Cloud adoption has transformed the enterprise IT landscape, but that transformation has not followed a clean or linear path. Instead of standardizing on a single platform, most organizations now operate across a patchwork of public clouds, private data centers, and legacy systems. Hybrid and multi cloud strategies have become the norm, not by design but by necessity. In most cases, organizations are responding to practical demands such as legacy infrastructure that cannot be easily migrated, regulatory requirements for data locality, the complexity of merging IT environments after acquisitions, and the need to leverage the unique strengths of different cloud providers.
At the same time, artificial intelligence is reshaping how applications are built and delivered. AI capabilities are now embedded in everything from customer-facing features to internal decision support systems. These AI applications, like most modern software, are built and deployed using containers and Kubernetes. They often move through complex, multi-stage pipelines to be deployed across cloud and on-premises infrastructure.
This combination of cloud native architectures and AI innovation is powerful. It is also difficult to secure.
Fragmented Infrastructure Creates Real Risk
The benefits of hybrid and multi cloud strategies are well understood. They offer agility, cost optimization, and the ability to use best-of-breed tools. But they also lead to fragmentation. Each cloud provider has its own identity management, tooling, and compliance controls. Legacy systems introduce yet another layer of complexity. AI workloads add an entirely new surface for risk.
Security teams must now protect applications that span public cloud services, private clusters, container platforms like Red Hat OpenShift, and even IBM Z series mainframes. Some applications are deployed as containers in cloud environments. Others are containerized AI models running on high-performance infrastructure. In many cases, these systems are connected and interdependent.
What ties these workloads together is the need for consistent, real-time protection. Unfortunately, most organizations are still relying on disconnected tools and manual processes that were not designed for this level of complexity.
The New Challenge: Securing Cloud Native and AI Workloads End to End
AI is increasingly embedded into enterprise applications, and those applications are almost always containerized. In regulated industries where strict data control and high performance are critical, we expect growing adoption of AI focused on premises platforms such as IBM Z and LinuxONE. Much of the data that powers AI models already resides on the mainframe, and instead of moving that sensitive data to the cloud, enterprises are bringing AI to their data. This approach allows organizations to run containerized AI workloads directly on systems like IBM LinuxONE with Red Hat OpenShift, combining the agility of Kubernetes with the performance, resilience, and compliance of mainframe infrastructure.
But as these platforms grow in adoption, so does their risk profile. AI workloads running in containers introduce new challenges. Containers are dynamic, ephemeral, and highly interconnected, making them difficult to secure with legacy tools. Meanwhile, AI itself brings additional risk, including threats like prompt injection, model manipulation, and unauthorized access to sensitive inference data.
The result is a growing attack surface that spans both modern and traditional infrastructure. Securing this new class of cloud native AI applications requires a modern, targeted approach that can protect containerized workloads wherever they run, including on mainframes.
The Limitations of Siloed Security
Most organizations still approach security in fragments. Developers focus on secure coding and vulnerability scanning. Infrastructure teams manage cloud permissions. Security teams monitor for runtime anomalies. But in a hybrid and multi-cloud environment, this model breaks down.
Security policies are inconsistent. Visibility is incomplete. Detection happens too late. Compliance becomes reactive instead of continuous. While some organizations can maintain control through disciplined processes and tooling, the fragmented approach quickly becomes unsustainable as environments grow more dynamic. Cloud workloads shift rapidly, identities multiply, and development cycles accelerate. Siloed teams and tools cannot keep pace. As new technologies are layered on, especially containers and AI, gaps widen, and risk increases.
To close these gaps, organizations need a unified approach that treats every application and every environment as part of a single security fabric.
Harmonizing the security posture across the entire hybrid cloud builds a fabric of protection that helps keep “bad guys” from entering through the weakest link. Enterprise security management from the top down allows enterprises to achieve consistency
Complete Protection Is Now a Requirement
Modern security must protect applications from the moment code is written through to runtime in production. This includes:
- Securing the software supply chain and CI pipelines
- Applying consistent policies across clouds, clusters, and on-premises systems
- Monitoring workload behavior and enforcing protection at runtime
- Validating AI models before deployment and monitoring their use in production
- Ensuring compliance with evolving regulatory frameworks
This is not a checklist. It is a continuous process that spans environments and lifecycles. It must work just as well for containerized applications in AWS Fargate as it does for AI models executing on IBM Z series hardware. Only complete, end-to-end protection can meet this challenge.
Aqua’s Role: Securing Every Application, Everywhere
Aqua’s Platform is built for this challenge. It delivers full lifecycle protection for applications across all environments, from cloud native deployments to legacy systems.
Whether applications run in containers on Red Hat OpenShift, as serverless functions in AWS Fargate, or on IBM Z series mainframes, Aqua provides the same level of control, visibility, and protection.
Aqua also secures AI applications. This includes both those that consume large language models and those that serve them to other applications and users. Aqua applies the same consistent policies and behavioral enforcement to these workloads, reducing AI-specific risks without slowing innovation.
With Aqua, organizations can:
- Define security policies once and apply them across all environments
- Detect and stop threats in real time, no matter where they originate
- Maintain visibility and control across public cloud, private infrastructure, and industry-specific platforms
- Ensure that security is built in from development to runtime for every application
The Future Requires Security That Is Comprehensive by Design
Technology is becoming more connected, more intelligent, and more distributed. Hybrid and multi cloud strategies are now the default, and AI is accelerating that trend.
Security must evolve to match this complexity. It must be automated, scalable, and consistent across every environment. It must be able to secure traditional workloads and modern AI applications with equal precision. Aqua helps you do just that. From containers to mainframes, from cloud infrastructure to AI applications, Aqua gives you the power to secure every application, everywhere.
Get your Copy: Hybrid Cloud Multi Cloud Every Cloud Secured
