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UMESH SHANKAR: Hi, everyone.
My name is Umesh Shankar.
I'm the chief technologist here at Google Cloud Security.
And I'm excited to talk to you today
about work we've been doing in the area of generative AI.
We're going to cover a number of areas here today.
We're going to go through a brief history of innovation
in AI and the generative space here at Google.
We're going to talk about key security challenges
that we face as an industry and how AI could help with those.
We're going to talk about how we've been infusing AI
into all our products.
And we're going to talk about a custom AI model
that we've developed here in the Cloud Security Group
and how we're going to make that model
available through a security AI platform.
And finally, we're going to conclude
by talking about some next steps you can take.
Now, if you're like most of our audience,
when you think about the year 2017, you think,
isn't that the year that Harry Styles released his debut solo
album?
Yes.
Yes, it is.
But it turns out, over in the world of machine learning,
other exciting things were happening at the same time.
Google research released a paper then
called "Attention is all you need,"
which introduced the transformer architecture to machine
learning.
By being able to reason about words using
the context in which they appeared,
we were able to make immediate and dramatic gains
in machine translation.
What was hard to predict is that this architecture
would spark a revolution in AI with large language models that
had a broad set of abilities such as generating
content and logical reasoning as well.
Since that 2017 paper, Google has led a number
of foundational advances.
BERT led to significant improvements
in Google's Search quality.
LaMDA was a leap forward in conversation quality
and was the basis for the Bard chatbot that many of you
might be familiar with.
And the PaLM 2 model we recently introduced a Google I/O
made big steps in multi-lingual capabilities,
reasoning, and coding tasks.
So what does all this have to do with security?
Let's take a look at three longstanding big challenges
we face in the industry.
In some ways, we think AI can make a big difference.
In the world of medicine, there's
a concept of patient 0, the first person infected
with a novel disease.
In security, we have a similar concept.
The first user or organization hit with a novel compromise.
Today, it can be an overwhelming challenge
for people to keep up with the evolving threat landscape.
Even if you know all the threats, knowing what to do
and doing it isn't easy.
So we asked ourselves, what if we could prevent patient 1s,
if we could use AI to take what we learn in real time
from that first compromise and operationalize that
to protect everyone else?
Let's go to our second category, toil.
If we're being honest, the practice of security
can feel like trying to stay on a treadmill that goes
faster and faster every day.
And most of the people who do security
aren't security specialists.
They're software engineers, SREs, IT admins,
database admins.
They're people whose main job is not doing security.
And even experts often end up doing repetitive or manual
tasks, leaving them with little time
to focus on more complex and interesting problems
that only they can tackle.
This is a big cause of burnout in the industry as well.
It's something that we spend a lot of time thinking about.
So we asked ourselves, what if, instead, we
could build systems that secure themselves?
That, when you want to deploy a new piece of software
or a setup, we're able to automatically generate
the right security policy to go along
with that that eliminates enormous amounts of manual work
to just figure out what is the right change to make?
And finally, let's turn our attention to the talent gap.
We know, in our industry, there's
a gap of millions of jobs compared
to the number of skilled people who are available to fill them.
We think AI can make non-specialists productive
much more quickly with the skills they already have
and help them climb the learning curve to expertise
much more quickly.
We're already building features in our products
to tackle each of these challenges.
VirusTotal today, with its Code Insight feature,
is able to analyze previously unseen PowerShell
scripts to explain and assess them using generative AI.
It was able to classify some scripts as risky
as well as or better than existing AV engines.
Breach Analytics for Chronicle takes
indicators and other markers from known breaches,
including those happening in real time,
and operationalize those to detect the same activity
elsewhere.
Our Assured Open Source offering lets
customers get curated, scanned, securely
built versions of OSS packages, helping to mitigate software
security supply chain risk.
We're using AI to detect previously unknown
vulnerabilities more scalably.
Duet AI for Mandiant Threat Intelligence
is able to synthesize and summarize relevant threat
intelligence and make it digestible by a range
of audiences, saving lots of manual analysis
and compilation.
Duet AI for Chronicle lets users who
are not familiar with the domain-specific UDM schema
language write natural language search queries like,
"Show me which Windows machines used
RDP or VNV in the last month."
And have it generate the exact search query
that you need to do that.
This can be a big timesaver for experts as well,
giving them a starting query that they can then
refine by hand.
Authoring detections in Chronicle
is also going to benefit from the AI.
