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Speaker 1: so use that code. Hello there, I'm Chuck Todd and
Speaker 1: welcome to another episode of Sunday Night Tonight. It's a
Speaker 1: one topic deep dive on artificial intelligence, and maybe the
Speaker 1: simplest way to frame the AI debate right now is this,
Speaker 1: Americans are being asked to trust a new technological revolution
Speaker 1: at a moment when they're not entirely convinced the last
Speaker 1: one worked out so well.
Speaker 2: Think about it.
Speaker 1: Search engines once felt empowering, social media once felt connective.
Speaker 1: Online marketplaces once felt simpler and more useful. But over time,
Speaker 1: many of those same technologies became harder to trust, distorted
Speaker 1: by algorithms, advertising, misinformation, polarization, and the constant incentive to
Speaker 1: monetize our attention. So when Selagon Valley tells the public,
Speaker 1: don't worry, this next technological leap will change everything for
Speaker 1: the better, it's understandable that many of us respond with
Speaker 1: a little skepticism, or maybe a lot of skepticism, because
Speaker 1: AI isn't some distant concept anymore. It's already shaping classrooms, workplaces,
Speaker 1: financial systems, warfare, and even the information we consume every day,
Speaker 1: which raises two very different but deeply connected questions. One,
Speaker 1: how do we protect consumers from AI? And two how
Speaker 1: do we protect country from AI? So tonight we're going
Speaker 1: to explore both sides of this challenge.
Speaker 2: Miriam Vogel joins me. First.
Speaker 1: She chairs the National AI Advisory Committee and leads an
Speaker 1: organization called Equal AI that's focused on governance, fairness, and
Speaker 1: consumer trust. She's trying to answer the question how do
Speaker 1: we build guardrails around AI before the technology outruns the
Speaker 1: public's confidence in it. Then I'll be joined by and
Speaker 1: Newburger on the issue of global security, because while Miriam
Speaker 1: Vogel is trying to make sure AI doesn't discriminate against
Speaker 1: you when you apply for a mortgage, and Newburger is
Speaker 1: trying to make sure that same technology isn't used by
Speaker 1: a foreign adversary.
Speaker 2: To shut down the bank.
Speaker 1: Altogether, it's the same technology, it's two completely different battlefields,
Speaker 1: and somewhere between innovation and regulation, between opportunity and fear,
Speaker 1: is the question governments around the world are now struggling
Speaker 1: to answer in real time, how do you govern a
Speaker 1: technology that evolves faster than governments themselves. Tonight's conversation comes
Speaker 1: to you from the Unconventional Diner. It's one of Washington's
Speaker 1: great neighborhood spots, known for taking classic American comfort food
Speaker 1: and giving it an inventive twist, which trankully feels like
Speaker 1: the perfect backdrop for a conversation about technology trying to
Speaker 1: reinvent itself in our everyday life.
Speaker 2: So let's get started. Very in Bogo. It's nice to
Speaker 2: see you.
Speaker 1: Let me start with tell me a little something about
Speaker 1: what equal AI is supposed to be.
Speaker 3: So we created equal AI seven and a half years
Speaker 3: ago because we saw the emergence of AI. We saw
Speaker 3: that there was a deep enthusiasm starting to root across
Speaker 3: the country and the globe, but without a lot of
Speaker 3: understanding of the potential risks and the need for guardrails,
Speaker 3: and so we created it that time more of a
Speaker 3: temporary place where companies, policymakers, lawyers, other key stakeholders could
Speaker 3: align on guardrails. How do we make sure that this
Speaker 3: really serves people? How do we contain risks and make
Speaker 3: sure that we can optimize use. So flash forward seven
Speaker 3: and a half years, it turns out we have not
Speaker 3: gone out of style in terms of meeting the big
Speaker 3: place to align on guardrails and best practices.
Speaker 1: The American electorates skepticism and AI seems very rational to me.
Speaker 1: When you look at Web one point zero search and commerce,
Speaker 1: the user experience at first was great, then the.
Speaker 2: User became the product.
Speaker 1: Social media, the initial launch seemed great, building up communities.
Speaker 1: Then what happened algorithms, the user becomes.
Speaker 2: The product, and so we don't like social media.
Speaker 1: We're more frustrated than ever with how the regular old
Speaker 1: Internet works, how Amazon or any of the online commerce
Speaker 1: marketplaces work.
Speaker 2: And so to me, it's very rational.
Speaker 1: They look at AI and, oh, great, yet another way
Speaker 1: that just is going to exploit us.
Speaker 3: I think you're absolutely right. We bring a lot of
Speaker 3: tech baggage to this conversation, and I think There's another
Speaker 3: layer of a problem here, and that's that we're not
Speaker 3: listening to people we're expecting them to adopt AI. We're
Speaker 3: talking about you say we.
Speaker 2: Who do you feel like you're speaking for when you.
Speaker 3: Say week leadership in general, corporate leadership, policymakers, I think
Speaker 3: civil society. I think we all need to do a
Speaker 3: better job of listening to people.
Speaker 2: Know.
Speaker 3: We've published a book in October called Governing the Machine
Speaker 3: where we outline nine categories of risk, and each of
Speaker 3: those categories is very meaningful, and we need to be
Speaker 3: taking those into account when we're talking about how people
Speaker 3: and why people should be using AI. So if your
Speaker 3: concern is privacy, we can't dismiss that. We need to
Speaker 3: talk about how we're going to address your privacy concerns
Speaker 3: before we can expect you to be using it. I
Speaker 3: think we need to be doing a better job of
Speaker 3: having a nuanced conversation where first of all, we're telling
Speaker 3: people how we're using AI. Too many people don't know
Speaker 3: that they have been using it all day, every day,
Speaker 3: unlocking their phones, meeting their news feeds, scrolling through social media.
Speaker 3: When to let people know that they are using it,
Speaker 3: then we need to let people know what safe use
Speaker 3: looks like. We need to be establishing what the frameworks
Speaker 3: are that companies and organizations are putting in place so
Speaker 3: they know who to trust and what trustworthy governance looks like.
Speaker 1: All of this makes sense, except it doesn't look like
Speaker 1: anybody in the industry is even remotely thinking about this.
Speaker 1: Whether it's Open AI, whether it's Plowed, whether it's the
Speaker 1: other Gemini, whether it's Rock everybody. I mean, when you
Speaker 1: look at the amount of money being invested in infrastructure
Speaker 1: to build out even more AI, that means there's a
Speaker 1: whole bunch of investors looking to get their money extract
Speaker 1: profits out of this. Nobody seems to be worried about guardrails.
Speaker 1: In fact, I look at the whenever a new update concept,
Speaker 1: it feels like, you know, there's how we so powerful.
Speaker 1: We're not sure we can you know, we're not sure
Speaker 1: that the new cybersecurity can handle it. It's like a
Speaker 1: marketing ploy, not actually a serious attempt to add actually
Speaker 1: doing baby steps.
Speaker 3: Well, you're not wrong. A report last year from McKenzie
Speaker 3: said that over eighty eight percent of companies are using
Speaker 3: AI in at least one pivotal function that was a
Speaker 3: year ago. It's probably a much higher number now, but
Speaker 3: at that point only a third had any AI governance
Speaker 3: in place. The World Economic Forum had a report earlier
Speaker 3: this year that said only less than one percent of
Speaker 3: companies have strong AI governance frameworks in place. So you
Speaker 3: are correct that it is not something we're seeing a
Speaker 3: lot of our hearing a lot about. Although I have
Speaker 3: the privilege of working with companies that really do prioritize
Speaker 3: AI governance, so I can tell you the good news
Speaker 3: is there is alignment across industry for companies, for leaders
Speaker 3: that care about AI governance. You'll not see a lot
Speaker 3: of distinction between what those best practices are across the
Speaker 3: companies and organizations that have strong AI governance frameworks. You'll
Speaker 3: see visibility into how they're using AI. You'll see accountability
Speaker 3: from the top. It has to start at the top,
Speaker 3: but then across divisions so people understand who's the point person,
Speaker 3: what they should be looking for. You'll see not just
Speaker 3: AI principles, but operationalization.
Speaker 1: I feel like you're describing what a company that might
Speaker 1: adopt an AI platform into their business and integrate it,
Speaker 1: that they may be taking care in trying to do
Speaker 1: these things.
Speaker 2: That's not who I'm talking about. I'm talking about.
