Your company rolls out a new claims system powered by AI. It is fast, efficient, and customers love the experience. Then one day, a single malicious input slips through. The model is tricked into revealing sensitive records, or a workload spins out of control and consumes expensive GPU cycles. What began as a breakthrough quickly becomes a security incident.
This scenario is becoming increasingly common as AI shifts from experiments to production. Organizations are embedding AI directly into the applications they build and run in their own environments. Hosting these workloads locally or in private clouds gives greater control over data and performance, but it also introduces risks that are far less predictable than traditional software.
The Gap Between Generic Models and Business Needs
The difference between a helpful assistant and a frustrating liability comes down to context. Most widely available models today have been trained on vast amounts of public data. They are powerful but detached from the specific business scenarios where companies want to use them.
Imagine asking your banking app to transfer funds, only to receive a polite explanation of how transfers usually work. Without the context of who you are, what permissions you hold, and which account to use, the model cannot execute the task.
Business applications require models that understand the user, the data, and the policies that govern every transaction. They need boundaries that define what an AI component is allowed to do and safeguards that prevent it from stepping outside those lines.
The Technology Stack Matters
Delivering that context is not the job of the model alone. It depends on a chain of components that work together:
- Client and API gateway: Authenticate the user and enforce access policies
- Context service: Gathers additional information from internal systems, such as account data or business rules, and attaches it to the request
- Inference service: Manages the interaction with the model and any supporting tools or agents
- Model and GPU resources: The model processes the request with the compute capacity required for complex queries
Each of these layers is more than plumbing. Together, they transform a generic model into a business-aware system capable of executing real transactions rather than offering generic advice. Much of this infrastructure now lives on premises or in private cloud environments. It is built on containers, orchestrated by Kubernetes, and closely tied to the organization’s application fabric.
That means AI security is inseparable from container security. A misconfigured cluster, an unscanned image, or an unchecked GPU workload can create ripple effects that AI only amplifies.
New Risks in Familiar Places
The move to AI does not erase the old risks of cloud native infrastructure, it multiplies them. A prompt injection can trick a model into revealing sensitive information or executing an action it should never perform. Data can leak between tenants if boundaries are weak. SDKs can hide AI use inside applications without security teams knowing it is there.
Even supply chain risks grow sharper. External models and components are often introduced into local systems without the same scrutiny given to traditional software. For example, something as simple as uploading a document can become a potential attack vector when malicious inputs can alter how the model responds.
Unlike traditional applications, AI does not always behave predictably. That unpredictability means the old perimeter defenses are no longer enough. Security teams need visibility into what AI workloads are doing in real time, and they need controls built for the unique ways prompts, models, and tools interact.
“Need to demand that security is added as AI moves to production because otherwise you’re going to be exposed.”
Best Practices for Protecting AI Workloads On-Premises
Securing AI workloads starts with a shift in approach. These applications are not side projects or experiments. They are business-critical, and they must be governed with the same discipline as any other system.
That begins with knowing what models are in use, where they came from, and how they are configured. It means scanning every image and SDK, enforcing least privilege, and monitoring workloads continuously for drift. But it also means adopting protections unique to AI. Prompts and responses need to be visible and analyzed. Guardrails must block jailbreak attempts or emotionally manipulative prompts designed to push the model outside its boundaries. GPU use must be controlled so a single workload does not consume resources at the expense of the rest of the environment. And when incidents occur, organizations need forensic data that allows them to investigate and learn from what went wrong.
Why On-Premises AI Security Cannot Wait
The line between experiment and production is disappearing faster than many security teams realize. AI is already finding its way into applications that handle sensitive data and serve critical business functions. Running those systems locally or on premises gives enterprises control, but it also gives them responsibility.
The organizations that succeed will be the ones that act now to bring visibility, governance, and runtime protection into their AI environments. Those who wait will find themselves facing unpredictable risks with limited defenses.
Aqua Secures Every Cloud Native Application Everywhere
Aqua Secure AI extends the Aqua Platform’s proven protection for containers and cloud native applications to the emerging world of AI workloads. With a lightweight eBPF-based Enforcer, Aqua discovers AI usage across applications, captures prompts while stripping sensitive data, and applies runtime guardrails to block jailbreaks and misuse. It connects supply chain scanning with runtime activity to give security teams full lifecycle coverage.
As enterprises bring AI from experiment to production, Aqua helps them secure it as part of the same fabric that already protects their cloud native environments.
Watch the webinar to learn how to secure AI where you build and run it.
