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Cybersecurity in 2020: The rise of the CISO – MIT Technology Review

As the new year (and new decade) begins, one thing is certain: cybersecurity will continue to have an increasing impact on business, for better or worse. In this episode, we hear from Stephanie Balaouras, a cybersecurity expert who has spoken to thousands of customers over her 15 years at Forrester Research. She is the vice president and group director of security and risk research, as well as infrastructure and operations research.

Balaouras makes the case that all businesses should have a chief information security officer, or CISO, as the world of cyberthreats becomes more intricate and perilous. "Even companies that have a CISO should take a hard look at how high in the organization they report," Balaouras says. "Do they have the right budget? Do they have enough staff? Have you given them the right span of control?"

Balaouras also reviews some of the biggest cybersecurity trends in 2019 and makes predictions for 2020.

Business Lab is hosted by Laurel Ruma, director of Insights, the custom publishing division of MIT Technology Review. The show is a production of MIT Technology Review, with production help from Collective Next. Music is by Merlean, from Epidemic Sound.

Cybersecurity isnt only about stopping the threats you see. Its about stopping the ones you cant see. Thats why Microsoft Security employs over 3,500 cybercrime experts, and uses AI to help anticipate, identify, and eliminate threats. So you can focus on growing your business, and Microsoft Security can focus on protecting it. Learn more at Microsoft.com/Cybersecurity.

Show notes and links

Forrester Research: Cybersecurity

A CISOs Guide to Leading Change by Jinan Budge, Forrester Research

Stephanie Balaouras

The Need for Complete Cloud Security, an interview with Stephanie Balaouras, on YouTube

Full transcript

Laurel Ruma: From MIT Technology Review, I'm Laurel Ruma and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Security threats are everywhere. That's why Microsoft Security has over 3,500 cybercrime experts constantly monitoring for threats to help protect your business. More at microsoft.com/cybersecurity.

Our topic today is cybersecurity and more specifically the role of the chief information security officer, the CISO. We'll also review cybersecurity news from 2019 and look ahead to cybersecurity trends for 2020. One word for you: Deepfakes. My guest is Stephanie Balaouras who is a cybersecurity analyst and has spoken with thousands of customers in her nearly 15 years at Forrester research. Stephanie is the vice president and group director of security and risk research as well as infrastructure and operations research. Stephanie, thank you so much for talking with me on Business Lab.

Stephanie Balaouras: Thanks.

Laurel: So just to start, in 2017 Forester published a report by Jeff Pollard, a member of your team, about the career paths of CISOs, chief information security officers. And I particularly like talking about this role because it is so new to the C-suites in the business. If Citibank just appointed the first CISO ever, in 1995, that's really recent history. But if every company is also a technology company, why doesn't every company have a CISO?

Stephanie: If you look at the history of other roles, like I think the first CMO, chief marketing officer was in the 1950s you still had companies 20, 30 years later without a CMO. So when these emerging roles first start out, it does take some time for it to become the norm. But I will say every company really should have a CISO. Publicly traded companies are required to have a CISO. But what we will often find is, depending on the size of the company, sometimes they'll get away with calling the CIO also the CISO or some other IT executive the CISO as well. So that's pretty common with smaller companiesthey'll get away without having a standalone role.

But what you'll also find is if they have a breach or some sort of major cybersecurity issue or even a major compliance violation that's data security-related, the first thing that they'll do is name a dedicated CISO. And then even companies that have a dedicated CISO, when they have a breach, a lot of times what happens is they realize the CISO didn't report up high enough in the organization or didn't have the right span of responsibilities or enough budget or enough people. So then they'll fix that. Name CISO should be requirement, but I would say even companies that have a CISO should take a hard look at how high in the organization they report. Do they have the right budget? Do they have enough staff? Have you given them the right span of control?

Laurel: Because that's an expensive fix, isn't it?

Stephanie: Yes, exactly.

Laurel: Only after an attack, do we have looking at the roles and responsibilities in a new light, in a more responsible light?

Stephanie: Exactly.

Laurel: So if the perfect CISO is a bit of both a businessperson who can talk directly to the CEO, explain the necessity for security and risk mitigation, but then also talk to customers perhaps as well as other employees and talk about security and how that role is important to the company. Where are these people coming from? Where are they getting all of this education?

