WEBVTT - Cloud Security, Female MBA Enrollment

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<v Speaker 1>Bloomberg Audio Studios, Podcasts, Radio News.

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<v Speaker 2>You're listening to Bloomberg BusinessWeek with Carol Messer and Tim

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<v Speaker 2>Stenebek on Bloomberg Radio. So just in the last few days, yes,

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<v Speaker 2>we've learned that Chinese hackers have targeted the phones of

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<v Speaker 2>Donald Trump and JD Vance and affiliates of Kamala Harris's campaign.

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<v Speaker 2>This is all according to NBC News. Then there's this

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<v Speaker 2>great profile out today by our Bloomberg News colleague, Katrina Manson.

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<v Speaker 2>It's about how Jen Easterly, who's director of the Cybersecurity

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<v Speaker 2>and Infrastructure Security Agency, is working to protect election infrastructure

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<v Speaker 2>from hacks and misinformation. And we'll need to convince the public.

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<v Speaker 2>Perhaps that's the hardest job of the elections legitimacy after

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<v Speaker 2>the vote.

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<v Speaker 1>That's right, needlessen to say, Tim, data security is top

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<v Speaker 1>of mind ahead of the election, and also always top

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<v Speaker 1>of mind for business leaders looking to safeguard there and

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<v Speaker 1>ours data. That's where Lane Best comes in. He's the

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<v Speaker 1>CEO of the deep learning AI CyberSecure platform deep Instinct.

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<v Speaker 1>He's also the former CEO of the publicly traded security

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<v Speaker 1>software company z Scaler, and before that, he was the

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<v Speaker 1>CEO of Palo Alto Network, So yeah, he knows about

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<v Speaker 1>this stuff. Tim.

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<v Speaker 2>It's good to have you with us, Lane, Thanks so

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<v Speaker 2>much for joining us. Hey, before we get into what

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<v Speaker 2>exactly Deep Instinct does, because it's it's distinct from some

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<v Speaker 2>of the other companies that are out there, what is

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<v Speaker 2>the biggest threat that you think we as a country

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<v Speaker 2>face when it faces when it comes to cybersecurity, it.

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<v Speaker 3>Is really the unknown threats. Today, a lot of the

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<v Speaker 3>money and resources and expenditures are towards known threats, and

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<v Speaker 3>the unknown threats are becoming much greater. Seventy two percent

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<v Speaker 3>of the threats that hit companies and governments today are

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<v Speaker 3>of an unnever seen nature and therefore the systems that

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<v Speaker 3>had been trained and the machine learning technologies that are

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<v Speaker 3>typically in place can't stop them. And in the early

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<v Speaker 3>phases of new technologies and all point specifically to AI,

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<v Speaker 3>the advantage goes to the attackers because they can use

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<v Speaker 3>simple tools and sophisticated ones to compromise companies' data and

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<v Speaker 3>ultimately cause havoc in some cases nation state attacks. It

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<v Speaker 3>takes a time for the practitioners to catch up because

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<v Speaker 3>they've already invested millions in the cyber technologies, and they've

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<v Speaker 3>got to explain why they're going to spend more money

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<v Speaker 3>to address some of the more challenging, unknown, new, never

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<v Speaker 3>seen threats. They eventually catch up, but in this early stage,

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<v Speaker 3>particularly with the AI threats, it can be quite dangerous.

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<v Speaker 1>So what are the unknown threats? Potentially? Sure?

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<v Speaker 3>So, known threats are usually in families of threats that

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<v Speaker 3>have been seen before, and the cloud models which many

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<v Speaker 3>of the companies in cyber have been able to train,

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<v Speaker 3>can essentially see them. But typically and using AI tools,

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<v Speaker 3>they can change code very slightly, and they can inject code,

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<v Speaker 3>and typically that evades even the best companies defense and

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<v Speaker 3>depth capabilities, and so simple changes aided in some cases

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<v Speaker 3>by more sophisticated AI, can take something that was generally

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<v Speaker 3>in a known category family and creates something entirely unseen before,

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<v Speaker 3>and therefore it can get through and cause significant data breaches.

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<v Speaker 2>Are we more concerned about data breaches? Are we more

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<v Speaker 2>concerned about an entire electrical grid becoming unusable? About traffic

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<v Speaker 2>signals being disrupted, in tunnels getting shut down, bank accounts

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<v Speaker 2>being wiped out, or those related?

