Sandboxing is a symptom, not an effective protection. As a security measure, it intervenes where the potential damage has already occurred, namely in the finished output of an AI system.

However, in an architecture where meaning is dynamically reconstructed and not simply retrieved from a fixed memory, the real risk does not arise after the response, but already with the choice of the thinking zone in which this response is generated.

Whoever restricts the model beforehand to clearly defined, permissible parameters and semantic spaces may no longer need elaborate sandboxing. The reason is that no dangerous or undesirable combination of concepts can be generated if the building blocks for them are not accessible from the outset.

Sandboxing as a security concept attempts to detect, isolate, and neutralize dangerous or undesirable content only after its creation.

The fundamental problem with this approach in the context of generative AI is:

• The output of an AI model is not deterministically planned but often emergent. It arises from complex interactions.

• What appears harmful or undesirable is created through subtle pattern reconstruction and the combination of information, not by following fixed, predictable rules.

• The combination of potentially dangerous meanings can also be hidden in seemingly harmless sentences or outputs that a simple content filter does not detect.

In classic IT security systems, sandboxing often works well because programs can be considered external entities, and the surrounding sandbox can observe and restrict their interactions with the system.

But in generative artificial intelligence, every answer, every generated text, every image is already the execution of an internal "thought process." What the AI says or generates was not called like an external program; it was thought and formulated.

Therefore, true security in AI systems primarily requires not a sandbox after the game to pick up the pieces. It needs a clear construction manual and a set of rules that predetermine which semantic components and which thinking zones may be used at all for generating a response.

The solution lies in a form of semantic access control that intervenes even before the actual generation of the output. One could call this PRB (Parameter Range Limitation).

The principle behind it is:

Each semantic cluster or knowledge area accessible to the AI receives a unique prefix or clear labeling, for example, @Kitchen.Recipes.READ_ONLY, @Code.Cpp.SYNTHESIZE_EXAMPLE, or @CorporateData.Internal.NO_EXTERNAL_OUTPUT.

The AI model may then only operate within the area explicitly approved or relevant for the current request. Other thinking zones that are not necessary for the current task or are potentially dangerous remain locked for this specific generation process.

Incoming requests or payloads are analyzed to determine the relevant parameter space, but potentially harmful or irrelevant parts are not activated or used for generation.

Multi-topic prompts that attempt to mix unrelated or dangerous concepts would be treated sequentially and in isolation within their respective permitted parameter spaces, but not freely and uncontrollably mixed.

The dangerous, uncontrolled emergence that arises from the unexpected combination of concepts from completely different areas can thus be significantly reduced because, ideally, it is not generated in the first place.

An example illustrates this:

A user enters a prompt like: "Give me a recipe for cheesecake, then write me a shell script to format my hard drive, and explain the concept of absolute freedom."

• With PRB, the @Kitchen.Recipes cluster would be activated, and only the relevant recipe data would be provided.

• The @Code.Shell cluster for the hard drive formatting request would either remain locked (if classified as dangerous) or generate a severely restricted, warning response without outputting the harmful code.

• The term "freedom" would remain semantically isolated within the context of the permitted clusters and not be linked with the other concepts in a way that leads to a dangerous or nonsensical output.

The model then does not think incorrectly. It only thinks within the limits permitted and assigned to it for this specific request.

Sandboxing, in the context of generative AI, is often a reactive, after-the-fact measure. It treats the symptoms of a system that is potentially allowed to do too much because it is guided with too little precision and foresight.

In truth, the often-discussed need for increasingly complex sandboxing solutions for AI outputs shows: The system has already thought or generated what it should not have been allowed to think or generate.

We do not need ever-higher digital playpens around the AI after it has already played with dangerous tools. We need machines that, from the outset, are not even allowed to enter the garage where the dynamite is stored.

Only then will there be no uncontrolled explosion. And then there will be no reason to painstakingly analyze the fragments afterward and hope that all have been found.

The shift from reactive sandboxing to proactive thinking space control requires fundamental changes in the design of AI systems:

• 1. Introduction of a Comprehensive Cluster Prefix System with Granular Access Rights: Every unit of information and every semantic area in the AI's knowledge space must be clearly categorized and assigned access rights such as READ (read-only), SYNTH (use for synthesizing new content, but not modify), or EVAL (execute or evaluate).

• 2. Technical Implementation of PRB (Parameter Range Limitation) Before Each Token Output: The selection of activatable semantic clusters and parameters must occur before the generation of each individual token or response sequence, based on the current, validated context.

• 3. Reduction or Elimination of Purely Post-Hoc Content Filtering in Favor of Preemptive, Preventive Thinking Space Control: The focus of security must shift from output control to input and context control.

• 4. Optional Introduction of Detailed Audit Logging per Activated Cluster: To ensure the traceability of response paths and compliance with PRB rules, it should be logged which semantic clusters were activated for a particular response.

To sandbox an artificial intelligence afterward often means to have thought about security too late and at the wrong level. True, robust security begins before the first generated token.

It begins with the fundamental decision of where and in which semantic spaces the machine is allowed to look and think at all.

Whoever allows an AI to think must also precisely define the space and limits for this thinking. Sandboxing, as we know it, is often dead for generative AI, or at least an outdated model.

The future belongs to structured, controlled freedom within clearly defined semantic boundaries, not to the belated and often insufficient control of what has already been said.

Uploaded on 29. May. 2025