Stephanie: When we looked at CISO career paths, we did find most of them did come up through the security ranks. So typically they did start off as security professionals. They gained decades of experience in the role. But what we often found is the majority of them often would go back for graduate degrees, and they would actually go after a business degree. They would often get MBAs, and it was because they needed to satisfy both of those requirements, which is, yes, I'm a technology executive, but at the same time I'm a technology executive that in a large company reports to the board on a quarterly basis or reports directly to the CIO or directly to the CEO. So it's definitely a combination of education and experience.

I would say universities are doing a better job of providing undergraduate and graduate degrees in information security. There's some areas where they're incredibly weak, like application security is not taught well at the undergraduate level, if at all. It's done really poorly. The other thing I'll pick on universities about is they're not doing a good job of recruiting women into undergraduate and graduate programs. There is a huge skills gap as well as a staffing issue in security, and at Forrester we like to say it's largely self-inflicted because we're not recruiting from half of the population, and we're not recruiting people from diverse backgrounds. We've got this one mold of individual that we recruit from, and then we're shocked when we can't find enough people with this very narrow skill set, so.

Laurel: Yeah, the report said nine out of 10 CISOs are male.

Stephanie: Exactly, exactly. We haven't looked at the end of 2019 yet, but the last couple of years that's been true. And if you look at the staff as well, it's worse than the general security industry. It's at about 11% of security staff are female. It's worse than general IT. General IT also has a problem, but it's more somewhere between 20% to 30%, so security is even worse.

Laurel: So when we talk about lack of diversity in security in general, how are companies trying to respond to that? Are you seeing any particular companies showing best practices?

Stephanie: There are definitely some best practices. Actually, I've seen a lot of vendors, like large technology vendors, they're actually partnering with universities and they're actually even partnering at the high school level. For example, with the Girl Scouts of America, to actually foster programs that get girls excited and interested in cybersecurity from a very young age and then want to continue to pursue it at an undergraduate and graduate level. At a number of universities, there's pretty aggressive scholarship programs.

And then also there's just a lot of introspection that's happening at the corporate level where we kind of look at the culture of security teams. We look at a lot of the traditional routes from where we recruit from, which is conferences that are male-dominated or again, we have these job descriptions that emphasize a lot of military experience as an example. So it's sort of like broadening the aperture of people that will recruit into the security industry, a willingness to develop their skill set as well as doing a much better job of actually filling the funnel, filling the actual pipeline over the long term.

But to your point, diverse teams make better decisions, and in the long run they're higher-performing. And then the second thing I would say is there are so many open jobs in security, not just in the US, but also globally. It's also a math problem. We are not going to fill these open positions if we're not recruiting from half the population.

Laurel: Also, it seemed like not a lot of security talent was necessarily promoted from within. In the reports from Forrester, it sounded like you had more likelihood to be given a promotion if you went to a different company. Are companies now re-examining their own talents?

Stephanie: That's actually true at kind of the individual level as well as the CISO level. So we've found amongst the Fortune 500 that first-time CISOs were rare, and they weren't promoted from within. So they'd like to hire externally for CISOs, and they wanted CISOs that had prior experience as a CISO. And actually if you are somebody in security, so that means if you want to be promoted into CISO, your best opportunity is actually to look externally, outside your company. And we also found that when companies hired CISOs externally as opposed to promoting them from within, they were more likely to have them report higher up in the organization. So yeah, at the CISO level, companies could do a better job of looking within and giving those individuals the right opportunity to report higher in the organization.

But we also found things similar again at manager levels and individual contributor levels, which is they weren't hiring from within the company or when they did hire individuals, they weren't giving them good career paths and ongoing skills development. So again, if those individuals really wanted to further their career, most of them ended up leaving. So that's why we say so much of the skills and the staffing challenges are completely self-inflicted.

Laurel: So it's a bit of a blind spot that probably everyone could do a little bit better on, right?

Stephanie: Exactly. Yeah.

Laurel: I was reading this Ponemon Institute report, and this particular phrase jumped out at me and as we were talking about the CISO and what kind of person would do that role in the first place and the experience they had, more than just a resume, it's also your attitude and ability to act really quickly and really smartly and also communicate very well. But the quote was, and I'm paraphrasing a bit here, technology has transformed the internet age into a period of cruel miracles for security professionals. All of our cruel miracles are that we have devices in every pocket. We can go anywhere, we can talk to anybody at anytime, and we can do it at the speed of a lightning bolt, but at the same time if you're a CISO, how do you secure it all?

