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Computer Vision AI Accelerators Inference & Training Brain-Inspired Computing
Chip Design Compute 2.0 Data Centers Machine Learning

8 December 2023 13 min read


GENERATIVE AI FROM AN ENTERPRISE ARCHITECTURE STRATEGY PERSPECTIVE

Generative AI

EYAL LANTZMAN

Read More

 1. How is machine learning done?
 2. Governance and compliance in generative AI models
 3. Balancing fine-tuning and control in AI models
 4. Challenges and strategies for cross-cloud integration
 5. How do we think about security and governance in depth?
 6. Navigating identity and access management
 7. Innovative approaches to content moderation
 8. Conclusion

Eyal Lantzman, Global Head of Architecture, AI/ML at JPMorgan, gave this
presentation at the London Generative AI Summit in November 2023.

I’ve been at JPMorgan for five years, mostly doing AI, ML, and architecture. My
background is in cloud infrastructure, engineering, and platform SaaS. I
normally support AI/ML development, tooling processes, and use cases.

Some interesting observations have come about from machine learning and deep
learning. Foundation models and large language models are providing different
new opportunities and ways for regulated enterprises to rethink how they enable
those things.

So, let’s get into it.


HOW IS MACHINE LEARNING DONE? 

You have a data set and you traditionally have CPUs, although you can use GPUs
as well. You run through a process and you end up with a model. You end up with
something where you can pass relatively simple inputs like a row in a database
or a set of features, and you get relatively simple outputs back. 

We evolved roughly 20 years ago towards deep learning and have been on that
journey since. You pass more data, you use GPUs, and there are some different
technology changes. But what it allows you to do is pass complex inputs rather
than simple ones. 

Essentially, the deep learning models have some feature engineering components
built in. Instead of sending the samples and petals and length and width for the
Iris model and figuring out how to extract that from an image, you just send an
image and it extracts those out automatically. 


GOVERNANCE AND COMPLIANCE IN GENERATIVE AI MODELS

Foundation models are quite interesting because first of all, you effectively
have two layers, where there’s a provider of that foundation model that uses a
very large data set, and you can add as many variants as you want there. They're
GPU-based and there's quite a bit of complexity, time, and money, but the
outcome is that you can pass in complex inputs and get complex outputs. 

So that's one difference. The other is that quite a lot of them are multimodal,
which means you can reuse them across different use cases, in addition to being
able to take what you get out of the box for some of them and retrain or
fine-tune them with a smaller data set. Again, you run additional training
cycles and then get the fine-tuned models out. 

Now, the interesting observation is that the first layer on top is where you get
the foundation model. You might have heard statements like, “Our data scientist
team likes to fine-tune models.” Yes, but that initial layer makes it available
already for engineers to use GenAI, and that’s the shift that’s upon us. 

It essentially moves from processes, tools, and things associated with the model
development lifecycle to software because the model is an API. 

But how do you govern that? 

Different regulated enterprises have their own processes to govern models versus
software. How do you do that with this paradigm? 

That's where things need to start shifting within the enterprise. That
understanding needs to feed into the control assurance functions and control
procedures, depending on what your organization calls those things. 

This is in addition to the fact that most vendors today will have some GenAI in
them. That essentially introduces another risk. If a regulatory company deals
with a third-party vendor and that vendor happens to start using ChatGPT or LLM,
if the firm wasn’t aware of that, it might not be part of their compliance
profile so they need to uplift their third-party oversight processes. 

They also need to be able to work contractually with those vendors to make sure
that if they’re using a large language model or more general AI, they mustn't
use the firm's data.

AWS and all the hyperscalers have those opt-out options, and this is one of
those things that large enterprises check first. However, being able to think
through those and introducing them into the standard procurement processes or
engineering processes will become more tricky because everyone needs to
understand what AI is and how it impacts the overall lifecycle of software in
general. 


BALANCING FINE-TUNING AND CONTROL IN AI MODELS

In an enterprise setting, to be able to use an OpenAI type of model, you need to
make sure it's protected. And, if you plan to send data to that model, you need
to make sure that data is governed because you can have different regulations
about where the data can be processed, and stored, and where it comes from that
you might not be aware of. 

