>> Hi, welcome to the demo.
In this demo, you will see how Azure Confidential Services were
leveraged to solve data and code confidentiality problems,
to build the trust between
the multiple parties that are involved in hosting the solution.
The business problem lies between three parties with hospital
as the primary customer sharing the patient sensitive information.
All three parties were able to move forward
leveraging confidential computing nodes on
AKS and related confidential
services with Azure for an end-to-end protection.
The client is running on
Azure Blob Storage hosted on Lamna Azure subscription,
who's the primary customer and the hospital.
A Python Flask based Web API application
is leveraging SCONE for a lift and
shift and Redis Cache
as a storage for a full in-memory encryption.
Fabrikam here, is using
ONNX based model that
Microsoft ONNX team is making it
available for running it confidentially as well.
All three parties here are able to trust each
other through an attestation services that provides
transparent attestation of the hardware and
the software and the runtime
that is included to run these applications.
Let's dive into the details now.
On this part, you would see how AKS,
two clusters were used to host Fabrikam on Contoso,
where you could add a DC skew to the nodepool,
that'll basically enable confidential containers platform.
Heterogeneous nodepool was created to run the sensitive parts of
applications of microservices on
the same cluster leveraging the current SEC or DevOps.
Here you see a Python app Contoso is
using that is taking
as-is and converting that as confidential containers.
Running the Python engine
inside of an enclave is only the first step
since an adversary could just
modify or replace Python code of the Web API.
SCONE containers are encrypted and IP protected with
key release policies attached to
the hardware that are transparently handled by SCONE.
For the Redis component,
this is a straightforward process.
In this part of the demo,
you'll see the web portal that is hosted
by Lamna Hospital as a static website.
When you hit the "Submit" button,
the Python Web API is called,
that is going to the Redis Cache which is all running in
an enclave for securely fetching the patient details.
If a patient is not found,
a form is prompted for the hospital employees
to punch in the sensitive information to store back on Redis.
Once the information is filled out,
the hospitals can also upload MRI scans for the brain to
determine the probability of tumor as
additional data point for
the hospitals to follow up on the next steps.
The Fabrikam is hosting
the ML model that shows the tumors and it is using on X runtime,
in this case, that is being
communicated directly with Python Web API.
For the confidential inferencing,
a PyTorch model was taken and converted it into
an ONNX to be run as confidential inferencing service.
A Microsoft Azure Data Station is
leveraged in combination with SCONE attestation
as a key service provider to
identify the apps and the hardware they are initiated on.
Azure Data Station collects enclave evidence,
validates it against security baseline,
and evaluates it against an attestation policy.
Upon successful verification, the service generates a JWD token,
and that token is used by
the relying party to look at the trust of the overall application.
Azure Data Station AIP is protected by Azure AD
and hence require an AAD token for you to toke.
The relying party could be extended to Azure Key Vault or
any other Azure services that understand the
JWD token for further downstream processing.
This is the end of the demo.
To summarize, you saw how confidential computing
was leveraged to create trust between multiple parties,
hospital in this case,
how the data and its IP was protected for the containers,
and how Azure component services
like Azure Attestation services and
confidential inferencing were leveraged to
verify the security posture and have an end-to-end protection.
But last but not the least,
how lift and shift was enabled for
an unmodified Docker Apps through a partner.
Thanks for watching.