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<v Speaker 3>They all are related. And the science, particularly the science

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<v Speaker 3>that we address at deep instinct addresses both cases. Typically,

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<v Speaker 3>though what we're seeing right now is the biggest cost

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<v Speaker 3>to companies is in data breaches. The average data breach

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<v Speaker 3>is a four point five million, and we've seen some

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<v Speaker 3>notable ones that have been in the hundreds of millions.

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<v Speaker 3>But if you talk about infrastructure disruption, these what I

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<v Speaker 3>consider to be infrastructure and nation state attacks. These can

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<v Speaker 3>just as easily be launched using the same tools. And

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<v Speaker 3>if you take a look at the industry, it has

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<v Speaker 3>been pretty much in a layers of defense, detect, remediate

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<v Speaker 3>fix afterwards. The posture that governments and also companies has

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<v Speaker 3>to take now is to get the most sophisticated AI

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<v Speaker 3>to battle the onslaught of sophisticated AI to prevent these threats,

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<v Speaker 3>and it does take a specific tibe of technology, one

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<v Speaker 3>in the form of deep learning that.

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<v Speaker 1>We have Selene walk us through the difference between AI,

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<v Speaker 1>machine learning, and deep learning and how cyber criminals are

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<v Speaker 1>actually using deep learning right now. Yeah.

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<v Speaker 3>So deep learning is like a brain, a neural network,

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<v Speaker 3>and without getting too much into the science of how

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<v Speaker 3>it works, you and I can take massive amounts of

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<v Speaker 3>data in our brain and we can make determinations and

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<v Speaker 3>with inferences and in our case, instinctual decisions. Machine learning models,

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<v Speaker 3>which can be very sophisticated, are based on parameters that

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<v Speaker 3>have been defined by people and rules that are trained

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<v Speaker 3>by people, and therefore they don't train upon themselves, only

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<v Speaker 3>what they've been defined. With deep learning models, Neural network

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<v Speaker 3>models are self training and take massive amounts of data,

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<v Speaker 3>in this case anything everything that might be in the

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<v Speaker 3>cyber space, and essentially extrapolate the potential of something being

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<v Speaker 3>a threat with ninety nine percent efficacy. So that's probably

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<v Speaker 3>the biggest differentiation.

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<v Speaker 2>When it comes to individuals out there, Like I know

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<v Speaker 2>that a lot of what you focus on is key

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<v Speaker 2>being companies safe, but at the end of the day,

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<v Speaker 2>we're only as strong as the weakest link. And I

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<v Speaker 2>think that's why correct. And I know we do this here,

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<v Speaker 2>but we do so much training about understanding phishing understanding.

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<v Speaker 1>I just did another training. I think the deadline was

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<v Speaker 1>on Tuesday. I was on vacational last week and came

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<v Speaker 1>back and I had you had to do it. I

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<v Speaker 1>had to do all the courses or they're going to

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<v Speaker 1>show off.

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<v Speaker 2>Correct. But there's a reason why, because with this social engineering,

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<v Speaker 2>all they need to do to gain access is is

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<v Speaker 2>get access through one of us.

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<v Speaker 3>So it's correct.

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<v Speaker 2>What's the way that we need to recognize threats in

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<v Speaker 2>a world where I can get a phone call. It

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<v Speaker 2>looks like it's from Amazon, but it's really somebody who

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<v Speaker 2>wants to steal money from me. It looks like it's

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<v Speaker 2>from my brink, but it's really someone who wants to

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<v Speaker 2>steal money from me.

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<v Speaker 3>And you hit it on the head most organizations, and

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<v Speaker 3>it's it's a big business in and of itself. The

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<v Speaker 3>training and readiness and IT organizations are spending a lot

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<v Speaker 3>on this, and we ourselves as individuals have to do

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<v Speaker 3>it because the weakest link is the insider threat, and

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<v Speaker 3>we've seen many of these stories. Ultimately, though, what happens

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<v Speaker 3>is those threats get in and in some way they're

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<v Speaker 3>going to go in and sit within your data sets

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<v Speaker 3>or within the applications in your environment. So even if

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<v Speaker 3>they evade through social engineering, having an efficacy level and

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<v Speaker 3>ability to determine that something that has come in is

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<v Speaker 3>not quite kosher is going to be a key backup

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<v Speaker 3>to the training that needs to take place. And most

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<v Speaker 3>of the detection and remediation is clean up after the

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<v Speaker 3>accidents already happen, So I would absolutely you're dead on,

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<v Speaker 3>and I'm happy to hear that Bloomberg is taking the efforts.

