Week 8 + 9: Completed Demonstration and Model Evaluation

These last few weeks at MITRE the main focus was completing the encrypted vs unencrypted side by side demonstration. Both virtual machines are now fully set up, one running with AMD SEV-SNP enabled for hardware level memory encryption and one with no encryption. Both VMs run the same Llama 3.2 model through the chat interface we built. The demo is now fully working end to end. Using QEMU’s management socket we can freeze either VM, dump its memory to a file, and search through it. On the unencrypted machine the conversation comes back in plain text exactly as the user typed it. On the encrypted machine the same attack returns nothing (the dump completes but the contents are completely unreadable ciphertext thanks to SEV-SNP encrypting RAM at the hardware level).

I also finished preparing the presentation for the end of spring internship at MITRE. The presentation walks through what the demonstration shows, why memory-level vulnerabilities matter, and how hardware encryption solves the problem.

On the research side, I finished running the Random Forest models and got the evaluation results this week. The models perform significantly better than the logistic regression baseline, which confirms the idea that AI adoption follows complex patterns that simple statistics can miss. I’m now working on analyzing the feature importance rankings to see which variables matter most for each business size group. The comparison between micro-businesses and larger SMEs is leaning towards showing what I expected: technology factors matter more for the smallest businesses while organizational factors are much more important for larger ones. I’m also starting to add more to the results and analysis section for the final paper now that I have more leads.