Update AI Use Cases

The Use Case working group generated slides describing Use Cases, this PR brings the description of the Supply Chain Use case into the website content.

Signed-off-by: James Magowan <[email protected]>

Author: magowan

content/en/docs/use-cases/confidential-ai.md

@@ -1,6 +1,6 @@
 ---
 title: Confidential AI
-description: Confidential Containers for AI 
+description: Confidential Containers for AI Use Cases
 categories:
 - use cases
 tags:
@@ -9,4 +9,74 @@ tags:
 weight: 60
 ---
 
-Coming soon
+## Federated Learning
+
+![Federated Learning](/img/FederatedLearning.png)
+
+## Attestations
+- Federated Learning requests multi-SDK attestation
+- FL Servers needs to verify all client’s trustworthiness
+- Attestation at different points, self and cross verifications via attestation service
+
+## CC Policies:
+- Bootup policy – provided by hardware vendor
+- Self-verification CC policy – user defined
+- Cross-verification CC policy  -- user defined
+
+## Protected Assets
+- **Code**: Training code (client), aggregation code (server)
+- **Data**: input data
+- **Model**:  initial model, intermediate model, output model
+- **Workspace**: check points, logs, temp data, output directory
+
+## Key Broker Service
+- Key management depends on user case, global model ownership, key release management process
+
+## Bootstrap:
+- Need a process to generate the keys, policies and input them into key-vault to avoid tempering
+
+### Concerns when using federated learning
+- Trustworthy of the participants
+- Execution code tampering
+- Model tampering
+- Model inversion attack
+- Model theft during training
+- Private data leak 
+
+### CoCo will not protect if
+- code is already flawed at rest
+- data is already poisoned at rest
+- model is already poisoned at rest
+
+## Multi-Party Computing
+
+![Multi-Party Computing](/img/MultiPartyCompute.png)
+
+Source: [https://uploads-ssl.webflow.com/63c54a346e01f30e726f97cf/6418fb932b58e284018fdac0_OC3%20-%20Keith%20Moyer%20-%20MPC%20with%20CC.pdf](https://uploads-ssl.webflow.com/63c54a346e01f30e726f97cf/6418fb932b58e284018fdac0_OC3%20-%20Keith%20Moyer%20-%20MPC%20with%20CC.pdf)
+
+
+### Requirements
+- Attestation-cross-verification
+  - In MPC or FL cases, we need to explicitly verify all participants to be trustworthy according to the cross verification CC policy. 
+- Periodic self-check test and cross-verification
+  - a long running workload can ensure that the TEE is still valid
+- Ad hoc attestation verification for given participation node
+  - From CLI or API, application wants to know what’s the current status (trustworthiness) of the specified node
+- Attestation audit report 
+  - each party would like to get details on when attestation was performed by the TEE and relevant details
+- Connecting to multiple KBSes
+  - the workload needs to connect to different KBS each belonging to a specific party for getting access to the keys
+- Support multiple devices
+  - One participant may has only one type of device (CPU), but need to verify other participant’s devices including different CPU and GPU
+
+## Retrieval Augmented Generation Large Language Models (RAG LLM)
+
+![RAG LLM](/img/RAG_LLMS.svg)
+
+Further RAG Use Case Information 
+- [NVIDIA Generative AI Examples](https://github.com/NVIDIA/GenerativeAIExamples)
+
+### Threat Model
+Potential Threats: [OWASP Top 10 for LLMs](https://owasp.org/www-project-top-10-for-large-language-model-applications/)
+
+![RAG Threat Model](/img/RAG_ThreatModel.svg)

static/img/FederatedLearning.png

@@ -106,3 +106,5 @@ plaintext
 Dockerfile
 ghcr
 repo
+[bB]ootup
+hoc