# Intel SGX Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/intel-sgx
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> Technology record last updated: 2026-02-27T15:12:04Z
> Generated: 2026-09-21T10:50:11Z

Intel SGX secures sensitive data in-use by isolating code and information within hardware-encrypted CPU enclaves.

Intel Software Guard Extensions (SGX) provides hardware-level isolation for sensitive workloads through secure memory partitions called enclaves. These enclaves reside in the Processor Reserved Memory (PRM): a dedicated slice of RAM encrypted at the hardware level. Because the CPU handles encryption, data remains protected even if the operating system or hypervisor is fully compromised. Developers use the Intel SGX SDK to split applications into trusted and untrusted components. Major implementations include Signal (for private contact discovery) and Microsoft Azure (Confidential Computing series). It successfully shrinks the attack surface to the processor and the specific code inside the enclave.

- Official technology site: https://www.intel.com/content/www/us/en/architecture-and-technology/software-guard-extensions.html
- Public AI Tinkerers demos and talks: 3
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Encrypted Inferences: A 5‑Minute FHE with FHE‑Studio and FHE Model Marketplace](https://toronto.aitinkerers.org/talks/rsvp_tT7xsSECN2Y)

I’ll present https://fhe-studio.com/, an open-source development environment designed to make encrypted data structures, algorithms, and AI workflows practical for real-world use. We will go through coding in cipher patterns I published at: https://towardsdatascience.com/coding-in-cipher-encrypted-data-structures-and-algorithms-dd99e584a655/ The session will include a live demonstration of building and running computations directly on encrypted data, followed by an overview of an AI Marketplace https://aimarketplace.01com.com/ that enables publishing FHE-based models and running encrypted inference without exposing plaintext data to model owners, infrastructure providers, or operators

- Event context: AI Tinkerers Toronto - January 2026 Meetup at Google! — 2026-01-29 — Toronto
- Public talk page: https://toronto.aitinkerers.org/talks/rsvp_tT7xsSECN2Y

### [Stateful AI Without Vendor Lock-In](https://amsterdam.aitinkerers.org/talks/rsvp_S03vGD_udWI)

This talk presents the architecture and live technical demonstration of Intangle, a stateful AI system built around a self-learning memory layer called the Coherence Engine. I will walk through how we securely persist, scope, and retrieve long-term conversational memory while orchestrating an agentic mesh across multiple MCP-compatible model providers. The session will focus on concrete implementation details: encrypted memory graphs, agent governance, execution inside Trusted Execution Environments, and cross-vendor context portability. All examples will be within our production environment and demonstrated live, without slides.

- Event context: Trust & Security in AI Demo Night — Amsterdam Edition — 2025-12-16 — Amsterdam
- Public talk page: https://amsterdam.aitinkerers.org/talks/rsvp_S03vGD_udWI

### [Beyond Public Data: Unlocking AI's Next Frontier with Secure Private Data](https://waterloo.aitinkerers.org/talks/rsvp_cXgF867zQU8)

This session will provide a technical deep dive into building secure data access infrastructure that enables AI systems to train on private data without compromising privacy or security. We'll walk through the complete architecture of a decentralized data access system that uses Trusted Execution Environments (TEEs), encryption, and blockchain technology to create a trustless environment for data access. You'll leave with practical knowledge of the technical challenges in private data aggregation and working code examples you can adapt for your own projects.

- Event context: AI Tinkerers - Waterloo April Meetup — 2025-04-21 — Waterloo
- Public talk page: https://waterloo.aitinkerers.org/talks/rsvp_cXgF867zQU8

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