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<v Speaker 3>We do it with our own employees regularly, including myself

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<v Speaker 3>in terms of the social engineering, and this again is

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<v Speaker 3>where AI tools and some of the simplest tools with

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<v Speaker 3>regard to social engineering can help evaide.

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<v Speaker 1>So I don't know if this happen to you, Tim,

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<v Speaker 1>but I've been constantly getting messages that are definitely scams,

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<v Speaker 1>but they're supposed to be from recruiters. But this has

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<v Speaker 1>been happening to yes or text messages, and this has

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<v Speaker 1>been happening to some of my colleagues too. But it's

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<v Speaker 1>it's scary because you're wondering, you know, how you're getting this.

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<v Speaker 1>Sometimes it happens to me on WhatsApp as well. It

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<v Speaker 1>used to be more bitcoin messages, but now it's more

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<v Speaker 1>of these kind of fake recruiting type messages.

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<v Speaker 3>Right.

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<v Speaker 1>But I like my current gig, so I'm going to

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<v Speaker 1>try to keep that. But Lane, what safeguards do you

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<v Speaker 1>think need to be put in place when this is

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<v Speaker 1>such a complicated type of issue, and especially with all

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<v Speaker 1>of the technology involved.

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<v Speaker 3>Right at the end of the day, common sense is

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<v Speaker 3>probably the thing that I say to most people, the

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<v Speaker 3>when you get something from somebody you don't know, and

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<v Speaker 3>I get it all the time. In fact, while we

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<v Speaker 3>were talking here, I think I got a spam call

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<v Speaker 3>coming into my phone. Seriously, it's just every day. And

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<v Speaker 3>just rule of thumb is if it's not from somebody

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<v Speaker 3>you know, if it's not from the organization you're in,

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<v Speaker 3>you just don't click on it. Is enticing, as the

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<v Speaker 3>headline might be, there's a lot of clickbait we see today,

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<v Speaker 3>and at the end of the day, this is just

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<v Speaker 3>going to become an ongoing thing. And again, our goal

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<v Speaker 3>at Deep Instinct is to make it so that if

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<v Speaker 3>there is a breach wherever they try to get at

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<v Speaker 3>critical company data or critical company IP or resources, we

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<v Speaker 3>will detect it and we will stop it and flack it.

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<v Speaker 3>So I'd like to think that even if the first

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<v Speaker 3>line of defense, which is practical behavior by our employees

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<v Speaker 3>and by individuals, deep instinct deep learning technology, I say

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<v Speaker 3>it this way, it's the best AI to fight AI.

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<v Speaker 2>Okay, so let's talk a little bit about that and

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<v Speaker 2>sort of the technology that's being used here. Because one

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<v Speaker 2>thing that I always wonder is who has the upper

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<v Speaker 2>hand right now. Is it the actors who have incredible

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<v Speaker 2>upside but also a lot of downside or is it

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<v Speaker 2>the people who are trying to protect us who has

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<v Speaker 2>more technological advantage right now?

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<v Speaker 3>Yeah, I hate to say it, particular in the AI space,

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<v Speaker 3>IT advantage does goes to go to the bad actors.

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<v Speaker 3>And one of the challenges is and one of the

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<v Speaker 3>hardest jobs in cyber and n it is the CIO

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<v Speaker 3>and the chief information security officer. These individuals have gone

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<v Speaker 3>to the well to put in a tremendous amount of

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<v Speaker 3>cyber protection and detection technology for number of years, and

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<v Speaker 3>even still ransomwares and attackers get through. And it's getting

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<v Speaker 3>even more challenging the pace at which the malicious acts

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<v Speaker 3>are taking place and the ability for the teams of

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<v Speaker 3>people within enterprises and governments can address them. The gap

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<v Speaker 3>is widening, and so again the bad actors have the advantage.