Speaker 1: The purveyors themselves, whether it's Open AI, Anthropic Google, with
Speaker 1: Gemini or ix whatever we're calling the elon of musk
Speaker 1: operation these days. What evidence that there is any interest
Speaker 1: in good governance in those far entities.
Speaker 3: I can tell you the ones that I work with
Speaker 3: so in our Badge program for senior executives, in our
Speaker 3: agentic AI summit we had this week, we have some
Speaker 3: of the best minds at Google, Deep Mind, at AWS
Speaker 3: and Amazon Microsoft who are being very intentional about their
Speaker 3: governance frameworks. You can look online to see what they've implemented.
Speaker 3: You can talk to their senior executives who will be
Speaker 3: very well versed in what they are doing intentionally and
Speaker 3: across their companies to make sure they have strong governance
Speaker 3: in place. But I don't want to dismiss the deployer
Speaker 3: role because most of their applications that will have access
Speaker 3: to and that we're currently using, are from those large
Speaker 3: companies small companies that are using AI who also have
Speaker 3: a key role in making sure that it's safe.
Speaker 1: If the industry is so concerned about governance, why are
Speaker 1: they fighting Why are they fighting state based regulation? Why
Speaker 1: is it so important to them to convince the Trump
Speaker 1: administration to do whatever it takes to prevent.
Speaker 2: What many of us refer to as the laboratories of democracy.
Speaker 1: In state government's figuring out a way to protect consumers.
Speaker 2: From a state level.
Speaker 3: Yeah, and it's a fair question. I think, first of all,
Speaker 3: there's nuance in what different companies are and are not
Speaker 3: pushing for. But to your question about state versus federal
Speaker 3: I'm a former general counsel, so I have sympathy into
Speaker 3: how hard it is to respond to a patchwork of laws.
Speaker 3: I think it's in everyone's best interest that there're certainty
Speaker 3: and clarity, not just across the country, but across the globe.
Speaker 3: I mean, we're talking about AI. It doesn't have boundaries,
Speaker 3: and users across the globe are going to need that
Speaker 3: it's safe and lawful. So the war we have clarity
Speaker 3: and certainty and uniformity, the easier it is to be compliant.
Speaker 1: I understand that. But if you have a dysfunctional Congress,
Speaker 1: it is not a functioning body of government. They're at
Speaker 1: war with each other. You have a we don't have
Speaker 1: a federal government interested in these safeguards, So why step
Speaker 1: on the state governments from Trump.
Speaker 3: I can't speak to that. What I can say is
Speaker 3: I do personally have some great experience of good things happening,
Speaker 3: the very thoughtful, proactive things happening in the government space.
Speaker 3: So for instance, at our agentic AI summit this week,
Speaker 3: we got to talk with the Acting Secretary of Labor
Speaker 3: who's being very forward thinking and proactive about preparing the
Speaker 3: workforce and employers for an AI economy. They have an
Speaker 3: AI literacy framework. Most people don't know what AI literacy is,
Speaker 3: and so they've broken it down for employers so they
Speaker 3: can adopt and learn from this framework. They have text
Speaker 3: based AI literacy tools, and that's really important because when
Speaker 3: we're thinking about AI literacy, not every American has access
Speaker 3: to Wi Fi, and so if you have an application online,
Speaker 3: many people will not have access to it. So they've
Speaker 3: created a digestible text based program where people can get
Speaker 3: a ten minute overview and introduction to AI. That will
Speaker 3: not make everybody AI ready workforce in terms of being
Speaker 3: an end and neuro computer scientists, but at least gets
Speaker 3: people over the hump of knowing what AI is and
Speaker 3: how they're using it.
Speaker 2: I feel like we're trying to do multiple things here.
Speaker 1: That sounds like we're on the education side, how to
Speaker 1: get people up to speed, how to get people comfortable
Speaker 1: with using if it was like trying to teach me
Speaker 1: a body, he's a personal computer. Well, what about the
Speaker 1: consumer protection side? And that's where it seems as if
Speaker 1: the industry is petrified of any attempt at a regulation
Speaker 1: that's consumer protection because they say, well, that's stepping on
Speaker 1: our ability to be able to either beat China in
Speaker 1: the AI race or keep up with China.
Speaker 3: You're right, we need to have our eye on the
Speaker 3: state houses and the congresses across the globe to understand
Speaker 3: what new laws are coming down. But we also should
Speaker 3: not lose sight where I think most of this plays out,
Speaker 3: and that's in the courthouses. I think most of how
Speaker 3: AI is regulated will come down to how courts are citing,
Speaker 3: how current laws on the books will be applied to
Speaker 3: AI use. We have three chapters in the book on
Speaker 3: how different countries across the globe have laws currently in
Speaker 3: place that will be impactful. We've seen it play out
Speaker 3: in IP and copyright that will only increase in the
Speaker 3: years to come. We've seen it in several other privacy
Speaker 3: areas and so forth. I think we can expect.
Speaker 2: All eminating out of Europe.
Speaker 3: Right we have no way of case law here in
Speaker 3: the US as well. For sure, I think will increasingly
Speaker 3: see towards law consumer protection laws, contract laws where we'll
Speaker 3: be deciding who has liability because the lawyers are catching
Speaker 3: on to this and they will make sure that there
Speaker 3: is liability for those that are taking their eye off
Speaker 3: the ball in terms of safety and what the negative
Speaker 3: risks and impacts could result in.
Speaker 1: Let's assume we had a functioning, feral legislative branch that
Speaker 1: was going to look at this not as seeing which
Speaker 1: side is going to contribute the most amount of money
Speaker 1: to their mentor, because that's the other part of this.
Speaker 1: All of the AI associated super PACs I think are
Speaker 1: sort of warping the conversation on regulation. Let's assume we
Speaker 1: didn't have this political cloud hanging over what would what
Speaker 1: is a recommendation of what first step regulation.
Speaker 2: Would look like with AI bodels and what.
Speaker 1: Entity in the government is best equipped to regulate these ais.
Speaker 3: It's a great question. I think there's more. We need
Speaker 3: more certainty on what the expectations are out of companies.
Speaker 3: We need to understand The basic question with AI use
Speaker 3: that we always need to be able to answer, which
Speaker 3: is for who could this fail? We need to make
Speaker 3: sure that AI deployment are designed around thinking about what
Speaker 3: the human use cases are and what the actual application are.
Speaker 3: And I think there's a role for every agency and government.
Speaker 3: They're all going to be using AI personally if they're
Speaker 3: not already. We know there's an executive order mandating it's
Speaker 3: being used across agencies as well as the oversight, but
Speaker 3: I don't want to lose sight of the other ways
Speaker 3: that this is where leadership is popping up. So with
Speaker 3: Equal AI, we've been doing an AI literacy tour across
Speaker 3: the country to listen to people, to hear what they're seeing,
Speaker 3: what they're worried about, but also to elevate leadership. What
Speaker 3: it looks like when AI leadership is what it looks
Speaker 3: like when we have AI leadership. So for instance, we've
Speaker 3: been to Tennessee, We've been to Arizona. We're having conversations
Speaker 3: and cities across the country where we're seeing mayors who
Speaker 3: are using it for smart traffic management, creating resource hubs
Speaker 3: so that small businesses are in touch with academics, training,
Speaker 3: our next gen, but also getting small businesses AI ready.
Speaker 3: I think there's a lot of tools that we have
Speaker 3: now at our disposal where we have foundations of leadership,
Speaker 3: and so we just need to understand what does that
Speaker 3: look like. You know, at ASU where they are leaning
Speaker 3: into AI USE, they're giving students the capabilities of generative
Speaker 3: models and that even how will I am teaching augentic
Speaker 3: AI used to their students. So I think we actually
Speaker 3: have a lot of opportunities for leadership that we shouldn't
Speaker 3: be overlooking.
Speaker 1: I hear you, but this I mean talk about scattershot
Speaker 1: like we just described, seems the scatter shot and frankly,
Speaker 1: and again I'm liking you'd like defend all of these
Speaker 1: and and I don't. I don't mean to try to
Speaker 1: put you on the defensive, but it does appear to
Speaker 1: be that it's all scattershot, and it's really those that
Speaker 1: have the ability to to maybe even curiosity to be
Speaker 1: first movers and to learn this stuff. We'll do it,
Speaker 1: but we I mean, it does feel as if this
Speaker 1: is way ahead of anny guardrails that are even remotely
Speaker 1: ready to be used.