Stephanie: Right. Yeah. All of these devices that extend the four walls of the company, they are basically extending the attack surface of the organization. So for CISOs, it's been sort of this march away from a traditional perimeter-based approach to security and actually taking more of a data-centric and application-centric, and I would even say identity-centric approach to security.

Not that the network's not important, network security is hugely important, but it's more of the perimeter-based approach to security that's changed dramatically. So again, where there's no true four walls of the corporation. The perimeter is actually much smaller. So we tend to think of secure enclaves. How do I build a micro perimeter around our most important assets?

When we talk about an extended network of all kinds of devices like you mentioned or the computing environment itself, which could be a combination of on-premise, cloud, hosted private cloud, and every variation thereof and any kind of user population that interacts with the company systems and data. That could be your own employees, it could be consumers and customers, it could be third-party partners. So when you think about devices, user populations and different computing models, there is no perimeter. So the focus becomes on protecting the data itself regardless of where it travels and regardless of the hosting model or the location. Really, really taking a hard look at identity. So limiting and strictly enforcing access, both human and nonhuman. So it does kind of flip the traditional security paradigm on its head. You move away from perimeter-centric to data- and identity-centric.

That's what we typically recommend to CISOs. And we call that the zero-trust model of security, which is you assume you already have a breach, and you never assume trust in your environment. You just always assume that something's going wrong somewhere, but it works. It perhaps is not the most positive spin in the world, like, oh, zero trust, but it works. It's very effective.

The other thing I would say is I would really encourage manufacturers of all these devices, IoT sensors, IoT devices, everything that you can think of to really do a better job of building security into the device itself from the beginning. That would definitely make the CISO's job much easier. It's just so frustrating. It's largely out of their control as well.

Laurel: Right? Well, especially

Stephanie: Except for the CISOs that actually work at product companies. You should be involved in product development. You should be advising the organization.

Laurel: And it's interesting because security doesn't always come first, does it?

Stephanie: No.

Laurel: Especially when you're doing product design. So do you see that happening often though? CISOs actually actively involved in product design?

Stephanie: Not to date, unfortunately, but it is something that we do recommend. And I would say some CISOs don't necessarily see it as their traditional role, like their traditional role has been to secure the back-end systems of record and infrastructure and the company's data and not necessarily get involved in development, but we actually actively encourage CISOs to get involved in product design and product development to really help the organization secure what you sell. So whatever it is you sell, whatever service it is you're delivering to a consumer, a patient, a citizen, another corporation, if you're a B2B organization, actively being involved in securing what you sell.

Laurel: And that's certainly a competitive differentiator, isn't it?

Stephanie: Yeah, absolutely. Absolutely. We found security, as well as privacyand those aren't synonymous, but sometimes they do go hand in handdoes create competitive differentiation for companies.

Laurel: Yeah. And that's an important differentiator, and all the noise that you have coming out. So if there's something very specific that you can market, that would be a good one. But we're also kind of talking about the CISO really taking an active role in everything. So you have to be this multi-talented person who can talk and understand product as well as be out and about in the community, right? All at the same time sharing, but not sharing, company secrets and how you defend the data because there is this idea, especially in the tech community, where you do share your best practices and what you've learned from. And I was just wondering a bit about that, how do CISOs actually share but not share everything?

Stephanie: Yeah, there is that challenge. A lot of CISOs are very loathe to talk about specifics about their deployments, and I don't necessarily see that changing anytime soon. Sometimes in smaller groups though, there are a lot of communities that support CISOs. Actually at Forester, we have a peer networking group of about a 100 CISOs. There's all kinds of ISACs and intelligence sharing communities amongst like CISOs that are industry specific. So often in tightknit communities where there's an understanding that everything's under NDA, where there's candidness, where there's some personal relationships, CISOs will share a lot more. But I have found CISOs willing to talk about overall strategy. When I mentioned moving from perimeter-based approaches to data- and identity-centric. Talking about culture. Culture is actually hugely important, not just at the CISO but for the rest of the security organization as well.

Because you need an organization that has the right kind of staff that can actually talk to developers and be part of secure application development, that can work with infrastructure and operations teams to secure cloud deployments. That could actually work with marketing teams to help them understand privacy implications of how they might be personalizing services and data and ads to consumers. So you need also the security team itself, not just the CISO, the security team itself to be vocal and outspoken, collaborative and willing to insert themselves into core business and IT processes throughout the organization. So they'll talk about culture, they'll talk about staffing, they'll talk about the kind of skills that are required as well. We definitely see some change there.