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<v Speaker 3>And now the challenge that a lot of the IT

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<v Speaker 3>professionals have is they have to once again go to

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<v Speaker 3>management say this is a continuing and escalating this is

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<v Speaker 3>probably one of the most difficult jobs in technology, the

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<v Speaker 3>cyber protection, and it's only going to get greater and

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<v Speaker 3>so I encourage organizations to not think about, well, what

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<v Speaker 3>are we going to replace in our tech stack to

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<v Speaker 3>justify the cost of prevention. My view is that this

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<v Speaker 3>is a budget line that unfortunately continues to be need

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<v Speaker 3>to be evaluated. But more importantly, these practitioners need to

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<v Speaker 3>go beyond the technologies they've been using and look at

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<v Speaker 3>the more sophisticated technologies such as the things that we

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<v Speaker 3>we provide a deep Instinct because the same old tools

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<v Speaker 3>aren't going to work against the bad actors. Eventually, the

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<v Speaker 3>chech catches up early on. It's a challenge.

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<v Speaker 2>Lane, appreciate you joining us. That's Lane Best CEO over

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<v Speaker 2>at Deep Instinct. He joins us from New York.

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<v Speaker 1>As you know, Tim, this is a really big issue

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<v Speaker 1>here when you're thinking about how women are continuing, unfortunately

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<v Speaker 1>to lose these C suite seats in corporate America. In fact,

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<v Speaker 1>women's representation in senior level positions at US companies saw

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<v Speaker 1>signs of fatigue for the first time in two decades,

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<v Speaker 1>with gender parity and C suite positions potentially not reaching

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<v Speaker 1>parody until twenty seventy two, according to an S and

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<v Speaker 1>P report earlier this year.

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<v Speaker 2>That's literally a lifetime.

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<v Speaker 1>Yes, that's a lifetime. I will probably be dead that

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<v Speaker 1>hopefully hopefully not hopefully not that hopefully not though, But anyway,

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<v Speaker 1>I digress. But while there are lingering issues affecting women's

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<v Speaker 1>abilities to seek, prepare, and attain business leadership positions, there

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<v Speaker 1>actually are some signs recently of progress, and the total

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<v Speaker 1>number of women enrolled in top full time NBA proms

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<v Speaker 1>this fall by a record setting pace. Here So, joining

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<v Speaker 1>us now to discuss more is Alyssa sayingster ceo a

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<v Speaker 1>fort Foundation from San Antonio, Texas and for some insight

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<v Speaker 1>for our listeners. Forte Foundation is a nonprofit designed to

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<v Speaker 1>increase opportunities for women in leadership through access to business

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<v Speaker 1>education and professional development. Thank you so much, Alissa for

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<v Speaker 1>joining us. And I was looking through this latest report

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<v Speaker 1>that y'all put together. Here walk us through some of

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<v Speaker 1>the highlights and what drove women's NBA enrollment this fall.

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<v Speaker 4>Sure, so, I mean we're seeing some great results. We

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<v Speaker 4>are seeing women at right now forty two percent enrolled

0:13:39.480 --> 0:13:43.360
<v Speaker 4>in top business schools and that has been increasing, I

0:13:43.360 --> 0:13:45.640
<v Speaker 4>believe over the last five years we've seen a four

0:13:45.720 --> 0:13:50.600
<v Speaker 4>percent increase. We've seen number of schools reach gender parity.

0:13:50.720 --> 0:13:53.280
<v Speaker 4>Eight of our schools have hit that fifty to fifty

0:13:53.360 --> 0:13:56.720
<v Speaker 4>or more mark. But we've also seen some great progress

0:13:56.760 --> 0:13:59.240
<v Speaker 4>at forty five percent and at forty percent.

0:14:00.080 --> 0:14:02.480
<v Speaker 1>And looking through this too, because there's a lot of

0:14:02.480 --> 0:14:05.240
<v Speaker 1>interesting points here, and of course Tim and I were

0:14:05.280 --> 0:14:07.800
<v Speaker 1>just mentioning some of the pitfalls. I mean, what do

0:14:07.840 --> 0:14:10.640
<v Speaker 1>you think before was really holding back some of that,

0:14:10.679 --> 0:14:13.280
<v Speaker 1>because I know COVID was a big issue where we

0:14:13.320 --> 0:14:15.240
<v Speaker 1>saw a lot of women having to pull back from

0:14:15.240 --> 0:14:15.920
<v Speaker 1>the workforce.