Speaker 3: You know, I think you're getting at something check that
Speaker 3: we need to be prepared for AI is a disruptor,
Speaker 3: and I think we need to look at this as
Speaker 3: a disruption for good. We can't operate in silos anymore.
Speaker 3: AI connects us all and makes us all dependent on
Speaker 3: one another to ensure that we know how each other
Speaker 3: fit into the use cases. We can't imagine that only
Speaker 3: within our company are we going to be impacted by
Speaker 3: our AI use We can't imagine only within our state.
Speaker 3: There's overlap across boundaries when we're talking about AI, and
Speaker 3: so we're going to have to break silos. We're going
Speaker 3: to have to rethink culture in organizations where you can't
Speaker 3: just you know, pinpoint which agency or which department is
Speaker 3: in charge of AI. It is across organizations, it is
Speaker 3: across states, and you're right, it's dissipated. It's not the
Speaker 3: way we're constructed. But we're going to have to rethink that's.
Speaker 2: Talking about it.
Speaker 1: I guess, agentic AI, do you really think we're ready
Speaker 1: for AI to make decisions without a human interface.
Speaker 3: I think there are a lot of concerns about the
Speaker 3: state of readiness with agentic AI because of the statistics
Speaker 3: we talked about that if less than one percent of
Speaker 3: companies have strong AI governance in place, they are not
Speaker 3: prepared for AI deployment, let alone agentic AI deployment. I
Speaker 3: mentioned we had an agentic AI summit this week where
Speaker 3: we were looking at what is the optimal use cases
Speaker 3: of a dentic AI. What does it look like if
Speaker 3: we get this right. We looked at challenges, We looked
Speaker 3: at the map between where we are today and how
Speaker 3: we will need to prioritize the steps to get to
Speaker 3: the optimal end state. And you could only do this
Speaker 3: if you have strong governance in place. And again, I
Speaker 3: think we need to break down what we mean by governance.
Speaker 3: You know, too many people think it's a government responsibility
Speaker 3: or a stop gapped innovation. We really need to understand
Speaker 3: that governance is something that leadership needs to be held
Speaker 3: accountable for and take ownership of whatever your organization is
Speaker 3: because you're going to be using AI, and we need
Speaker 3: to be realizing that it is the norms, the culture,
Speaker 3: the practices that build trust and really unlock the benefits
Speaker 3: of this innovation.
Speaker 1: Is you know, and this could happen because of the
Speaker 1: populist backlash that I think is coming right now with AI.
Speaker 2: Right some of it has to do with what I
Speaker 2: talked about with social.
Speaker 1: Media, Others will be fear of AI job displacement, and
Speaker 1: you can just feel that there is certainly exploitable anger
Speaker 1: out there in our politics that you could see if
Speaker 1: Congress passed the law next that said no AIP, you
Speaker 1: cannot use agentic AI.
Speaker 2: Period.
Speaker 1: There has to be a human interface at the very
Speaker 1: very end, whether it's confirming a purchase, confirming a decision,
Speaker 1: that it cannot be.
Speaker 2: Left only to.
Speaker 1: The AI platform.
Speaker 2: What would the industry say to something.
Speaker 3: I think it would say that you're a little late
Speaker 3: because it is being deployed. So we cannot slow down innovation.
Speaker 3: We can speed up governance. That's our only option. Governance
Speaker 3: is the seatbelts in a car. It's the speed limits.
Speaker 3: It's knowing there's a doometer. We wouldn't get in a
Speaker 3: car if we wouldn't if we weren't sure that we
Speaker 3: had these.
Speaker 1: The industry bought seatbelts literally until the debts piled up.
Speaker 1: I mean, are we going to have the industry fight
Speaker 1: regulation literally until there are what we have right now
Speaker 1: with social media, a pile up of lawsuits that hey,
Speaker 1: they made a defective product.
Speaker 3: Well that's why we need to have more of alignment
Speaker 3: on what these best practices are, What are the safeguards
Speaker 3: that everyone should be adopting when they're you using AI,
Speaker 3: because then we know what it looks like to be
Speaker 3: doing this well. I think at the end of the day,
Speaker 3: we've seen time and again that people will not adopt innovation.
Speaker 3: I mean, look at the ROI on investment. You see
Speaker 3: tremendous investment in AI. We're not seeing the ROI for
Speaker 3: many companies, and I'm very convinced, and the data show
Speaker 3: it's because they don't have governance in place and because
Speaker 3: they haven't taken the time to deploy AI literacy.
Speaker 1: Is there anything to be learned as as we try
Speaker 1: to catch up on governments governance with AI, about.
Speaker 2: What we didn't do with social media?
Speaker 3: Yeah, there's a lot of lessons to be learned. Where
Speaker 3: to begin, We need to first of all understand the
Speaker 3: pace we are. We moved quickly with social media. I
Speaker 3: think that innovation was adopted with record speed. For our
Speaker 3: past innovations, usually it takes decades for adoption. That happened
Speaker 3: in a pretty integrated way within a decade. Within less
Speaker 3: than that, AI will continue to scale at a pace
Speaker 3: that is exponentially higher than that, So I think any
Speaker 3: comfort people can take in waiting should be disabuse. There's
Speaker 3: no opportunity to wait. AI is being used in our
Speaker 3: daily life every day. It is being used in our workplaces,
Speaker 3: and so there needs to be a clear path towards
Speaker 3: ensuring its safety and ensuring there's more agency, more people
Speaker 3: understand why and how to use AI. I mean, we're
Speaker 3: seeing these very problematic adoption gaps, not just across the
Speaker 3: fifty percent of the US that is not interested in
Speaker 3: adopting AI, but we see a gender gap in age gap,
Speaker 3: education and wealth, and even a regional gap where in
Speaker 3: the middle of the country there's more anxiety towards AI
Speaker 3: than on the coasts. These are all understandable. It's communities
Speaker 3: that have deep concerns that need to be heard and
Speaker 3: addressed before they're going to be willing to use AIA.
Speaker 2: Have you had any luck with the AI.
Speaker 1: Platform companies in understanding what they have or do you
Speaker 1: feel like they're just in a hurried anyday.
Speaker 3: I think there's several conversations going on right now. There's
Speaker 3: certainly that competition once amongst one another, with amongst.
Speaker 1: Us open AYE trial, it's not reassuring to watch all
Speaker 1: of this sort of dirt get circulated out, but you're
Speaker 1: just sitting there going it's just a bunch.
Speaker 2: Of people trying to make more money off of us.
Speaker 1: Again, this doesn't feel like anybody's actually been concerned about
Speaker 1: what the technology.
Speaker 2: Is going to do to the public.
Speaker 3: Well, I think the more we can do to empower
Speaker 3: the public, policymakers, lawyers to understand what good outcomes look like,
Speaker 3: the better off we are. I think you know it's in.
Speaker 2: You're right.
Speaker 3: It's an interesting look into what's happening under the hood.
Speaker 3: But again, people have agency here and there's many yders
Speaker 3: across the spectrum. Journalists have brought it to account many
Speaker 3: of the AI use cases that have been problematic, and
Speaker 3: that's where you see changes. So the more there's clarity
Speaker 3: in what good looks like and the more we're able
Speaker 3: to pressure test what some of these problematic outcomes look like,
Speaker 3: the more we can have our expectations in place. But again,
Speaker 3: I wouldn't lose sight of some of the forces, the
Speaker 3: market forces we have in place, whether it's ROI or
Speaker 3: the litigation we're seeing increasingly in the courts. Last year
Speaker 3: there was a the AI Index report. They said there
Speaker 3: was a six times increase in litigation over sixty years.
Speaker 3: I'm curious to see how much exponentially higher that is.
Speaker 1: Now, all right, let's try to let's try to end on.
Speaker 1: Give me some best practices of entities out there that
Speaker 1: do seem to be prioritizing governments.
Speaker 3: When you're seeing governance, there's at least five overlapping indicators.
Speaker 3: There's visibility. Most companies have not done a survey to
Speaker 3: understand where they are using AI across enterprise.
Speaker 1: You think there's some companies their CEO doesn't even know where, yeah,
Speaker 1: AI is being deployed.
Speaker 3: It was a very problematic study out a year ago
Speaker 3: where they looked for at HR uses of AI, which
Speaker 3: is one of the most predominant use cases of AI.
