Please welcome Ryan Nalette, Security Operations Engineer and
Cloud Response at AWS, John Miller, Head of Mandiant
Intelligence Analysis at Google Cloud.
Alan. Shindo, VP of AI Threat Research
at Wiz, hosted by Crying Out Cloud host Eden Kobe Naphtali.
Hello, my name is Eden Kobe Noftali and I am a product
manager at Wiz as well as the Co host of the Crying Out Cloud
podcast. On the podcast, we fittingly
remind everyone at the beginning of every episode that this is
the place to laugh, cry, and reconsider all of your cloud
security fears. So I think it's a good as time
as ever to do that. After Alon's amazing
presentation, really, it never ceases to amaze me the inspiring
work that research is doing and also how fast the fret landscape
is evolving. So thank you, Alon.
And Alon, in addition to being the VP of AI and Threat research
at Wiz, he is also the producer of the Crying Out Cloud podcast.
So this is super exciting to get him from behind the scenes on
stage. OK.
And now we have an amazing chance to digest and continue
the conversation with our esteemed guest from GCP and AWS,
John Miller and Ryan Dolett. Thank you for being here.
Thank you for having us. Yay.
OK, So just so everyone knows a little bit about who you are,
can you tell us a little bit about what your role is today?
Yeah, John Miller, I'm Director of Operations for Google Threat
Intelligence Group, which recently brought together Mandy
and Intelligence as well as as Google's Threat Analysis Group
tag. So we have a mission of applying
threat intelligence to protect Google and our users and our
customers from advanced threats. And my background personally is
in building and scaling threat intelligence teams.
And I'm Ryan Nollette. I'm the technical lead for the
vulnerability disclosure program at EWS.
Basically I work with the people who go to the dark places on the
Internet, poke it with a stick and see what crawls out.
Those are the folks that report things to me.
I'm also the co-author of AWS Detective and a bunch of the
proactive defense tools and detection tools that AWS does.
Awesome. OK, so there's a question we ask
everyone who comes on the podcast to actually get to know
them a little bit better. If you were a vulnerability,
what type of vulnerability would you be?
I would not be an edge device vulnerability because I would be
having a very busy year. So I think what I would be is
zero click remote code execution because why settle for?
Less love it. I put a lot of thought into this
so I think I'm going to go with zombie process because I seem
like I'm dead but I consume a lot of resources.
Great answer alone. What are you?
I'll be a malicious model, I think, because I like fancy new
new things. Yeah, he does.
I know that for a fact. OK, so now serious questions.
Let's start off where Alone left off, how AI is affecting the
attack surface and a changing attacker behavior.
A long went through it how we see it from Wiz's perspective.
But John, I'm curious how it looks from the Google vantage
point. Also, as Google is incubating
really cool AI security projects, how do you think AI is
shaping the security landscape? Where my mind goes with that
question is what do we see AI doing to how threat actors
operate? And the short answer there is we
see extensive and rapid change in how threat actors operate
based on the availability of of AI technology.
And I think that's impacting the landscape for defenders in two
really important ways. First of all, to just the level
of interest that we see, we see in underground communities, you
know where nation state actors and criminal actors source their
capabilities. We see a growing number of tools
and capabilities that malicious actors sell to each other that
are AI powered. Our team also took a look at
what threat actor activity do we see attempting to use Gemini and
we saw a whole lot there as well.
We saw threat actors from over 20 countries trying to use that
one AI tool for capabilities really across the cycle of how
threat actors operate. And so, you know, given all that
interest, what do we see actually happening to to how
threats manifest? And I think it comes down to two
important changes #1 we see actors applying AI tooling to
increase the variability of their operations.
You called out just a minute ago, you know, generating
phishing content. We also see threat actors doing
things like using AI to bury the commands that they execute in
victim environments. And I think that's challenging
defenders to up level their focus on preventing classes of
threats rather than individual instances of threats.
And the second change I would say we see being driven is an
increase in threat actors ability to respond and adapt to
challenges that they encounter in victim environments.
We see threat actors leaning on AI to do things like learn about
new technology that they encounter in environments and to
generate new tools quickly for interacting with utilities that
they encounter in victim environments.
And so I think that connects back to, you know, we heard
earlier today quite a bit about the concept of addressing
critical risks. More and more, you know,
defenders are in a race to solve those before attackers do,
before attackers exploit them. And attackers speed to
exploiting those issues is being accelerated by AI.
Totally. And Ryan, you have another
interesting viewpoint from another cloud provider, Amazon.
And also from the response side, have you seen a change in the
number of incidents because of AI And can you share promising
mitigation strategies that you've already put into action?
Yeah. So I, I think there's a pretty
common theme that everyone's really set up today and that is
speed and context are the two really big changes with the
implementation of AI from the fence side.
Like I get vulnerability reports sent to me right now, just like
the blue teams and everyone else here who is using AI to try and
improve their life, their workflows, etcetera.
So are threat actors and researchers, etcetera.
So one of the big changes I got is called AI slot.
Most of you probably have members of your team who use AI
to generate their reports. So you know what I'm talking
about. 10 pages to say two things.