Some countries have restrictions about where you can process the data, so if you
happen to provision your LLM endpoint in US-1 or US central, you possibly can't
even use it. 

So, being aware of those kinds of circumstances can require some sort of
business logic.

Even if you do fine-tuning, you need some instructions to articulate the model
to aim towards a certain goal. Or even if you fine-tune the hell out of it, you
still need some kind of guardrails to evaluate the sensible outcomes. 

There’s some kind of orchestration around the model itself, but the interesting
point here is that this model isn’t the actual deployment, it's a component. And
that's how thinking about it will help with some of the problems raised in how
you deal with vendor-increasing prices. 

What's a component? It's exactly like any software component. It's a dependency
you need to track from a performance perspective, cost perspective, API
perspective, etc. It’s the same as you do with any dependency. It's a component
of your system. If you don't like it, figure out how to replace it.

Now I’ll talk a bit about architecture.


CHALLENGES AND STRATEGIES FOR CROSS-CLOUD INTEGRATION

What are those design principles? This is after analyzing different vendors, and
this is my view of the world. 

Treating them as a SaaS provider and as a SaaS pattern increases the control
over how we deal with that because you essentially componentize it. If you have
an interface, you can track it as any type of dependency from performance cost,
but also from an integration with your system. 

So if you're running on AWS and you're calling Azure OpenAPI endpoints, you'll
probably be paying for networking across cloud cost and you'll have latency to
pay for, so you need to take all of those things into account. 

So, having that as an endpoint brings those dimensions front and center into
that engineering world where engineering can help the rest of the data science
teams.

We touched on content moderation, how it's required, and how different vendors
implement it, but it's not consistent. They probably deal with content
moderation from an ethical perspective, which might be different from an
enterprise ethical perspective, which has specific language and nuances that the
enterprise tries to protect. 

So how do you do that? 

That's been another consideration where I think what the vendors are doing is
great, but there are multiple layers of defense. There’s defense in depth, and
you need ways to deal with some of that risk 

To be able to effectively evaluate your total cost of ownership or the value
proposition of a specific model, you need to be able to evaluate those different
models. This is where if you're working in a modern development environment, you
might be working on AWS or Azure, but when you're evaluating the model, you
might be triggering a model in GCP. 

Being able to have those cross-cloud considerations that start getting into the
network and authentication, authorization, and all those things can become
extremely important to design for and articulate in the overall architecture and
strategy when dealing with those models. 

Historically, there were different attempts to wrap stuff up. As cloud
providers, service providers, and users of those technologies, we don't like
that because you lose the value of the ecosystem and the SDKs that those
provide. 

To be able to solve those problems whilst using the native APIs, the native SDK
is essential because everything runs extremely fast when it comes to AI and
there's a tonne of innovation, and as soon as you start wrapping stuff then,
you’re already out in terms of dealing with that problem and it's already
pointless.


HOW DO WE THINK ABOUT SECURITY AND GOVERNANCE IN DEPTH?  

If we start from the center, you have a model provider, and this is where the
provider can be an open source one where you go through due diligence to get it
into the company and do the scanning or adverse testing. It could be one that
you develop, or it could be a third-party vendor such as Anthropic.

They do have some responsibility for encryption and transit, but you need to
make sure that that provider is part of your process of getting into the firm.
You can articulate that that provider’s dealing with generative AI, that
provider will potentially be sent classified data, that provider needs to make
sure that they're not misusing that data for training, etc. 

You also have the orchestrator that you need to develop, where you need to think
about how to prevent prompt injection in the same way that other applications
deal with SQL injection and cross-site scripting. 

So, how do you prevent that? 

Those are the challenges you need to consider and solve. 

As you go to your endpoints, it's about how you do content moderation of the
overall request and response and then also deal with multi-stage, trying to
jailbreak it through multiple attempts. This involves identifying the client,
identifying who's authenticated, articulating cross-sessions or maybe multiple
sessions, and then being able to address the latency requirements. 