Speaker 3: They surveyed the global companies and about eighty eight percent
Speaker 3: of the chief HR officers confirm their using AI in
Speaker 3: HR functions. At those same companies, about sixty two sixty
Speaker 3: four percent of CEOs indicated they were using AI and
Speaker 3: their HAAR functions. Forty eight percent of general counsels indicated
Speaker 3: the same. So, yes, I think there is a huge
Speaker 3: problem of people not knowing, not having visibility into how.
Speaker 2: AIS is inside their own.
Speaker 3: Inside their own companies. We can do this in our
Speaker 3: own homes too. Second of all, you have to have accountability.
Speaker 3: You have to have someone in your C suite who
Speaker 3: cares about this, who takes ownership of this, who make
Speaker 3: sure that there's the budget. But even equally important is
Speaker 3: that there's alignment across dvice. Too many people think that
Speaker 3: this is the innovations of the past where you can
Speaker 3: assign it to one department, and AI doesn't work that way.
Speaker 3: AI is in every news media.
Speaker 1: Will have a digital team. It's like all media is
Speaker 1: on digital. I remember thinking that back then. So, yeah,
Speaker 1: there's such an AI team. It's like saying you're going
Speaker 1: to have a deskot you.
Speaker 3: Got it, you got it. You have to have that
Speaker 3: C suite level buy in and interest. But then you
Speaker 3: also need that because a lot of this is going
Speaker 3: to play out in the front lines. Your employees, your
Speaker 3: consumers are going to find some of the errors play out,
Speaker 3: and you need them to know who to report them
Speaker 3: to and that it's safe to be reported up.
Speaker 1: The play is the realistic first regulation, then just simply transparency.
Speaker 3: Transparency. I think that's a complicated issue because it has
Speaker 3: to be stated in such a way that could be
Speaker 3: understood meaningfully.
Speaker 2: Take surveillance pricing.
Speaker 1: I know New York State passed the law about surveillance
Speaker 1: pricing that if it was done with AI, it had
Speaker 1: to be set how to say, so New York does
Speaker 1: this regulation. But even if you got to know it
Speaker 1: was a good example to wash the post, it had
Speaker 1: to put a little disclaimer that they were using surveillance
Speaker 1: pricing on their subscription services. And somebody who lived in
Speaker 1: Oklahoma got that warning even though Oklahoma didn't have that law.
Speaker 2: New York State has that law.
Speaker 1: But it felt like, well, all right, that's a case
Speaker 1: where transparency at least is a way to at least
Speaker 1: give the consumers something.
Speaker 3: Transparency is a start, but we don't want the human
Speaker 3: fatigue of checking I'll accept the cookies so I can
Speaker 3: get quickly to the website of where I want to go,
Speaker 3: And we don't want it to be written in a
Speaker 3: way that they can't understand. So I think absolutely transparency
Speaker 3: is key. Where are you using AI? What have you
Speaker 3: tested for? I mean, let's talk about in the medical
Speaker 3: use cases, which are some of the most exciting use
Speaker 3: cases of it. If you're going to tell me that
Speaker 3: your cancer detection is eighty eight percent successful. I need
Speaker 3: to know for whom I need to know if that
Speaker 3: is true for the patients that you're talking to. Was
Speaker 3: were they appropriately represented in the satisfaction rate and the
Speaker 3: success rate of that product? For whom could this fail?
Speaker 1: How are we going to get most regulation due to
Speaker 1: court fights rather than due to legislation?
Speaker 2: Is that true? Is that real mean? And what that
Speaker 2: means is civil litigation.
Speaker 3: I think in the short term there's a lot happening
Speaker 3: in the courts that people should not overlook, a lot
Speaker 3: that will impact their use, their rights, their company. That
Speaker 3: if they are not keeping their eye on what's happening
Speaker 3: across the country and frankly across the globe in the courthouses,
Speaker 3: they're missing a lot of the liabilities they will be
Speaker 3: walking into if they do not have good governance in place.
Speaker 1: Well, I'll say this, if you're a legislator and you're
Speaker 1: watching this, get our booked, because it does sort of
Speaker 1: in a very thought in a good way, layout sort
Speaker 1: of how garden You're trying to figure out what grdros
Speaker 1: to build and I feel like your book does sort
Speaker 1: of layout.
Speaker 2: Okay, if you want to do this, Cardrail, this is
Speaker 2: the way to do it. Thank you, check Ary, appreciate.
Speaker 3: You saying that, thank you so much for having me.
Speaker 2: Well.
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Speaker 1: have transitioned from governments into the private sector. Still in
Speaker 1: the AI space, let's talk about AI security, especially we've
Speaker 1: got this China summit coming up between She and Trump
Speaker 1: and apparently AI governance is now going to be on
Speaker 1: the agenda. So let's start there. What should global AI
Speaker 1: governance look like between superpowers and how? And is nuclear
Speaker 1: proliferation or non proliferation the model?
Speaker 4: So, first of when we think about AI security, there's
Speaker 4: really two lenses to think about it through. One is
Speaker 4: the risk that AI brings, for example, in accelerating the
Speaker 4: depth and speed of cyber attacks.
Speaker 5: And the second is the risk of AI.
Speaker 4: If models are not secure, if models can be poisoned
Speaker 4: and of themselves. And then if they're embedded into whether
Speaker 4: it's a decision about whether to loan somebody money or a.
Speaker 5: Decision about we're in the electricity.
Speaker 4: Grid, thank you so much. One optimizes, then they bring
Speaker 4: additional risks. So we think about it in.
Speaker 5: Both of those lenses.
Speaker 4: And to your point about global governance, it's the question
Speaker 4: we used to have often about cyber governance.
Speaker 5: What are the rules in the room? How can countries
Speaker 5: use these dual use technologies.
Speaker 4: They're both defensive they help us secure infrastructure, for example,
Speaker 4: and they're offensive they help between countries. They may be
Speaker 4: used offensively in the context of a crisis or conflict
Speaker 4: to destabilize infrastructure. So what should the rules that they
Speaker 4: will be when fundamentally there are no borders of the rules.
Speaker 1: Well, it's similar to nukes, right, we had nuclear energy
Speaker 1: and one set of rules versus nuclear weapons, right, positive
Speaker 1: and negative.
Speaker 4: It's very different in that it is a private sector
Speaker 4: developed technology that's broadly available, versus nukes that are primarily
Speaker 4: developed by governments with very strict rules around.
Speaker 1: For the funny you say that, I've talked to I
Speaker 1: have a retired general friend of mine who is obsessed
Speaker 1: with the fact that he thinks AI never should have
Speaker 1: been allowed to be developed with the private sector first,
Speaker 1: that there always should have been like with nuclear technology,
Speaker 1: where it was essentially government driven with academic research and
Speaker 1: sort of bringing the private sector into the government sphere
Speaker 1: and the internet itself private sector into the government sphere.
Speaker 2: It's too late to go there now with AI. But
Speaker 2: was that a mistake?
Speaker 5: You know?
Speaker 4: The power of the American innovation system is that we
Speaker 4: have the laws, the financial markets, and the investment in
Speaker 4: R and D that happens in the private sector. That
Speaker 4: means technology, drug R and D, drugs is primarily coming
Speaker 4: out of the private sector. Yes, there's some investment from government,
Speaker 4: but it's mostly there. So I think the strength of
Speaker 4: our system is that innovation comes out of the private sector.
Speaker 5: So should have could a? I think it's just it's it.
Speaker 2: Doesn't matter what the philosophy is.
Speaker 6: We are where we are, so so let's talk about
Speaker 6: you talk about cyber and a global and you try
Speaker 6: you develop something in the Biden.
Speaker 2: Administration having to do with at least coming to a
Speaker 2: collective agreement on rants.
Speaker 1: Explained absolutely, because it is gold apply for AI, right, it's.
Speaker 5: An interesting approach. You're right to apply to AI. You know.
Speaker 4: So in the past we would often think about the
Speaker 4: big risk of cyber as espionage. We talked for years
Speaker 4: about how China was growing at strategic technology sector by
Speaker 4: stealing intellectual property from America. And you know, governments have
Speaker 4: been trying to steal each other's secrets.
Speaker 5: Whether as human assets is five. But if we say
Speaker 5: that moved to digital because it's so much easier. You
Speaker 5: don't have to get a guy into a as old
Speaker 5: as a country.