And where this really falls in as a big issue is that when
you're accepting vulnerability reports, you have to treat
everything as a valid issue until you prove it is not.
And as nice as these reports look, it tells you nothing.
And that is one of the the big like resource and time killers
that you get because people are leveraging AI to make the
reports look better. And it's taking human time away
because you have to decode those reports on.
Additionally, when we do get those valid reports in building,
So AWS is a team of builders. Our biggest focus is always on
securing the customer. You've heard a lot in our
messaging. When the things are found and we
have to fix them, are we going to do some kind of vibe code and
are we going to do regular development?
You have a senior look at it. How are you prioritizing the
risk that has come in to be fixed?
Is it a low enough risk that you could use AI just to fix it, or
do you want to give it to one of your senior developers to make
sure it's done and done right the first time?
And with the increase of AI usage, we're seeing that the
human factor of deciding where in the loop the human goes in
the AI workflow really depends on the severity of the risk.
And it allows us to change prioritization and how we assess
risks. And hopefully a bit later we'll
talk more about different types of vibe coding and ways to kind
of improve the security around those.
But those are the two of the big things that we see.
Also you guys are probably seeing in the news, there are
now entire development teams that are AI bug hunters.
We have worked with a few very low fidelity so far in finding
things successfully that are of any critical criticality.
And I think that's going to change over the next couple of
years. But vulnerability scanners
started out like this as well, right?
They looked for the common things, the stuff everybody kind
of knows to look for, but get missed sometimes by people in a
rush. And as they mature, they're
going to be more complex and they're going to be looking at
things besides just your supply chain.
Yeah, that's Kelly. And so how are you using AI to
help you and your teams, not just looking at AI as a threat?
Yeah, good question. First of all, we definitely are
seeing many use cases where AI is really accelerating what our
team can do from being able to process large amounts of event
data and tell our experts where they need to look closer,
identify new malicious codes, generate detections, monitor
underground communities where there's a lot of noisy activity.
And I think a key question a lot of organizations are having to
ask right now is given just how much opportunity there is and
how many different opportunities they hear about, how can they
make sure that they're not missing the right opportunities,
you know, to apply those capabilities to to their own
organizations? And so I think it's really
important to be able to step back and look at the area of
security you're assessing and ask, you know, what's a useful
framework to be able to inventory the things that need
to happen and think about where can I apply AI to those?
And I'll give an example in the world of threat intelligence.
SO1 framework for thinking about what has to happen in order to
create threat intelligence would be 3 things, visibility,
processing, and interpretation. Visibility would be I have
access to useful security data, for example, malicious emails.
Processing would be, I use that to create useful standardized
observations. For example, what malware did we
see associated with malicious emails?
And, and #3 Interpretation would be, I use that to answer a
stakeholder questions like how big a risk is this type of
malware to our organization? And you can break down
capability that way. And in that example, say, OK,
visibility, the advent of MCP servers, excuse me, the advent
of MCP servers is making it a lot easier for organizations to
accelerate integration work for new data sources, effectively
processing lots and lots of opportunities there because
there is stereotypically so much security data that was below the
threshold of what anyone can look at.
And now an agentic workflow can look at it to at least some
extent the interpretation phase, we see probably the most need
for a human in the loop because that's often the point where
you're making a significant security decision, but still
plenty of opportunity to accelerate workflows.
So overall, you know, being able to step back and look at
capabilities through the lens of that type of framework is really
important to make sure the organization is pursuing the
right opportunities. Human in the OODA loop, right?
For those who don't do a lot of security, UDA is a fighter jet
or a dog fighting type thing that was invented for military a
long time ago, stands for observe, Orient decide act.
And it's critical part of incident response and you know,
siding fact. Thank you.
There's four people clapping, which is surprising.
And that was a really bad joke. All right, moving on to a couple
other things that we're seeing with AI internally to try and
improve things for us. As I said, I read a lot of
reports. I also read a lot of content.
Part of my groups or my teams role is being the CNA for Amazon
and AWS. So we issue out security
advisories, so CVS, GHS as security bulletins, lots of
written content that all has to get generated.
And a lot of it has to also go through content that researchers
send us, you know, blogs, presentations, things like that,
that they'd like technical feedback on.
You know, how do we assess them at scale?
So we've written things that allow us to consistently and
repeatedly assess content for the same questions over and over
and over. So very much like what you would
have your analysts do, we're trying to write things with Jen
AI to do. So there's a couple of
challenges that I've seen. So last week I wrote my first
ever completely independent MCP server for an application that I
like has nothing to do with my work.
After actually writing it and making sure it all worked, I
then went and I tried to do a security review of it.
So I used a bunch of different AI tools from various vendors to
actually do security assessments against it.
The biggest thing I found was inconsistency.
Even when you asked the question the same way in the same prompt.
So it brought up, how do we improve that response?
And as a lot of vendors are saying now, context, making your
AI context aware so it's able to see all the information.
So we have things like vector databases and RAG and all that
kind of cool stuff. Each vendor I believe has a
different name for it, but we're using that to improve the
repeatability and accuracy. Additionally, what I like to
write in my Gen. AI stuff when I'm assessing
contents, tickets, entire engagements, is that when it
gives a a response that it gives a quote from where it found that
answer and the exact text that was used to answer that
question. So then we can score it for
accuracy after the fact because a large part of using AI
internally is scoring right? How accurate was it?