Laurel: And also the business has to be willing to allow security kind of come full circle on this idea but not just product but then also everyone else. So marketing, thinking, again, security first or security at some point. How do you then have this conversation, so everyone is a bit educated? You don't have to be an expert in security if you're in marketing, but you have to be willing to listen.

Stephanie: A lot of times CISOs would kind of tell the stories and everything was doom and gloom. I think taking a much more risk-based approach where you're helping the business understand future risks and helping them just understand both probability and impact and advising them on making the right decisions, like moving from that department of no to more of that consultative role I think helps. The more you become that consultative subject matter expert more, I think you can bring along the rest of the organization with you. I think that that's a big help and it sort of varies by CISO skill set as to how good they are at doing that. I think anytime you can put things in a positive business terms as well, that helps.

There was an analyst on my team that wrote this report, it was called security for profit, and in it he outlined ways that security could potentially be a revenue generator for the company. Again, it could be value-added features that people were willing to pay more, or it becomes a competitive differentiator in a product or service that you offer. So it could actually contribute to the top line. And then he also outlined all the ways that can actually save the company money beyond breach avoidance and avoidance of compliance fines.

There's all kinds of ways where if you do security right, it can actually dramatically improve employee experience and reduce operational costs within the company. Identity is one of the biggest examples, when you think about onboarding an employee and the ability to automate all the ways that you give them access to the systems that they need. Resetting passwords. I mean, there's so many just low-hanging fruit where you can make employees lives easier, but then you're actually really reducing hard costs.

Laurel: Yeah. And that's certainly something you don't think about, but you are certainly frustrated when you have to redo your password and it takes forever and/or you have to go on a different system and blah, blah, blah. But that kind of streamlining is not just from a security perspective, but as you said, it's from everyone's perspective to just make their lives easier, which is what ultimately every employee wants.

Stephanie: Yep.

Laurel: So how do CISOs stay on top of the latest trends? I mean, conferences, those small groups that they talk to?

Stephanie: Yeah, I think they do do their own research, whether it's publications like yours, firms like Forrester, the other big kind of strategy consulting firms as well. They do do their own research though. They'll often send their staff to a lot of the conferences. And then I do think those peer-networking groups help dramatically as well. But it is hard to stay on top of every single possible trend. So I do think it always helps to have some sort of external advice as well, to give you a heads up on emerging threats, on emerging risks, emerging compliance and regulations that are happening all over the globe.

Laurel: Yeah, and then just like you said, having that peer group to establish trust and some kind of transparency with sharing best practices and just hearing various stories, even if it's from a friend I've heard, to kind of get those warnings out to various organizations and people. Speaking of that, other than in these peer groups, is there much cooperation between government and business? Are you seeing more of it or do people pretty much pretty stay in their lane because there are other conflicts to worry about with businesses and governments?

Stephanie: Yeah. In the US and actually other countries like the UK, if you're considered a critical infrastructure industry, you will need to have close relationships with federal government officials. If you're in critical infrastructure, I mean, there's going to be industry-specific cybersecurity regulations that you have to follow, you know, if you're in energy. I mean, even financial services is considered critical infrastructure. So then you'll have to follow NIST guidelines, as an example. Anybody doing business with the federal government will have to follow NIST.

You don't want to wait to form relationships with the federal government or specific agencies, like the FBI. You don't want to wait until you suspect something or have a breach. Or in a lot of cases, it's the reverse which is, they've detected something, they're alerting you to it. Sometimes, they can't offer you specifics because their hands are tied as part of a larger investigation. So you can actually develop relationships with a lot of the US federal government agencies ahead of time, so that you can share threat intelligence. Or again, should something actually really occur, you already have those pre-existing relationships in place.

Laurel: Yeah, and speaking of something already occurring and preparation plans, are you seeing more companies develop those preparation plans for, again, not if, but when they are hacked or a cyberattack happens and they need to go public with it?

Stephanie: So with incident response, there's sort of the internal incident response, which is sort of all of the processes that you need to detect, then remediate, and then respond. And a lot of the responding is more of what we call kind of a forensic level responding: determining exactly what happened, remediating it, potentially collecting forensic evidence if you decided that you were actually going to pursue legal action, depending on who it was afterwards. Then there's the external response, and you really need both. You really need a sophisticated incident response, process and initiative within the company with dedicated experts, particularly if you're a large enterprise.