Speaker 2: Bad borders on countries, it exactly.
Speaker 1: I'm sure the feudal lords were also spying on each other,
Speaker 1: and it's.
Speaker 5: So much easier in cyberspace.
Speaker 4: You don't have to get a human past a fingerprint
Speaker 4: check into a country. So that was the way we
Speaker 4: looked at it. It changed with the advent of two things.
Speaker 4: One is cryptocurrency and the second a criminal ecosystem that
Speaker 4: essentially built out cyber tools and that enable them to
Speaker 4: use those tools to disrupt companies, infrastructure, hospitals around the
Speaker 4: world and get.
Speaker 5: Paid for it easily.
Speaker 2: It's the so called dark web, the dark.
Speaker 4: Web plus the financing right, because the dark web is
Speaker 4: a way that people can just communicate outside the formal
Speaker 4: Internet ecosystem. Essentially, as you know, every IP address has
Speaker 4: a matching mean that makes it easier for us to
Speaker 4: find Amazon dot Com without necessarily knowing its Internet.
Speaker 2: For right, everybody's address exactly right.
Speaker 4: On the Internet dark web, there's just the addresses without
Speaker 4: the to who it is, so people have to know
Speaker 4: where they're going. What changed in ransomware, to your point,
Speaker 4: was that you had a criminal ecosystem that now had
Speaker 4: a way to move money outside traditional banking systems. And
Speaker 4: in many ways, while there are rules around cryptocurrency anti
Speaker 4: money laundering done by the Financial Action Task Force, they
Speaker 4: are not implemented around the world for various reasons. So
Speaker 4: as a result, you would have these safe and some
Speaker 4: countries provided safe haven. So Russia, for example, looked away
Speaker 4: or quietly enabled criminals with some ties to their.
Speaker 2: Korea.
Speaker 1: There was a whole sort of access of maybe unfriendly
Speaker 1: countries to.
Speaker 4: The West that were doing this right exactly, and those
Speaker 4: actors ended up disrupting infrastructure to get paid a ransom,
Speaker 4: and in many cases, like when you think about Britain's
Speaker 4: National Health system, disrupted the operations of hospitals.
Speaker 5: So to them, they're ready to pay.
Speaker 4: So the debate became while for one entity paying a
Speaker 4: ransom could be the fastest way to get back online,
Speaker 4: every payment fuels the ecosystem, right, So overall we're enabling
Speaker 4: these attackers to do more and more of these attacks.
Speaker 4: It's essentially incentivizing them. So in the last administration, we
Speaker 4: formed something called the Counter Ransom Initiative. We said, we
Speaker 4: need a way to coordinate among countries to put it
Speaker 4: in place the practices, getting countries to implement anti monitor
Speaker 4: laundering rules for cryptocurrency, disrupting the operations of these ransomware attackers,
Speaker 4: and fundamentally in each of our countries, trying to put
Speaker 4: in place policies to not allow ransoms.
Speaker 1: How hard is it though, to get I mean, does
Speaker 1: that need to be like a again? Does that look
Speaker 1: like a treaty like we have with nuclear weapons and
Speaker 1: what we have with biological weapons?
Speaker 2: Is that what this would eventually look like?
Speaker 5: So it's a really good question.
Speaker 4: On the positive side, we formed the Counter Ransom Innership
Speaker 4: that grew to eighty countries, and we had working groups
Speaker 4: that did things like countries sharing with each other threat
Speaker 4: and so if a particular technique was used against the
Speaker 4: hospital in the UK, that was shared across the governments
Speaker 4: so that another country would then inform their hospitals keep
Speaker 4: an eye out for that, protect against this. In that way,
Speaker 4: that was successful because it was sensitive sharing and it
Speaker 4: expanded it to more countries than had ever been shared
Speaker 4: together before. A second success was in training countries and
Speaker 4: how to look for cryptocurrency money laundering. Cryptocurrency enables cheaper
Speaker 4: and faster transactions for all kinds of people, people sending
Speaker 4: remittances back home, for example, but it also enables payment
Speaker 4: of ransoms quickly. So we among different countries who had
Speaker 4: more sophisticated systems, we trained other countries, and we could
Speaker 4: also use more offensive capabilities to say if a given
Speaker 4: country is not acting, it can be put on a
Speaker 4: gray list, to say other countries say we're not going
Speaker 4: to do business with you, because you really need to.
Speaker 4: The stick is essentially doing routine business transactions, not in cryptocurrency.
Speaker 4: Nobody wants to be on a grade list which says
Speaker 4: they're not following the rules of anti money laundering because
Speaker 4: it puts their entire financial system under question. As well
Speaker 4: as another stick, which is sanctioning some of these exchanges
Speaker 4: that were moving a preponderance of illicit money. One was
Speaker 4: called Tornado dot Cash, and we did the first ever
Speaker 4: sanctioning of that entity to say.
Speaker 5: We're going to bring the traditional rules.
Speaker 4: Of enforcing the rules for banks to cryptocurrency virtual asset
Speaker 4: service providers. So we extended the traditional rules for fiat
Speaker 4: currency to cryptocurrency. So that's where it worked.
Speaker 2: Where didn't it work yet.
Speaker 4: And I guess actually a third one where it worked
Speaker 4: is the governments agreed that no governments would pay ransoms.
Speaker 5: That was the third way.
Speaker 4: We got agreement and a public statement saying we all agree,
Speaker 4: And the public statement was important because then we called
Speaker 4: ourselves accountable. Where we had more trouble was a fierce
Speaker 4: debate about what countries do to actually disincentivize ransom payments
Speaker 4: by private sector actors. In the US, it was a
Speaker 4: fierce policy debate. What we wanted to do was say
Speaker 4: we would announce in six months that ransom payments were
Speaker 4: not permitted unless a permission was requested from the Department
Speaker 4: of Justice for a national security emergency.
Speaker 2: Could be illegal.
Speaker 5: It would be yes, essentially, so I paid.
Speaker 1: A ransom, charges would be brought against the person that
Speaker 1: paid the ransom.
Speaker 4: Or if you wanted to pay a ransom, you'd have
Speaker 4: to come to the Department of Justice and they might
Speaker 4: see that. For example, if a hospital wanted to pay
Speaker 4: a ransom. What we wanted to do the goal of
Speaker 4: this was not only to disincentivize paying ransoms, but to
Speaker 4: incentivize putting in place the cybersecurity practices that enabled entities
Speaker 4: to recover without paying a ransom. What do I mean
Speaker 4: if you have a good backup, if you've encrypted your
Speaker 4: data so even if the attackers get it, they can't
Speaker 4: use it to blackmail you, that they have a bunch
Speaker 4: of sensitive medical information or your client's wire.
Speaker 5: Instructions that's sensitive.
Speaker 4: So a few practices like that help a company recover
Speaker 4: without having to pay a ransom.
Speaker 5: And what we want to.
Speaker 4: Do was essentially say, Okay, in six months, the easy
Speaker 4: out of a ransom payment is going away.
Speaker 5: Come on, folks, do what you need to do so
Speaker 5: you can recover without.
Speaker 2: It, So.
Speaker 1: That puts the burden on the company to have as
Speaker 1: most the most up to date security possible.
Speaker 2: In theory, I get it. On the other hand, you know.
Speaker 1: Is it should should that fall on the company or
Speaker 1: should that fall on the governments?
Speaker 4: That is one of the core policy debates in cybersecurity.
Speaker 4: Now to that point, there was responsibility for each and
Speaker 4: I think when you think about the physical world, to
Speaker 4: your point, when you walk into a large corporate building,
Speaker 4: there are guards there that are paid for, you know,
Speaker 4: by the owners of the building, but you then have
Speaker 4: a police department if they're right, so you have both
Speaker 4: with rules, and I think that's very similar in cyber space.
Speaker 4: So in the case of governments, you know, we were
Speaker 4: conducting at that point offensive cyber operations to disrupt the
Speaker 4: infrastructure of these criminals around the world. We were working
Speaker 4: with governments around there were a number of coordinated attempts
Speaker 4: to arrest people. So we'd share quietly, you know, the
Speaker 4: names of people and if they got on a plane,
Speaker 4: they went on vacation, or they tried to you know,
Speaker 4: enjoy their illicit gains.
Speaker 5: In Duvine, you know, they'd be arrested.