0:14:17.400 --> 0:14:19.680
<v Speaker 4>Sure, I think you know, there's a lot of choices

0:14:19.720 --> 0:14:22.320
<v Speaker 4>that people had, men and women when they were going

0:14:22.360 --> 0:14:26.080
<v Speaker 4>back to school during the COVID times. So it's a

0:14:26.120 --> 0:14:28.960
<v Speaker 4>big move and it's a big investment, and you have

0:14:29.040 --> 0:14:32.000
<v Speaker 4>to really think about all the factors. What we saw

0:14:32.000 --> 0:14:35.520
<v Speaker 4>about twenty years ago is that women were not pursuing

0:14:36.520 --> 0:14:39.080
<v Speaker 4>business school because they didn't have a lot of role models,

0:14:39.120 --> 0:14:41.680
<v Speaker 4>They didn't hear from people who influenced them that that

0:14:41.840 --> 0:14:45.720
<v Speaker 4>was a path they should pursue, and so there were

0:14:45.760 --> 0:14:48.680
<v Speaker 4>a number of other factors, but those things were holding

0:14:48.720 --> 0:14:50.320
<v Speaker 4>them back. And that's what we've been working on the

0:14:50.400 --> 0:14:53.080
<v Speaker 4>last twenty years is how to chip away at some

0:14:53.160 --> 0:14:57.280
<v Speaker 4>of those misperceptions they had about business school and business careers.

0:14:57.600 --> 0:15:00.720
<v Speaker 2>You know, I hesitate to tie everything to the election,

0:15:00.800 --> 0:15:02.840
<v Speaker 2>but it's top of mind for so many people here.

0:15:03.320 --> 0:15:07.880
<v Speaker 2>Tuesday is election day, and the US could see its

0:15:07.880 --> 0:15:11.640
<v Speaker 2>first female president in its entire history. It also could not.

0:15:12.440 --> 0:15:14.520
<v Speaker 2>But I'm wondering if you think a change at the top,

0:15:15.280 --> 0:15:17.360
<v Speaker 2>and I literally mean at the top, like the most

0:15:17.400 --> 0:15:21.120
<v Speaker 2>important position in the entire country, if that moves the needle.

0:15:23.200 --> 0:15:25.640
<v Speaker 4>It I mean, I think it absolutely does, because it

0:15:25.720 --> 0:15:30.520
<v Speaker 4>is a change in potential policies, it's a change in

0:15:30.880 --> 0:15:34.320
<v Speaker 4>who you see every day on your television. It's inspirational.

0:15:34.360 --> 0:15:36.840
<v Speaker 4>I mean, that is the role model for everyone. And

0:15:36.920 --> 0:15:41.080
<v Speaker 4>so I think you know why it may not inspire

0:15:41.160 --> 0:15:43.520
<v Speaker 4>more women to go to get their business degree. I

0:15:43.520 --> 0:15:46.320
<v Speaker 4>think it does inspire them to lead, and I think

0:15:46.440 --> 0:15:49.960
<v Speaker 4>ultimately that's the position you want to see a woman in.

0:15:50.200 --> 0:15:52.160
<v Speaker 2>There's also something happening right now that we've been hearing

0:15:52.200 --> 0:15:54.560
<v Speaker 2>a lot about in the context of the election. Then

0:15:54.600 --> 0:15:57.600
<v Speaker 2>that's the idea that more women are going to college,

0:15:57.720 --> 0:16:01.280
<v Speaker 2>more women are graduating from college, and that even though

0:16:01.440 --> 0:16:06.720
<v Speaker 2>as a whole, women are still at a paid less

0:16:06.760 --> 0:16:10.560
<v Speaker 2>than men, there's this idea that when it comes to advancement,

0:16:11.800 --> 0:16:14.360
<v Speaker 2>and this is a lot of been talked about this

0:16:14.440 --> 0:16:17.280
<v Speaker 2>with regard to the male support for Donald Trump, there's

0:16:17.320 --> 0:16:21.920
<v Speaker 2>this idea that men have been left back, and I'm

0:16:21.960 --> 0:16:24.600
<v Speaker 2>sorry that I'm it's hard for me to say that

0:16:24.640 --> 0:16:28.040
<v Speaker 2>with a straight face, just because you look at all

0:16:28.040 --> 0:16:31.920
<v Speaker 2>the data and you see that women are still on

0:16:31.960 --> 0:16:36.360
<v Speaker 2>a salary basis. You know, there's like equal payday as

0:16:36.400 --> 0:16:39.080
<v Speaker 2>months after the first of the year. So but I'm

0:16:39.080 --> 0:16:43.000
<v Speaker 2>wondering how you see that playing into this. Melissa Well,

0:16:43.040 --> 0:16:43.520
<v Speaker 2>I think.