But I think where companies often really fall down is on external breach response. And again, regulations require that if it's consumer-related, if it's affected individuals, you are required to notify them within specific days. In many cases, it's 30 days. Under GDPR in Europe, it's 72 hours or less. And we have seen companies royally botch the external breach response, meaning that they were cagey about offering information to consumers.

I don't want to pick on companies because victim blaming often isn't all that helpful, but I've seen companies kind of blame the consumer, in a way, saying, "Oh, if you had better password hygiene, if you were monitoring your own accounts much more closely, this wouldn't be as big of an impact." No. You need to show empathy with your customers. Put them first. Do everything you can to protect them. Don't be cagey about sharing information because of CYA kinds of concerns. And in some cases, if you do it right, it's an opportunity to not lose their trust, but potentially even to reinforce it and build it up, if you've put them first. But you can really botch it and make the breach so much worse than it needed to be.

Laurel: And that just cost the company even more money.

Stephanie: Exactly.

Laurel: When you look back at 2019 and there's a lot to talk about cybersecurity wise, if we kind of look at three specific areas, first off is just cyberattacks, but very specifically on cities and municipalities. So New Orleans was the most recent, as of the end of the year, that we know of, but it was also on the heels of the State of Louisiana having a cybersecurity attack. We know it's happening across the country. So to ask a very loaded question, why are cities and municipalities being targeted for cyberattacks when they're not necessarily the most well-funded outfits?

Stephanie: Yeah. So that's why, because they're easy targets. So if they've been underfunding their security efforts for years, then they're much easier to penetrate and then ask for a ransom, even if the ransom if small.

Laurel: It's better than nothing.

Stephanie: It's better than nothing. That's actually the consensus of a lot of the team, is so many of these local, city, and state governments and municipalities are just such easy targets because they have been underfunded and understaffed for years. And most of the time, there is financial motivation, but there are other types of motivation. It could be political, social. If you get to a larger kind of states or federal agency, you might even get into geopolitical and even military in some of the nature.

Actually, the City of New Orleans, what was interesting about that is the attackers didn't ask for a ransom. So they used ransomware to disable them. Everything was encrypted and forced them. I think they were replacing tons of computer infrastructure. It can be really difficult to recover from backups. We say that so flippantly, like, "Oh, just recover from your backups." Most backups complete with errors and the ability to recover from a backup at scale is actually very, very difficult. And who knows when the ransomware was actually introduced? So then you're just reinstalling the ransomware.

Laurel: Interesting.

Stephanie: But yeah. From my understanding, they didn't actually ask for a ransom. So their motivation wasn't financial. So it could've been ...

Laurel: Just disruption.

Stephanie: ... just disruption for the sake of it.

Laurel: To see if they could do it, yeah.

Stephanie: Or interestingly enough, I read this article where it's forced the city to replace a ton of computer infrastructure, laptops, desktops, server infrastructure. So theres a part of me that's wondering, "Oh, it could be city employees. I know how to get the city to upgrade."

Laurel: Right, right. Force them.

Stephanie: Force them.

Laurel: By ruining everything.

Stephanie: Yeah. So they're easy targets and the motivations for the attack are much varied, I think, when it comes to critical infrastructure and then city, state, and local government.

Laurel: And that's not necessarily when a ransom is asked for that you ever find out where they're coming from or who they are or if they are foreign state actors.

Stephanie: Yeah, you don't necessarily know.

Laurel: You'll never know. It's just a guess.

Stephanie: Yeah. We actually put out a controversial report this year that said, in some cases, organizations might want to consider paying the ransom. I'll be honest, I think for city, state, and local governments they might be prohibited from paying the ransom. I don't know. I would have to look into that, but private-sector companies, even though I'm sure FBI and other law enforcement agencies would prefer that they not do so, in some cases, it might actually make sense. And cyber insurers would even say that it might make sense in some cases. And there are actually firms that specialize in helping companies pay the ransom. Sometimes, you can actually negotiate for a lower ransom. It's like bartering. They'll act as the go-between between the various characters in the company. Obviously, you're paying them in a cryptocurrency. You're not just transferring cash.

Laurel: Of course.