Speaker 4: Among the members of the counter ransomware initiative were law
Speaker 4: enforcement cooperation and finally, government, like I said, created forums
Speaker 4: like that for information sharing, released advisories, and took those steps.
Speaker 4: But on the private sector side, there is the pretty
Speaker 4: in this case, basic steps like have backups, encrypt your data,
Speaker 4: make yourselves a harder target that when we think about
Speaker 4: the physical world, is very.
Speaker 5: Much the equivalent in the digital world.
Speaker 1: Let's go back to so one of the one of
Speaker 1: the reasons that the industry has fought regulation in these
Speaker 1: early days of AI adoption has been with this idea
Speaker 1: that we're in a race with China, and yet at
Speaker 1: the same time that there's no cooperation on how governments
Speaker 1: use for particularly how they weaponize AI. We could be
Speaker 1: in a We've already seen some asymmetric warfare successes thanks
Speaker 1: to AI for you know, essentially a smaller country being
Speaker 1: able to you know, operate with the with the bigger countries.
Speaker 1: What do you envision is realistic in trying to have
Speaker 1: some sort of AI red funk between China and the
Speaker 1: United States. I mean, is that what we're looking for here?
Speaker 4: So first, let's unpack the race with China, because I
Speaker 4: think you're right that term is used a lot, and
Speaker 4: we should ask what does that really mean. So from
Speaker 4: an AI perspective, there is AI that's helping in the
Speaker 4: fields of drug discovery.
Speaker 5: I mean they're currently drug trials. You can accelerate.
Speaker 2: The should be a border there should be borderless. One
Speaker 2: would think might competition.
Speaker 4: Because today, unfortunately, there are more diseases than there are
Speaker 4: vaccines for than there are drugs for. So that would
Speaker 4: be something where if it brings down the cost of
Speaker 4: drug production enables trialsity faster, that's a win for all
Speaker 4: of us.
Speaker 5: So in that case, I think the race is a
Speaker 5: race that's arm in arm.
Speaker 4: How do we make the most of this technology for
Speaker 4: the benefit of mankind. Similarly, there's many kinds of positive
Speaker 4: uses of AI. Frankly, there's positive use of AI for cybersecurity.
Speaker 4: It's hard to build secure code. Using AII that's trained
Speaker 4: on more secure code or on some malicious practices can
Speaker 4: then help ensure that code is generated securely against known
Speaker 4: threats and then can be tested as well before deployed.
Speaker 5: That's again a win win for all of us.
Speaker 4: There's the discussion of a race is essentially adoption of
Speaker 4: the most capable AI in national security fields, for example,
Speaker 4: in intelligence collection in for sample, in China, here's where
Speaker 4: an authoritarian country has an edge over a democracy. China
Speaker 4: has millions of cameras deployed around the country.
Speaker 5: Chinese technology is deployed around the world.
Speaker 1: They're going to probably surpass us on facial technology simply
Speaker 1: because of their system of government.
Speaker 4: Exactly, and I don't think we would trade our system
Speaker 4: of government. On the other hand, let's recognize that an
Speaker 4: American human spy traveling around the world is at greater
Speaker 4: risk as China trains facial recognition models. Then a reverse case,
Speaker 4: a Chinese spy would be because the US both did not,
Speaker 4: and we should talk about that. US technologies, let's say,
Speaker 4: in strategic areas like telecommunications, were overcome by the Chinese playbook.
Speaker 4: So if you go around the world, it's Wahwick, it's
Speaker 4: not ericson right, It's certainly not Motorola, which was or
Speaker 4: put out of business.
Speaker 1: We attempted the United States to try to hamstring Huawei,
Speaker 1: just failed.
Speaker 4: So let's come back to that, because I want to
Speaker 4: make sure I answer the prime you know. It's so
Speaker 4: when we talk about the race, that's where we mean
Speaker 4: in deployment of AI and national security areas like intelligence
Speaker 4: like by the military, where AI can make a difference crisis.
Speaker 1: I mean, you know it's funny. It's this is race,
Speaker 1: but you know it's always funny, and there's sometimes the
Speaker 1: arrogainst the west right where we assume that the other
Speaker 1: side is to see the potential advantage or disadvantage.
Speaker 2: If they if they allow.
Speaker 1: But with AI, the speed of innovation and frankly the
Speaker 1: speed with which you could counter the innovation is all
Speaker 1: happening so fast it feels like an unwinnable race. Meaning
Speaker 1: even if you think you there's no finish line. You
Speaker 1: may be here and in other country's here, but they
Speaker 1: may do this because we've seen these we've seen the
Speaker 1: AI models.
Speaker 2: They all leap each other.
Speaker 1: Who's to say Chinese is a Chinese deepline model isn't
Speaker 1: going to do the same thing.
Speaker 4: So that's exactly the way to think about your question
Speaker 4: about governance, because I think when we think about AI
Speaker 4: for all the positive use cases, let's arm in arm
Speaker 4: for the benefit of humanity, when we think about the
Speaker 4: case of AI deployed in military in weapons, that's where
Speaker 4: harkening back to your question around nuclear technologies, they had
Speaker 4: the same back and forth. Right, we were attempting to
Speaker 4: detect Soviet nuclear submarines, they were attempting to detect ours, and.
Speaker 5: Generally we would have a flywheel.
Speaker 4: Whereas our own detection technologies improved and became more sophisticated,
Speaker 4: we deployed better stealth on our own subs as well
Speaker 4: to protect against theirs. Now, clearly the pace of tech
Speaker 4: was far slower in nuclear technologies, so we could have a.
Speaker 1: Five year advantage a ten year advantage, it seems with
Speaker 1: this at best you're lucky to have a.
Speaker 4: Five month ANOW and the degree of depth, potentially of
Speaker 4: incorporation of AI into military decision making can be significant. Now,
Speaker 4: I want to be clear and say military culture around
Speaker 4: the world is generally more conservative. These are people who
Speaker 4: are trained in the laws of war. These are people
Speaker 4: who know what it means to lose a life, and
Speaker 4: they also know that any technology use on offense it's
Speaker 4: likely to come back to that you have to defend
Speaker 4: against as well. So it's not as if I think
Speaker 4: military communities anywhere around the world rush to deploy a
Speaker 4: new technology that's not fully understood. With that said, that
Speaker 4: is where as China and the US talk discussions about
Speaker 4: how will we incorporate AI into our military decision making
Speaker 4: and most importantly, if there is a misunderstanding, how do
Speaker 4: we communicate. One of the troubling things about US China
Speaker 4: relations is that we haven't had a military.
Speaker 2: To red phone. We don't have the exactly you know,
Speaker 2: the metaphorical right exactly.
Speaker 4: And in the past, with our last peer adversary, the
Speaker 4: Soviet Union, we had both a military red phone and
Speaker 4: a political line.
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Speaker 4: And to be clear, the US has attempted to put
Speaker 4: that in place. The onus is really the Chinese have
Speaker 4: not wanted to put it in place, and the concern
Speaker 4: really is that as a result, as each of us
Speaker 4: start to deploy AI and sensitive areas, there is the
Speaker 4: opportunity for miscommunication. There isn't necessarily the channel to resolve it.
Speaker 4: And that's where discussions among leaders are important, as well
Speaker 4: as putting in place the detection technology so that we
Speaker 4: and and really the intelligence capability is to try to
Speaker 4: understand how competitors are using AI in their own ecosystem
Speaker 4: so we can be aware as well and know and
Speaker 4: try our best not to misinterpret. But an ideal world,
Speaker 4: that's where you want the governance simply.
Speaker 1: We go to a larger issue. You're now in the
Speaker 1: private sector.
Speaker 5: There has been hard always stays in the national security.
Speaker 2: I hear you, I hear you. There is this, There is.
Speaker 1: There is The industry is petrified of any regulation at
Speaker 1: the moment. And I am cranky cinical reporter who says
Speaker 1: tech industry, you you you gave us social media and
Speaker 1: totally destroy the information ecosystem and we're suppos to trust
Speaker 1: you with AI. The industry, I feel like is not
Speaker 1: fully self aware as to how they have brought up.
Speaker 1: The skepticism that is out there about the technology exists
Speaker 1: because of essentially their bad behavior and the lack of
Speaker 1: lack of regulatory for any sort of significant regulation coming
Speaker 1: from government.
Speaker 2: And now we're jumping to it I entirely new technology.
Speaker 2: This feels this feels like.