0:16:43.360 --> 0:16:47.560
<v Speaker 4>It's discouraging when you hear that as a top line message,

0:16:47.600 --> 0:16:50.000
<v Speaker 4>because I think you're right. Maybe for a blink of

0:16:50.040 --> 0:16:53.720
<v Speaker 4>the eye, we've seen change happen and women have progressed.

0:16:54.040 --> 0:16:56.680
<v Speaker 4>But I think if you look back decades and centuries,

0:16:57.080 --> 0:16:59.560
<v Speaker 4>we're just now getting to the tip of the iceberg.

0:16:59.640 --> 0:17:02.520
<v Speaker 4>And so so I'm not sure that there's a lot

0:17:02.560 --> 0:17:05.080
<v Speaker 4>to complain about on either side. I think we just

0:17:05.160 --> 0:17:07.200
<v Speaker 4>need to kind of let things settle, and we need

0:17:07.240 --> 0:17:10.480
<v Speaker 4>to make sure that we're focused on, especially in business leadership,

0:17:11.280 --> 0:17:15.320
<v Speaker 4>building inclusive cultures, building opportunities for all of our people

0:17:15.359 --> 0:17:19.120
<v Speaker 4>to advance, and to make sure that companies are focused

0:17:19.119 --> 0:17:23.280
<v Speaker 4>on that, and they're focused on, you know, how to

0:17:23.359 --> 0:17:27.120
<v Speaker 4>engage all of their employees. In this discussion, I think

0:17:27.160 --> 0:17:32.000
<v Speaker 4>it's an unnecessary really to say men are not advancing

0:17:32.040 --> 0:17:34.560
<v Speaker 4>at the same rate as women, especially in business leadership.

0:17:34.600 --> 0:17:36.560
<v Speaker 4>I mean, there are some other societal things that we

0:17:36.600 --> 0:17:38.280
<v Speaker 4>want to focus on, and I don't want men to

0:17:38.320 --> 0:17:41.760
<v Speaker 4>be left behind, But I also don't think that's really

0:17:42.080 --> 0:17:43.520
<v Speaker 4>reflective of what's going on.

0:17:43.840 --> 0:17:46.560
<v Speaker 1>Of course, there was a debate, especially over the last decade,

0:17:46.680 --> 0:17:49.040
<v Speaker 1>as more and more people were getting their MBAs and

0:17:49.400 --> 0:17:52.080
<v Speaker 1>how much useful that would be as people were getting

0:17:52.119 --> 0:17:55.040
<v Speaker 1>say CFAs and other things. But when you're looking more recently,

0:17:55.160 --> 0:17:57.800
<v Speaker 1>especially coming out of COVID, how have especially women been

0:17:57.840 --> 0:18:00.639
<v Speaker 1>putting their nbas to work in particular industries.

0:18:02.240 --> 0:18:04.199
<v Speaker 4>Well, I think what you see is a really healthy

0:18:04.240 --> 0:18:07.480
<v Speaker 4>pipeline of women with their MBA, And what that typically

0:18:07.480 --> 0:18:10.560
<v Speaker 4>means is you're going to see strong outcomes in the

0:18:10.600 --> 0:18:15.720
<v Speaker 4>consulting industry and financial services, in consumer package goods companies,

0:18:15.760 --> 0:18:17.520
<v Speaker 4>and in technology. I mean, those are kind of the

0:18:17.560 --> 0:18:21.040
<v Speaker 4>four top recruiters that are coming to NBA campuses. It

0:18:21.080 --> 0:18:23.000
<v Speaker 4>also means that more women are going to be starting

0:18:23.040 --> 0:18:25.520
<v Speaker 4>their own businesses because they're going to be going through

0:18:25.920 --> 0:18:29.320
<v Speaker 4>these incubators that are on these business school campuses, and

0:18:29.359 --> 0:18:32.119
<v Speaker 4>that's really encouraging as well, given the lack of funding

0:18:32.160 --> 0:18:36.280
<v Speaker 4>in VC. You want to see more companies being started

0:18:36.280 --> 0:18:38.800
<v Speaker 4>by women and being successfully funded, And.

