I'm the founder of Lorica Cybersecurity, where I’m building infrastructure for secure, privacy-preserving AI — combining modern model development with encrypted computation and scalable deployment workflows.
🔐 I focus on:
- Confidential computing, FHE, and other privacy-enhancing technologies (PETs)
- Secure, scalable AI infrastructure design
I'm early in my open-source journey and currently laying the groundwork for several upcoming projects, including:
- Secure LLM training and inference pipelines using confidential computing (e.g., AMD SEV-SNP, Azure NCCadsH100v5)
- Infrastructure-as-code (Terraform + Vault + Kubernetes) for privacy-first AI deployments
- Experimental benchmarks evaluating performance tradeoffs of FHE, TEE, and other PETs in real-world AI workflows
I’ll begin publishing code and write-ups here as components mature.
AI will shape the future — but only systems designed with built-in security and privacy will be trusted.
At Lorica, I’m working to make secure-by-default AI practical for real-world deployment in high-stakes environments like healthcare, defense, and financial services.
I’m actively looking to connect with researchers and engineers working on:
- Encrypted inference
- Confidential container orchestration
- Secure LLM training workflows
If that’s you, let’s talk.
- Fully Homomorphic Encryption (FHE) — use cases, architecture, and performance tradeoffs
- Confidential Computing — deployment strategies, threat models, and performance implications for AI
- Designing privacy-preserving AI workflows end to end
- Email: alhassan@lorica.ai
- LinkedIn: linkedin.com/in/alhassankhedr
You'll find me spending time with family, reading, or watching sci-fi. I also enjoy swimming, rowing and volleyball.