Stephanie: So they can facilitate that, as well. I mean, if you look at the City of Baltimore, what they ended up spending to recover from the ransomware attack was probably a hundred times more than the actual ransom. I forget the numbers, but the difference was ridiculous.

Laurel: So some advice to cities and municipalities would be to actually look at your systems an try to get them up to date and protected, in some way.

Stephanie: Yeah. Certainly with ransomware, make sure all your systems are up to date, patched. If you look at most successful attacks, external attacks, they're taking advantage of vulnerabilities and other types of software exploits. It's nothing fancy. Everybody always loves to use advanced attacks or state-sponsored attacks. The reality is most of these attacks are pretty low budget, but yet still effective.

The other thing is take a close look at your backups. I can't emphasize it enough. People always overlook their backups. It becomes this rote IT process that nobody ever looks twice at or people demean it and call it not important. It could be more important today if you don't want to pay the ransom.

****

Laurel: Cybersecurity isn't only about stopping the threats you see. It's about stopping the ones you can't see. That's why Microsoft Security employs over 3,500 cybercrime experts and uses AI to help anticipate, identify, and eliminate threats so you can focus on growing your business and Microsoft Security can focus on protecting it. Learn more at microsoft.com/cybersecurity.

****

Laurel: So another interesting topic coming out of 2019 were just general data breaches. So 2019, it did really seem like, every other day, some company or someone was announcing a data breach. And then, according to Risk Based Security, 2019 saw more than seven billion records exposed. So when we get back to CISOs, how are CISOs and company executives really responding to that if 2019 was sort of this year where we [have seen so many] breaches, in one year?

Stephanie: Yeah. I do think 2019 was finally the year of breach fatigue. I mean, it was even difficult for us to keep up with every breach that hit the news. I do think it helps to put it in perspective. Not every one of these breaches was an attack. A lot of them actually were the result of accidental exposures. So if, for example, you misconfigured cloud storage, that's actually considered a breach, even though there's not necessarily any proof that any kind of third party or external attacker or organization actually misused or abused the data. Just the fact that somebody either internally or, oftentimes, it's the security researcher actually who discovers that all the information was less exposed. That is considered a breach.

But yeah. If you look at breaches, themselves, 51% of companies had at least one breach in the past year. And that number is probably higher because a lot of organizations don't know about it immediately. But then, there are a large percentage of them, actually the majority, are internal, a result of internal incidents, third-party incidents, or just lost or stolen devices. And if you do look at true external breaches, where it was an external party that attacked you and gained access to your sensitive data, getting back to a lot of it's low budget, the top three attack vectors were a direct attack on your application, taking advantage of a software vulnerability, or compromised user credentials.

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Cybersecurity in 2020: The rise of the CISO - MIT Technology Review

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‘Financing the Future’ with Barry Gross, Partner at BCLP Law – Finvest Summit Special – Data Economy

In what is the first truly global awards for the financers, legalexperts and advisory firms in the data centre, cloud and edge computingmarkets, the Finvest Global Awards 2020 will be the beacon that recognises thosedriving global deals.

London/Zurich 09 February 2020 Data Economy, the global data centre news website and media service, has announced the shortlist of companies and personalities for the Finvest Global Awards 2020, taking place The Dolder Grand, in Zurich, Switzerland, on 13 February 2020.

Organised by BroadGroup, the accolades will celebrate the best ofbest in the industry from CFO of the Year to Law Firm of the Year, HyperscaleInvestment of the Year and more.

An independent panel of 12 judges, headed by industry entrepreneurand philanthropist Michael Tobin is in charge of finding the 2020 winners.

The awards take place on the evening of the Data Economy FinvestGlobal Summit. The networking and knowledge sharing event attracts every yearinvestors, private equity, hedge funds, bond specialists, pension funds,property specialists and bankers, as well as the IT infrastructure leadershipof data centre, cloud, edge computing and telecoms businesses.

Following the success of the first run of the Finvest Awards at Datacloud Global Awards 2020 in Monaco, the shortlist for Zurich 2020 is as follows:

HyperscaleInvestment of the Year Award

LawFirm of the Year Award

EdgeInvestment Award

M&Aof the year Award

CFOof the Year Award

GlobalFinancial Leader Award

DataEconomy Personality of the Year in the Global Finance and Investment Sector

Head Judge Michel Tobin says: Data Economy is the leading reference in the Data Centre industry so who better to select and recognise the leaders in the sector, at this years Finvest Summit and Awards. As this years Chair of judges, I am looking forward tocelebratingthe incredible achievements of the best the Data Centre industry can offer.

