dependent on internet connectivity and the reliability of the provider’s infrastructure.
While outages are rare, they can occur, and organizations need to plan for how they would respond.
Similarly, on-premise systems can also fail, sometimes for longer periods if replacement parts or specialized expertise are required.
Overall, resilience in the cloud tends to come from scale and distribution, while resilience on-premise depends more on the resources an organization is able to dedicate to redundancy and maintenance.
| Area | On-Premise | Cloud |
|---|---|---|
| Control | Full control of hardware and policies |
Control of data and settings; provider manages infrastructure |
| Security resources | Limited to internal IT capacity |
Dedicated provider teams monitor and update systems 24/7 |
| Data protection | Encryption and access must be set up locally |
Encryption and access controls are built-in and audited |
| Resilience and redundancy | Redundancy requires extra hardware and cost |
Failover and backups are built into provider systems |
| Compliance | Organization must maintain certifications |
Providers offer certifications; customers ensure usage compliance |
| Costs | High upfront and ongoing maintenance |
Pay-as-you-go; no hardware to maintain |
| Scalability | Bound by hardware and license limits |
Virtually unlimited scalability; resources can be provisioned on demand |
Shared responsibility
Cloud security is typically described as a “shared responsibility.” This means that some aspects are handled by the cloud provider, while others remain with the customer using the service.
The cloud provider (for example AWS, Azure, or GCP) is responsible for securing the physical infrastructure: data centers, networks, and the hardware on which the services run.
They ensure that servers are patched, facilities are monitored, and the service environment itself is protected from external threats.
The customer (e.g. Quanscient) is responsible for how its product is built and operated on top of that infrastructure. This includes how simulation data is encrypted, how authentication and access are managed, and how regulatory requirements are met.
Finally, end users also play a role. They control who within their organization can access which projects, how passwords and two-factor authentication are used, and how internal policies for data handling are applied.
This layered model makes clear that security is not the responsibility of a single party. It depends on the provider’s infrastructure, the vendor’s implementation, and the customer’s own internal practices all working together.
Quanscient’s security practices
In addition to the general protections provided by cloud infrastructure, Quanscient has implemented its own measures to safeguard engineering simulation data.
These measures address data protection, access control, compliance, and day-to-day operations.
Data protection
All customer data is encrypted both in transit and at rest, including object storage, temporary files, and databases. Encryption keys are managed through a Key Management System (KMS), which prevents direct access to keys by any individual, including Quanscient staff. TLS 1.2 or higher is used for all transmissions across potentially insecure networks, and SSL certificates are renewed regularly.
Authentication and access control
Allsolve uses strong authentication, with robust password requirements and optional two-factor authentication. Permissions are defined at a granular level so users only have access to the resources they require.
Compliance and privacy
Quanscient is ISO 27001:2022 and SOC 2 Type II certified. Personally identifiable information is stored separately to simplify retrieval or removal upon request. Data is stored in the customer’s own region, such as EU data being stored within the EU, and practices are aligned with GDPR and other applicable regulations.
Operational security
Day-to-day operations follow strict security standards. All company devices are equipped with endpoint protection, disk encryption, and automatic updates. Employee security training is conducted annually, and a cross-functional Product Security Team monitors risks and shares information across the organization. Vendor risk is also assessed carefully, with compliance checks before adopting new services.
These practices are designed to complement the broader cloud security model, ensuring that multiphysics simulation data is handled with consistent and auditable safeguards.
Read more and ask about our security practices on our security page.
Conclusion
Whether simulations are run in the cloud or on-premise, security remains a central concern.
The cloud provides standardized protections such as encryption, access control, and compliance auditing, while on-premise systems offer a sense of direct control that some organizations prefer.
In practice, security depends on the combination of infrastructure safeguards, vendor practices, and how individual teams manage access and processes.
The shared responsibility model highlights that no single party carries the entire burden.
Cloud providers secure the underlying infrastructure, vendors like Quanscient ensure that their applications are designed and operated securely, and end users manage how access is granted and used within their own organizations.
Quanscient’s security framework builds on this model by implementing encryption, strong authentication, independent certifications, regional data storage, and operational safeguards such as employee training and vendor risk assessment.
These measures are designed to ensure that engineering simulation data is handled consistently and in line with recognized best practices.
Ultimately, the decision about whether to trust the cloud with simulations comes down to understanding how these different layers work together.
By evaluating both the general principles of cloud security and the specific practices of vendors, organizations can make informed choices about how best to protect their intellectual property and maintain confidence in their R&D processes.
Learn more about Quanscient and get in touch now at quanscient.com
