Secure LLM Usage
Using large language models for enterprise tasks without exposing original sensitive data to external AI services. Enabled by LLM Capsule.
Explanation
Secure LLM usage is distinct from model-level security measures like prompt filtering or output scanning. Those approaches monitor the interaction with the AI model but do not prevent the data itself from being transmitted. Secure LLM usage operates at the data layer — transforming what the AI receives so that sensitive information never reaches the model.
This approach is model-agnostic. Whether the enterprise uses ChatGPT, Claude, Gemini, Perplexity, or any other LLM API, the AI enablement data layer remains consistent because it operates before the data reaches any model — enabling cross-model execution from a single AI enablement layer.
Example
A legal team uses Claude to analyze contract clauses across 100 vendor agreements. Each agreement contains proprietary pricing, vendor names, and internal project codes. Secure LLM usage means Claude receives structurally intact contracts with protected values — it can perform clause analysis accurately, but never receives the original vendor names or pricing figures. Outputs are restored locally for the legal team.
Enable Secure LLM Usage for Your Enterprise
Process sensitive data through any LLM without exposure. Experience the AI enablement data layer.
Enterprise AI Enablement by CUBIG
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 148 16" xmlns="http://www.w3.org/2000/svg"><path d="M 11.102 15.765 L 0 15.765 L 0 0.225 L 3.59 0.225 L 3.59 12.61 L 11.102 12.61 L 11.102 15.766 Z M 24.19 15.765 L 13.086 15.765 L 13.086 0.225 L 16.676 0.225 L 16.676 12.61 L 24.188 12.61 L 24.188 15.766 Z M 26.177 0.226 L 29.85 0.226 L 34.324 10.366 L 38.798 0.226 L 42.472 0.226 L 42.472 15.765 L 38.882 15.765 L 38.882 7.361 L 35.526 14.844 L 33.13 14.844 L 29.774 7.36 L 29.774 15.764 L 26.184 15.764 L 26.184 0.225 L 26.177 0.225 Z M 57.981 0 C 60.649 0 62.947 1.352 64.375 3.556 L 63.362 4.334 C 62.01 2.341 60.09 1.27 57.99 1.27 C 54.4 1.27 51.785 4.139 51.785 8.012 C 51.785 11.886 54.453 14.74 58.058 14.74 C 60.219 14.74 62.138 13.698 63.499 11.674 L 64.467 12.452 C 63.068 14.626 60.694 16 58.056 16 C 53.741 16 50.469 12.565 50.469 8.011 C 50.469 3.458 53.696 0 57.996 0 Z M 65.483 15.765 L 71.62 0.225 L 73.335 0.225 L 79.472 15.765 L 78.142 15.765 L 76.562 11.793 L 68.392 11.793 L 66.812 15.765 L 65.482 15.765 Z M 76.079 10.57 L 72.466 1.456 L 68.869 10.57 Z M 81.833 15.765 L 81.833 0.225 L 87.872 0.225 C 90.706 0.225 92.55 2.008 92.55 4.711 C 92.55 7.414 90.706 9.151 87.872 9.151 L 83.14 9.151 L 83.14 15.765 L 81.832 15.765 Z M 83.141 7.92 L 87.811 7.92 C 90.048 7.92 91.212 6.651 91.212 4.718 C 91.212 2.785 90.049 1.464 87.812 1.464 L 83.14 1.464 L 83.14 7.92 Z M 95.465 12.028 C 96.553 13.81 98.299 14.739 100.196 14.739 C 102.093 14.739 103.891 13.697 103.891 11.96 C 103.891 10.224 102.607 9.499 99.757 8.495 C 96.583 7.37 94.973 6.275 94.973 4.077 C 94.973 1.389 97.467 0 99.773 0 C 102.304 0 103.99 1.314 104.821 2.71 L 103.808 3.488 C 102.924 2.008 101.496 1.268 99.772 1.268 C 98.049 1.268 96.296 2.243 96.296 4.048 C 96.296 5.489 97.528 6.328 100.158 7.218 C 103.952 8.51 105.214 9.71 105.214 11.938 C 105.214 14.573 102.818 16 100.196 16 C 97.785 16 95.54 14.784 94.49 12.806 L 95.457 12.028 Z M 108.278 0.226 L 109.578 0.226 L 109.578 10.102 C 109.578 12.835 111.475 14.715 114.528 14.715 C 117.581 14.715 119.478 12.85 119.478 10.102 L 119.478 0.226 L 120.778 0.226 L 120.778 10.102 C 120.778 13.636 118.398 15.999 114.528 15.999 C 110.659 15.999 108.278 13.629 108.278 10.102 Z M 124.785 15.765 L 124.785 0.225 L 126.092 0.225 L 126.092 14.512 L 135.056 14.512 L 135.056 15.772 L 124.785 15.772 Z M 137.67 0.226 L 147.329 0.226 L 147.329 1.464 L 138.978 1.464 L 138.978 7.097 L 146.188 7.097 L 146.188 8.343 L 138.978 8.343 L 138.978 14.534 L 147.571 14.534 L 147.571 15.773 L 137.67 15.773 Z" fill="rgb(35, 31, 32)" height="16px" id="o43yy6bJZ" width="147.57099816894532px"/></svg>)