How do I improve it going forward?
Thinking of the AI assistant as just a very junior analyst and
training it the same way you would train a very junior
analyst is how we've been trying to implement.
Yeah, for sure. And following the threat of AI,
one of the things that Leung talked about was like the
Singularity incident where AI and supply chain security
crossover. What trends are you seeing in
threats to supply chain security?
Well, we see a lot of opportunities for improvement,
especially supply chain management.
We do see not, not just like particularly at AWS, but when
you look at the open source offerings with LLM in general or
AI in general, there's a lot of reuse of code.
Like 3 people figured out how to do something and posted it on
Stack Overflow and everyone just copied it.
So now when there's an issue there, it's not just an issue in
one person's package, it's an issue in 30,000 packages because
they all either copied the same function or they're using it as
a dependency. So how do you go up the supply
chain and get those things fixed for all the downstream people
who really, And that's one of the areas our team looks at,
because we're trying to help the community secure itself.
So finding who actually wrote this and like the top
attribution person to get it patched so then everyone below
it can inherit the patch and secure the community as a whole
is one of our big goals. And also call out one of the
most important trends in supply chains threats that that I'm
seeing right now. Actually one of the one of the
threat actors and, and campaigns that we heard called out a
couple minutes ago, the 5221 Rickstorm activity, I think is a
really good illustration of, of this particular trend.
So this is a sophisticated China Nexus actor.
And what we've seen is this is an actor that historically has
been tied to targeting technology companies in order to
get insight into undisclosed vulnerabilities.
The brickstorm activity that we reported recently, we saw this
actor engaging in in campaigns that were that were notable for
a couple reasons. In at least some of those cases
we saw and at least some of the compromises that we saw, the
threat actor was getting in by exploitation of edge devices.
Two, in a lot of cases it was difficult to tell how they got
in because they've been present in the environment for over a
year before they were detected, which is is kind of crazy.
And 3rd in, in a lot of those cases, you know, they were
targeting software as a service and other supply chain providers
where they could get at potentially both sensitive data
as well as again potentially insight into undisclosed
vulnerabilities. And So what we see there is
possibly a cycle of supply chain activity where supply chain
incidents enable this attacker to continue to conduct more
supply chain attacks. And I think, you know, again,
referencing back to the fact that these incidents involved
compromise of edge devices that really highlights the importance
that we heard called out earlier, defenders being able to
understand and get visibility and to address risks around
these devices. This is a particular area where
we see threat actors challenging defenders today and there is a
connection to supply chain threats.
And can I also just chime in that this is actually a a really
good topic with the supply chain because there's a big event
coming up in December where a WSGCP and Azure are all
combining with wiz0day.cloud event where the the targets that
are in there, well, they're all dependencies that a bunch of the
CSPS use. Why are we joining up to do
this? To protect the shared customer.
We all use these dependencies. Yeah.
So let's let's put the money where our mouth is, right?
And let's see where it goes. OK.
On that note and to wrap in front of your cloud security
leaders, practitioners, teams and given all the trends we've
looked at in the current threatscape, if you had to give
a one liner piece of advice to these teams, what would it be?
I'm terrible with one liners. I think you can do a few lines.
OK, Yeah, across the, you know, the, the activity that we see at
I think what stands out to us, there are a couple areas of
opportunity that are really important for defenders right
now. We we heard a couple minutes ago
that threat actors are increasingly tailoring their
their behavior to cloud assets. We absolutely do see that
happening and the areas where we see in breaches that we look at
defenders having the most opportunity really come down to
identity. Integrations with on premise and
visibility identity attackers are focusing in on you know how
can they subvert getting credentials, How can they
subvert multi factor connection to on premise.
As an example, we have seen cases in which attackers
targeted identity management solutions that were synchronized
between on premise and cloud and targeted the on premise side of
things as a way to get into the cloud environment.
So taking a close look at connections back into on premise
is really critical. And then the third visibility,
many organizations approach to logging they find just doesn't
translate well when they're having to investigate a cloud
incident. And so pursuing cloud specific
sources of visibility is really critical as well.
So I would say those are some of the most important themes we see
right now. Good.
Advice. OK, visibility and
accountability. If you can't see it, you can't
account for it. So keep improving your
visibility until you can answer the three basic questions of who
did what and when. Amazing and.
It's a another thing that written in the vision space is
eyes on the ball. I think that I agree with what
you said, but we here today and especially now with AI, there's
so much noise and, and you know, there are new novel techniques
that you see and you read about. But in the end, most of the real
incidents that we see, they start from the areas that we
already know from, you know, data leaks from the
infrastructure to even when there are new techniques, you
always have to stick to the frameworks and principles that
you already have for your involvements today and think how
you can extend them to these new type of Nova threats.
Amazing. Thank you all, and thank you for
listening to our live episode of the podcast.
If you want to listen to more, you can on wherever you listen
to your podcasts. And thank you for joining us.
Thank you.