0:18:38.800 --> 0:18:40.800
<v Speaker 1>Are you starting to see that in the data as

0:18:40.800 --> 0:18:43.080
<v Speaker 1>well as far as starting their own companies.

0:18:44.920 --> 0:18:47.000
<v Speaker 4>I mean, we're definitely seeing women advance. I mean, so

0:18:47.080 --> 0:18:49.080
<v Speaker 4>we've been doing this work for twenty years and I

0:18:49.080 --> 0:18:52.840
<v Speaker 4>think you're seeing them just becoming eligible for these very

0:18:52.880 --> 0:18:56.240
<v Speaker 4>senior C suite positions. So I do think there is

0:18:56.320 --> 0:18:59.200
<v Speaker 4>a robust pipeline. I think companies do have to continue

0:18:59.240 --> 0:19:03.520
<v Speaker 4>being intentional about how they advance all of their employees

0:19:03.560 --> 0:19:07.600
<v Speaker 4>and are they providing career roadmaps and ways for women

0:19:07.640 --> 0:19:10.439
<v Speaker 4>to advance and making sure again that they're focused on

0:19:10.480 --> 0:19:15.400
<v Speaker 4>that culture and really engaging all of their potential leaderships

0:19:15.440 --> 0:19:18.720
<v Speaker 4>in that conversation very transparently about how they see them

0:19:18.760 --> 0:19:19.800
<v Speaker 4>progressing to the top.

0:19:21.200 --> 0:19:23.640
<v Speaker 1>And ultimately, I mean, we only have about thirty seconds left,

0:19:23.640 --> 0:19:25.600
<v Speaker 1>But what do you think really needs to change in

0:19:25.720 --> 0:19:28.200
<v Speaker 1>order for this to accelerate here and change things?

0:19:30.080 --> 0:19:32.000
<v Speaker 4>Well, I think in business school, I mean we're getting

0:19:32.119 --> 0:19:34.240
<v Speaker 4>very close to parody, and so I would say the

0:19:34.240 --> 0:19:37.200
<v Speaker 4>big thing that needs to change is once those women

0:19:37.240 --> 0:19:40.879
<v Speaker 4>have graduated, how are we making sure that their advancement

0:19:41.000 --> 0:19:43.600
<v Speaker 4>is possible. There's a lot more shoots than ladders in

0:19:43.640 --> 0:19:46.280
<v Speaker 4>a women's career, and we want to make sure that

0:19:46.400 --> 0:19:49.280
<v Speaker 4>they're given those ladders to continue climbing and excelling.

0:19:50.320 --> 0:19:52.920
<v Speaker 1>Thanks so much for joining us. Really great insight into

0:19:53.000 --> 0:19:56.200
<v Speaker 1>all things, and especially such an important topic and great

0:19:56.280 --> 0:19:58.959
<v Speaker 1>question from Tim about obviously at the top and how

0:19:58.960 --> 0:20:02.160
<v Speaker 1>that could potentially change things here. But Alyssa Thankster, CEO

0:20:02.240 --> 0:20:05.720
<v Speaker 1>of Forte Foundation, joining us from San Antonio, Texas. Great

0:20:05.760 --> 0:20:07.080
<v Speaker 1>getting your insight on all of this.

0:20:07.800 --> 0:20:09.760
<v Speaker 2>But Tim, I'm sorry the electric just top of I mean,

0:20:09.840 --> 0:20:10.560
<v Speaker 2>it's a great.

0:20:10.359 --> 0:20:12.480
<v Speaker 1>Great point though as far as like, does that change

0:20:12.520 --> 0:20:14.440
<v Speaker 1>the dynamic when we're at the top though

0:20:14.560 --> 0:20:16.080
<v Speaker 2>It's I mean, I think a lot of people argue

0:20:16.119 --> 0:20:17.800
<v Speaker 2>it's all about representation, that's right,