You don't want to kill the model’s performance by doing that, so you might have
an asynchronous process that goes and analyzes the risk associated with a
particular request, and then it kills it for the next time around. 

Being able to understand the impact of the controls on your specific use case in
terms of latency, performance, and cost is extremely important, and it's part of
your ROI. But it's a general problem that requires thinking about how to solve
it. 

You're in the process of getting a model or building a model and creating the
orchestrated testing as an endpoint and developer experience lifecycle loops,
etc. 

But your model development cycle might have the same kind of complexity when it
comes to data because if you're in an enterprise that got to the level of
maturity that they can train with real data, rather than a locked up,
synthesized database, you can articulate the business need and you have approval
taxes, classified data for model training, and for the appropriate people to see
it.

This is where your actual environment has quite a lot of controls associated
with dealing with that data. It needs to implement different data-specific
compliance. 

So, when you train a model, you need to train this particular region, whether
it's Indonesia or Luxembourg, or somewhere else. 

Being able to kind of think about all those different dimensions is extremely
important. 

As you go through whatever the process is to deploy the application to
production, again, you have the same data-specific requirements. There might be
more because you're talking about production applications. It's about impacting
the business rather than just processing data, so it might be even worse. 

Then, it goes to the standard engineering, integration, testing, load testing,
and chaos testing, because it’s your dependency. It's another dependency of the
application that you need to deal with. 

And if it doesn't scale well because there’s not enough computing in your
central for open AI, then this is one of your decision points when you need to
think about how this would work in the real world. How would that work when I
need that capacity? Being able to have that process go through all of those
layers as fast as possible is extremely important. 

Threats are an ongoing area of analysis. There’s a recent example from a couple
of weeks ago from apps about LLM-specific threats. You'll see similar threats to
any kind of application such as excessive agency or or denial of service. You
also have ML-related risks like data poisoning and prompt object injection which
are more specific to large language models.

This is one way you can communicate with your controller assurance or cyber
group about those different risks, how you mitigate them, and compartmentalize
all the different pieces. Whether this is a third party or something you
developed, being able to articulate the associated risk will allow you to deal
with that more maturely. 


NAVIGATING IDENTITY AND ACCESS MANAGEMENT

Going towards development, instead of talking about risk, I'm going to talk
about how I compartmentalize, how to create the required components, how to
think about all those things, and how to design those systems. It’s essentially
a recipe book. 

Now, the starting assumption is that as someone who’s coming from a regulated
environment, you need some kind of well-defined workspace that provides
security. 

The starting point for your training is to figure out your identity and access
management. Who’s approved to access it? What environment? What data? Where's
the region? 

Once you've got in, what are your cloud-native environment and credentials? 
What do they have access to? Is it access to all the resources? Specific
resources within the region or across regions? It’s about being able to
articulate all of those things.

When you're doing your interactive environment, you'll be interacting with the
repository, but you also may end up using a foundation model or RAG resource to
pull into your context or be able to train jobs based on whatever the model is. 

If all of them are on the same cloud provider, it's simple. You have one or more
SDLC pipelines and you go and deploy that. You can go through an SDLC process to
certify and do your risk assessment, etc. But what happens if you have all of
those spread across different clouds?

Essentially, you need additional pieces to ensure that you're not connecting
from a PCI environment to a non-PCI environment. 

Having an identity broker that can have additional conditions about access that
can enforce those controls is extremely important. This is because given the
complexity in the regulatory space, there are more and more controls and it
becomes more and more complex, so pushing a lot of those controls into systems
that can reason about them and enforce that is extremely important. 

This is where you start thinking about LLM and GenAI from a use case perspective
to just an enterprise architecture pattern. You're essentially saying, “How do
we deal with that once and for all?” 

Well, we identify this component that deals with identity-related problems, we
articulate the different data access patterns, we identify different
environments, we catalog them, and we tag them. And based on the control
requirements, this is how you start dealing with those problems. 