Speaker 1: This just feels like government has no chance to sort
Speaker 1: of get its monitories.
Speaker 4: It is always a hard area to think about how
Speaker 4: to regulate a technology that's evolving so quickly. And I
Speaker 4: think there are some lessons we can learn from cyberspace
Speaker 4: and some that are very new. What do I mean,
Speaker 4: As you said, the US government failed to put in
Speaker 4: place regulations for cyberspace for a decade. The last attempt
Speaker 4: was a Lieberman Collins bill in twenty fifteen that's failed.
Speaker 4: After the Colonial pipeline incident, the President turned to me
Speaker 4: and said.
Speaker 5: And how is this happening?
Speaker 4: How can it be that we have one major pipeline,
Speaker 4: one major oil and gas pipeline supplying the entire East coast,
Speaker 4: only one, and a group of ransomware attackers sitting in
Speaker 4: Saint Petersburg can can conduct an attack that leads the
Speaker 4: company to take it offline, and as you know, that
Speaker 4: led to you know, americans at gasoline stations.
Speaker 5: One of the things we learned is never.
Speaker 4: Tell Americans please don't line up and get your gas
Speaker 4: all will be well, because then they immediately will go
Speaker 4: refill their pumps.
Speaker 5: And at the time we realized we.
Speaker 4: Were reflected on the President's question and said, you know,
Speaker 4: let's do a scrub and see are there regulations that
Speaker 4: already exist that apply. And what we found was that
Speaker 4: in pretty much every sector of critical infrastructure there were
Speaker 4: old safety regulations that could be reinterpreted for the digital age.
Speaker 5: What do I mean?
Speaker 4: Railroad signaling systems already had regulations to ensure that when
Speaker 4: they were mechanical, the mechanical box was properly secured. Who
Speaker 4: could have access to that, who could have the key?
Speaker 4: So we worked with TSA, who's the regulator for all railroads,
Speaker 4: took those regulations and updated them for the digital age
Speaker 4: to say, you know, you need multi factor authentication, not
Speaker 4: a password because everybody's passwords have been compromised. You need
Speaker 4: to ensure you have a monitoring system for your signaling system.
Speaker 5: And initially there was some pushback from the.
Speaker 4: Sector exactly as you said, and we realized that we
Speaker 4: had failed to properly engage them. So we invited the
Speaker 4: fifty seven railroad CEOs in the country to come in
Speaker 4: and we gave them briefings on the security threat, and
Speaker 4: we said, here are the security things that we think
Speaker 4: are needed. Let's have a discussion of that right balance
Speaker 4: of costs and impact, because if the threat is here
Speaker 4: and railroad signaling systems here, we can't jump here in
Speaker 4: one leap, but we can jump here, show the impact,
Speaker 4: show how we're reducing the cost of an attack, which
Speaker 4: is significant, and then do that in degrees. We did
Speaker 4: it for pipelines, we did it for ports, we did
Speaker 4: it for airports. We did it for water systems using
Speaker 4: existing regulators and existing regulations. And I think there's a
Speaker 4: lot we can learn from that because there's no one
Speaker 4: size fits all. I've visited water systems in San Jose.
Speaker 4: Securing those water systems so Americans have safe water is
Speaker 4: very different from securing an oil and gas pipeline. And
Speaker 4: the regulators know their sectors well and know what's reasonable
Speaker 4: in terms of the cost burden for impact in terms
Speaker 4: of driving down the threat. So I just to close
Speaker 4: on that I think there's some lessons to be learned
Speaker 4: in the case of AI, right Like, for example, if
Speaker 4: you think about people are worried that AI is used
Speaker 4: to determine if you or I should get alone, and
Speaker 4: they want to make sure that the data that's trained
Speaker 4: on is not biased and that the recommendation is explainable.
Speaker 4: So a human looking at it can say, why trust chock,
Speaker 4: why not trust in? There are existing regulations that do that.
Speaker 5: They have to be.
Speaker 4: Updated for the use of AI, many of them or
Speaker 4: they exist so many are adequate. That needs to be
Speaker 4: done for every sector. And then going back to the
Speaker 4: first question you asked me that talks about the risk
Speaker 4: AI brings separately, there are approaches we need to do
Speaker 4: to make sure the AI models that are deployed are
Speaker 4: safe themselves.
Speaker 1: So this gets back to you, we don't you know,
Speaker 1: we still don't regulate algorithms.
Speaker 2: Algorithms.
Speaker 1: Many companies believe their algorithms are this proprietary thing, and
Speaker 1: we don't know whether it's being used for predatory practices.
Speaker 1: For because it's an algorithm that could make that decision
Speaker 1: about a mortgage or about a loan. Sometimes after the fact,
Speaker 1: maybe a civil lawsuit is brought and then you can
Speaker 1: somehow claim that they violated the law. I get that
Speaker 1: the law is there eventually, but is there a proactive
Speaker 1: way to essentially, hey, we've got a new algorithm that's
Speaker 1: it's going.
Speaker 2: To help us.
Speaker 1: Get through these mortgage applications faster. Let's make sure it's
Speaker 1: approved by the government the same way we would approve
Speaker 1: a new drug before it was circulated on the market,
Speaker 1: sold as a as a as a pharmaceutical, you know,
Speaker 1: answer to some disease.
Speaker 5: It's a good question.
Speaker 4: And the big difference I think between an AI model
Speaker 4: or an algorithm, to your point, and a drug is
Speaker 4: one changes so continuously and one takes time to develop
Speaker 4: and test.
Speaker 5: It has IP protection attacked, I.
Speaker 1: Admit, but I you know, actually the speed of prescription
Speaker 1: drugs is going faster. I mean, I could make an
Speaker 1: argument that FDA and drugs AI models.
Speaker 2: You know, why shouldn't there be an FDA or AM
Speaker 2: And I think it's.
Speaker 4: I think to your point, there's a tension between the
Speaker 4: innovation and regulation, right because regulation often is slow to
Speaker 4: adapt to adapt to changing technology. And in fact, I think, frankly,
Speaker 4: if you look at the difference between the European Union
Speaker 4: and the United the States. European Union is a country
Speaker 4: that puts regulations first, an innovation second.
Speaker 1: I get it, and it's what's the line. Yeah, well,
Speaker 1: right now, there's no line, And I think we're not
Speaker 1: even attempting any of them because the industry has got
Speaker 1: aggressive politically and has got essentially made a political bargain
Speaker 1: with this administry.
Speaker 4: And I think there's a balance between the how we
Speaker 4: approach AI models themselves and then how do we approach
Speaker 4: when they're deployed in sectors where regulations exist, Like, for example,
Speaker 4: we'll just go use our loan example again.
Speaker 5: So if somebody is deploying an AI model.
Speaker 4: To determine who gets a loan, there are regulations around
Speaker 4: explaining how a decision is made coming out of an
Speaker 4: algorithm or model, and around what data that's trained on,
Speaker 4: So that you need transparency on the data it's trained
Speaker 4: on and explainability of how that decision is made. That
Speaker 4: exists in that sector. It doesn't exist in every sector,
Speaker 4: and that's really what we need. We need the transparency
Speaker 4: on the data models trained on to see is it
Speaker 4: trained on data that fundamentally was.
Speaker 2: Do we need new law in the books for this.
Speaker 4: I usually default to say, given that laws trail tech
Speaker 4: by such a long time, are there ways that we
Speaker 4: can achieve what we want, which is protecting the population
Speaker 4: from a new technology's most significant risks in a way
Speaker 4: that's also adaptable and changes quickly. And I think there
Speaker 4: to your point, looking at the AI models and saying,
Speaker 4: what is the delta harm they bring to different areas,
Speaker 4: and then what's the best way to control against that harm?
Speaker 5: What's what's needed?
Speaker 1: Look, a month ago, Anthropic came out and said, our
Speaker 1: new AI model is so powerful, we're.
Speaker 2: Not sure that.
Speaker 1: Current cybersecurity infrastructure is going to be able to defend
Speaker 1: against people deciding to use our model to potentially attack.
Speaker 2: And there's a part of me that says, so why
Speaker 2: release the model? Like should this?
Speaker 1: But again, there's no law in the books that says
Speaker 1: they can't release the model.
Speaker 4: So yes, And you know that's a fierce debate in
Speaker 4: the cybersecurity community.
Speaker 5: How much of that was Anthropic marketing and all that
Speaker 5: I saw.