Authoring detections can be challenging and require
lots of iteration to get right.
Being able to have a good starting point
from natural language and being able to use
the conversational AI features to continually refine
those detections is something we think
is going to save people time and lead to better detection
quality.
And finally, Duet AI for Security Command Center
enhances our attack path simulation capability
which shows possible ways your sensitive resources might
be accessed by adding a plain language explanation
and summary of the sometimes complex graph data
that you have to process.
Now, to do these things well, you
need a security-specific model, one
that speaks security natively.
And we discovered this because we tried general purpose models
first.
And we found they didn't always perform as well as we would
like on these security tasks.
For example, security-specific languages
aren't always well represented in general purpose model
training sets.
Beyond that, a lot of important security data
just isn't on the public internet,
which is where a lot of training data comes from.
So we combine both public security resources
and the incredibly rich threat intelligence data
we've compiled across Google and Mandiant
to fine tune a model aimed at solving
security tasks specifically.
In 2022, Mandiant conducted over 1,000 incident response
engagements.
This gives us World class frontline intelligence
visibility to capture new threats as they unfold.
Beyond these engagements, we have
hundreds of threat analysts looking for and tracking
behaviors by bad actors.
We also analyze millions of potentially malicious artifacts
a day.
All of this gives us a strong data foundation
on which to train our model.
SEC-PaLM 2 is a descendent of Google's general PaLM 2
model which we recently introduced at Google I/O.
Google is continually developing more powerful foundation
models like PaLM 2 and will evolve SEC-PaLM
to take advantage of those models
as they become available as well.
You already saw some ways we're using SEC-PaLM
to build innovative features to tackle the problems of threats,
toil, and talent.
Now we're making this model available via a Security AI
Workbench, the core of which is SEC-PaLM 2.
Vertex AI is Google Cloud's AI platform.
And we leverage the easy-to-use generative
AI APIs that were recently introduced.
By building on Vertex, you get enterprise-grade controls
for data protection, compliance, and sovereignty.
You get access to Google's custom-built AI chips, TPUs,
or Tensor Processing Units, and you
get management-friendly features like identity integration,
scale delivery, and SLOs.
It's the same platform we're using
to build our own AI-powered product features.
Of course, all this wouldn't be nearly as exciting if we
made it just for ourselves.
Customers will be able to use Workbench for internal use
cases as well as developing product features of their own.
The API is particularly well suited
to three kinds of atomic tasks--
summarization, classification, and generation.
Summarization is a key capability
when trying to make sense of large volumes of security data
and making it digestible for a variety of audiences, something
that often takes a lot of manual effort.
Classification in the security context
is often about detecting potentially risky
or malicious activity or artifacts
and explaining the reasons behind that decision.
And generation is valuable when working
with formats like security policy or query languages
when you want to generate the right snippet to capture
a higher level intent.
This is something that people often struggle to do manually.
Of course, a big part of the value of large language models
is their ability to stitch all of this together
and to present a conversational interface
on top of those capabilities.
We think this is particularly valuable in the security space
where information can be spread across many different systems
and have to be synthesized in order to make sense of it.
We know, in all this, that privacy
is top of mind for many of our customers.
It's important to know that the prompts that you give
to the API don't get logged or fed back into the training set
for the model.
We think extensibility is key to making the Workbench maximally
useful.
It's important to let customers tap into their own data
seamlessly and to connect to other security APIs,
like threat intelligence, from our partners.
To do this, we're planning to enable a plugin model
for the SEC-PaLM 2 API, which will allow secure API
connections to the data inference time
keeping all the platform data protections in the process.
You'll be able to do this without leaving your compliance
boundary.
Plugins can be private to a single customer
or made available for other customers
to use through a marketplace.
We'll continue to extend the SEC-PaLM 2
model to cover additional security-specific languages
that are necessary to interpret security
data that our customers find important
or that these plugins provide.
We've seen a lot of interest in the industry from our partners
and customers since we introduced this model
at the RSA conference in April this year.
We're now starting to work with some early partners
to build and extend the Workbench ecosystem.
It's important to emphasize that this is just
the start of a long and exciting journey.
We're all very excited here to see the innovative applications
all of you come up with.
If you're interested to learn more about what we're doing
with generative AI, check out our website
at cloud.google.com/security/ai.
Thanks again for joining me here today.
And I hope you enjoy the rest of the sessions
here at the Google Cloud Security Summit.
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