And then from that perspective, you end up with fully automated controls that
you can employ, whether it's AWS Jupyter or your VS code running in Azure to
talk to Bedrock or Azure OpenAI endpoint. They're very much the same from an
architecture perspective. 


INNOVATIVE APPROACHES TO CONTENT MODERATION

Now, I mentioned a bit about the content moderation piece, and this is where I
suggested that the vendors might have things, but: 

 1. They're not really consistent. 
 2. They don't necessarily understand the enterprise language. 

What is PI within a specific enterprise? Maybe it’s some specific kind of user
ID that identifies the user, but it's a letter and a number that LLM might never
know about. But you should be careful about exposing it to others, etc. 

Being able to have that level of control is important because it's always about
control when it comes to risk.

When it comes to supporting more than one provider, this is essentially where we
need to standardize a lot of those things and essentially be able to say, “This
provider, based on our assessment of that provider can deal with data up to x,
y, and z, and in regions one to five because they don't support deployment in
Indonesia or Luxembourg.” 

Being able to articulate that part of your onboarding process of that provider
is extremely important because you don't need to think about it for every use
case, it's just part of your metamodel or the information model associated with
your assets within the enterprise. 

That content moderation layer can be another type of AI. It can be as simple as
a kill switch that’s based on regular expression, but the opportunity is there
to make something learn and adjust over time. But from a pure cyber perspective,
it’s a kill switch that you need to think about, how you kill something in a
point solution based on the specific type of prompt. 

For those familiar with AWS, there's an AWS Gateway Load Bouncer. It’s extremely
useful when we're coming to those patterns because it allows you to have the
controls as a sidecar rather than part of your application, so the data
scientist or the application team can focus on your orchestrator. 

You can have another team that can specialize in security that creates that
other container effectively and deploys and manages that as a separate
lifecycle. This is also good from a biased perspective because you could have
one team that’s focused on making or breaking the model, versus the application
team that tries to create or extract the value out of it.

From a production perspective, this is very similar because in the previous
step, you created a whole bunch of code, and the whole purpose of that code was
becoming that container, one or more depending on how you think about that. 

That's where I suggest that content moderation is yet another type of kind of
container that sits outside of the application and allows you to have that
separate control over the type of content moderation. Maybe it's something that
forces a request-response, maybe it's more asynchronous and kicks it off based
on the session. 

You can have multiple profiles of those content moderation systems and apply
them based on the specific risk and associated model. 

Identity broker is the same pattern. This is extremely important because if
you're developing and testing in such environments, you want your code to be
very similar to how it progresses. 

In the way you got your credentials, you probably want some configuration in
which you can point to a similar setup in your production environment to inject
those tokens into your workload. 

This is where you probably don't have your fine-tuning in production, but you
still have data access that you need to support. 

So, having different types of identities that are associated with your flow and
being able to interact, whether it’s the same cloud or multi-cloud, depending on
your business use case, ROI, latency, etc., will be extremely important. 

But this allows you to have that framework of thinking, Is this box a different
identity boundary? Yes or no? If it's a no, then it’s simple. It's an IM policy
in AWS as an example, versus a different cloud, how do you federate access to
it? How do you rotate credential secrets?


CONCLUSION

To summarise, you have two types of flows. In standard ML, you have the MDLC
flow where you go and train a model and containerize it. 

In GenAI, you have the MDLC only when you fine-tune. If you didn't fine-tune,
you run through pure SDLC flow. It's a container that you just containerized
that you test and do all those different steps. 

You don't have the actual data scientists necessarily involved in that process.
That’s the opportunity but also the change that you need to introduce to the
enterprise thinking and the cyber maturity associated with that. 

Think through how engineers, who traditionally don't really have access to
production data, will be able to test those things in real life. Create all
sorts of interesting discussions about the environments where you can do secure
development with data versus standard developer environments with mock data or
synthesized data.


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Written by:

EYAL LANTZMAN

Architecture, Security and Engineering of AI optimized platforms and ecosystems
[Ex-Amazon, Ex-Microsoft].

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