Speaker 2: Debate, I think it is enough.
Speaker 1: It's no different than claiming that your prescription drug, if
Speaker 1: it works properly but for longer.
Speaker 2: Than four hours, call a doctor. Was that a side effect.
Speaker 5: Was that a marketing gold Well, you know, so it's
Speaker 5: so interesting.
Speaker 4: I've had the privilege of leading teams conducting offensive operations
Speaker 4: and I also, as you know, led defensive head of
Speaker 4: cybersecurity and essay and then let this work at the
Speaker 4: White House, and I will say that I really believe
Speaker 4: AI models will transform cybersecurity on defense. But it's a
Speaker 4: painful path there, and that's what we need to control
Speaker 4: for what do I mean it is far easier.
Speaker 5: To attack than to defend.
Speaker 4: An attacker has to find one way in a defender
Speaker 4: has to be checking every door, window and ceiling.
Speaker 1: Job in America's homeland security, yeah, because.
Speaker 5: N is not going to happen, and being a SISO
Speaker 5: is a really tough job.
Speaker 4: I mean, there's often discussions in that community around just
Speaker 4: how difficult, how much people know exactly this, et cetera.
Speaker 2: It's terrible.
Speaker 4: Now, what AI allows is attackers to jiggle every doorknob
Speaker 4: continuously and at scale. So it really does enable attackers.
Speaker 4: But what allows defenders to do if they're doing the
Speaker 4: same thing, if they're jiggling every doorknob in their infrastructure
Speaker 4: in advance and at scale, and AI then helps generate
Speaker 4: patches or controls to compensate that can be significant. Today,
Speaker 4: the model's capabilities on generating patches, et cetera need to
Speaker 4: be better.
Speaker 5: They're there, but they need to be better.
Speaker 4: So the approach that said, let's get the core companies
Speaker 4: producing digital infrastructure that everyone uses to use the models
Speaker 4: to find and fix vulnerabilities and deploy them via existing
Speaker 4: patches is a smart one because it starts locking down
Speaker 4: common infrastructure. What's missing today and what we need is
Speaker 4: that in all of our critical sectors. Hospitals are a
Speaker 4: good one, there's also a stack that rides on top
Speaker 4: of the core infrastructure and that is often what's used
Speaker 4: to compromise a hospital to disrupt its operations. That's missing,
Speaker 4: and I think that's what we need to be able
Speaker 4: to say, within each sector, what does the stack look like?
Speaker 4: And in some cases those are stacks that aren't updated
Speaker 4: and secure them quickly.
Speaker 1: Is government behind or industry being obstinate?
Speaker 4: Government needs to be doing convening and I see the
Speaker 4: power of government convening is significant when you bring everybody
Speaker 4: around the table and you say, look, we can regulate
Speaker 4: this or these are the things we.
Speaker 5: Need to see you get done.
Speaker 4: Particularly when companies are out there saying this is such
Speaker 4: a powerful weapon, this is so concerning, Well, then act
Speaker 4: it out.
Speaker 5: Then work with.
Speaker 4: Us in team to ensure that we can equip defenders
Speaker 4: to be ready, because, let's be honest, Chinese open source
Speaker 4: models followed by six months and that's a threat that's significant.
Speaker 4: So we have a window of time for models, for
Speaker 4: frontier model companies to be working, and frankly, for government
Speaker 4: to be helping convene the open source and the open
Speaker 4: source community to be getting access to the models because,
Speaker 4: as you know, open source, unlike a Cisco or Microsoft,
Speaker 4: is developed often by anyone, but broadly deployed in our infrastructure.
Speaker 4: So I think that's starting. You see, the White House
Speaker 4: has been bringing companies together. You see Project Last Wing,
Speaker 4: you see opening eyes approach to say we'll approve people
Speaker 4: because we.
Speaker 5: Wanted to know your customer.
Speaker 4: We want to know are you a SISO, are you
Speaker 4: a head of cyber or AI for a country who's
Speaker 4: setting strategy, and if so, will come up with ways
Speaker 4: to have you use the model in a way that's
Speaker 4: safe and doesn't broadly distribute it. But the race is on,
Speaker 4: and to be clear, even though you know I do
Speaker 4: believe it's going to be a game changer on cybersecurity.
Speaker 4: We've got to ensure that models are in the hands
Speaker 4: of defenders quickly, and we've got to keep the pressure
Speaker 4: on companies that build digital infrastructure to be getting those
Speaker 4: patches out and figuring out for infrastructure that is difficult
Speaker 4: to patch.
Speaker 5: You know, it's not so easy to just push a
Speaker 5: patch on a manufacturing plan.
Speaker 4: You know, it sometimes has side effects that we can
Speaker 4: use AI, for example, to simulate those patches to ensure
Speaker 4: that they're working right, to give users more confidence to
Speaker 4: deploy them. So there's a lot that needs to get done.
Speaker 2: You're outriting a lot that we need to do.
Speaker 1: Color me skeptical that we have the current governmental infrastructure
Speaker 1: to respond in the ways that maybe the public needs
Speaker 1: it to respond.
Speaker 4: You know, the first step is saying here's where we
Speaker 4: need to go. The second step is rally a community.
Speaker 4: The nice part is there are lots of people across
Speaker 4: the tech community are ready to roll up their sleeves
Speaker 4: and can be self organizing on this too.
Speaker 1: But you're right, governments, and yet we're all watching as
Speaker 1: voyeurs this elon musk Open AI trial and it doesn't
Speaker 1: give a lot of confidence in some of these companies.
Speaker 4: You're right that the role of government inconvening, and I
Speaker 4: do believe the White House has been doing convenings, bringing
Speaker 4: companies together and trying to get action out of them.
Speaker 4: And in fact, companies are moving in. I've talked to
Speaker 4: a number of companies participating in glass Wing.
Speaker 5: They're doing the work, they're building.
Speaker 4: The patches, they're planning to get them out to customers,
Speaker 4: pressing them.
Speaker 1: On psychically, initial models are not the trustworthy enterprise. It's
Speaker 1: one thing for Microsoft, JP, Morgan, some of these companies
Speaker 1: to be responsible with how they're using these models, but
Speaker 1: it feels like the companies that are making the models don't.
Speaker 1: Necessarily they seem to be more a bit self absorbed.
Speaker 4: There's a tension between the commercial value and the national
Speaker 4: security risk, and typically, you know, government makes that determination.
Speaker 4: What's so different here is the tech is coming out
Speaker 4: of the private sector, and it is important that government
Speaker 4: come out and clearly say here's where national security risk
Speaker 4: is significant. Here's an approach that insures we still lead
Speaker 4: on innovation, but we drive down the risk to an
Speaker 4: acceptable level.
Speaker 5: Along the way.
Speaker 1: And you've got a big job it's like with trying
Speaker 1: to bridge this divide.
Speaker 4: It's a large community, and you know, I always believe
Speaker 4: with tech, there's nothing wrong with tech that can be fixed.
Speaker 1: With the threat, I will say this, I think there's
Speaker 1: government is skeptical, a tech is skeptical of government, and
Speaker 1: yet they have to work together in world fits.
Speaker 4: I very much agree with that, and I think quite
Speaker 4: frankly those relationships. When I think back to the Snowden period,
Speaker 4: the divide between Washington and Silicon Valley was incredibly sharp,
Speaker 4: and that came from a sense that government surveillance wasn't
Speaker 4: farnollowing laws and policies of the country as a democracy.
Speaker 5: And frankly, you know, at the.
Speaker 4: Time, I after that, I was asked to be NSA's
Speaker 4: first chief risk officer, and I remember flying back and
Speaker 4: forth across the country and seeing how sharp the divide was.
Speaker 4: Folks in the national security community were saying, why don't
Speaker 4: people trust us? We're the good guys here, and folks
Speaker 4: in the private sector were like, Okay, how does this
Speaker 4: intelligence community work and doesn't really respect my values? And
Speaker 4: it took a series of conversations, you know, over the
Speaker 4: last decade to heal that.
Speaker 5: I think we're at the same place.
Speaker 2: Now where you Well, it's funny you bring up values.
Speaker 1: What are the values of these companies? I don't know
Speaker 1: sometimes and are they our values? I think there's some
Speaker 1: concern about that.
Speaker 2: But this is just the beginning, it is indeed, and
Speaker 2: thanks for your time.
Speaker 5: Thank you.
Speaker 2: Thanks