The high quality of the nominations received for these years Finvest Global Awards is outstanding and clearly shows how the data centre and cloud sectors have matured in the past decade into being a multi-billion Dollar industry of its own driven by talent and passion, says Joo Marques Lima, founder and editor-in-chief of Data Economy.

Companies can benefit by attending the Awards and also sponsoring one of the Awards and start making contact with a global network of potential customers and partners to jump-start the success of their innovations.

Hosted by Data Economy, this years Awards ceremony will bethe most exciting night of the year celebrating the best in the multi-billionglobal industry that data centre and cloud have now become.

For more information on how toattend, please visit the Data Economy Finvest Global Summit and Awards 2020website.

About BroadGroup

BroadGroup is an Information Media Technology company.Established in 2002, the company delivers premium event brands includingDatacloud and Edge and Awards, which are an internationally recognized beaconof high quality content, deal making, networking and industry recognition fordata center, cloud and Edge leaders, their enterprise customers, investors andsenior executives. It also owns the widely acclaimed Data Economy online andoffline global news resource and investor forums provider for the tech sector.BroadGroup is now a member company of FTSE 250 firm Euromoney InstitutionalInvestor PLC whose leading brands include Capacity, Metro Connect, SubseaConnect and ITW. http://www.broad-group.com

About Data Economy

Data Economy, launched in 2016, is part of publishing andevents company Broadmedia Communications, now acquired by EuromoneyInstitutional Investor PLC a member of the FTSE 250 share index. Data Economysaward-winning journalists deliver exclusive content targeted at C-levelexecutives in datacenter services companies, their investors, legal advisorsand technology suppliers. Collectively this audience contributes to criticalfinancial, infrastructure and business decisions that impact not only theirbusinesses but thousands of enterprises across the globe and their customers.Data Economy publishes a daily newsletter, online and print, and producesvideos, webinars and events. Data Economy is also an active member of theProfessional Publishers Association (PPA). Visit the website atwww.data-economy.com

Excerpt from:
'Financing the Future' with Barry Gross, Partner at BCLP Law - Finvest Summit Special - Data Economy

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The Role of AI and Machine Learning in Cybersecurity – Analytics Insight

AI and machine learning are the kind of buzzwords that generate a lot of interest; hence, they get thrown around all the time. But what do they actually mean? And are they as instrumental to the future of cybersecurity as many believe?

When a large set of data is involved, having to analyze it all by hand seems like a nightmare. Its the kind of work that one would describe as boring and tedious. Not to mention the fact it would take a lot of staring at the screen to find what youve set out to discover.

The great thing about machines and technology is that unlike humans it never gets tired. Its also better geared for being able to notice patterns. Machine learning is what you get when you reach the point of teaching your tools on how to spot patterns. The AI helps you interpret it all better and make the solution self-sufficient.

Cybersecurity solutions (antivirus scanners in particular) are all about spotting a pattern and planning the right response. These scanners rely on heuristic modeling. It gives them the ability to recognize a piece of code as malicious, even though it might be the case that no one has flagged it as such before. In essence, it has plenty to do with teaching the software to recognize and alert you when something is out of the ordinary.

As soon as something oversteps the threshold of tolerance, it triggers an alarm. From there on out, the rest is up to the user. For instance, the user may instruct the antivirus software to move the infected file to quarantine. It can do so with or without human intervention.

Applying AI to cybersecurity solutions is taking things up a notch. Without it, the option of having the software learn on its own by observing would not be possible.

Imagine having an entity working in the background that knows you so well that it can predict your every move. It might be slight nuances. For example, the way you move your mouse or the parts of the web youre browsing on a frequent basis. Even the order of the applications you launch upon logging in.

Without having to introduce yourself, the AI would get to know you and your habits pretty well. Thus, it would form a digital fingerprint of you. It sounds scary, but it could come in handy. For instance, it could raise the alarm if an unauthorized individual ever gets access to your PC.

Of course, observing your behavior is not the end of what employment of AI and machine learning can do. Why not do the same thing for computer processes?

Imagine having to monitor what programs are running in the background yourself. Tracking how much resources they consume all day, every day, by hand. It doesnt sound enjoyable now, does it? But its the work AI excels at.

Without lifting a finger, youd have a powerful watchdog that would start barking as soon as something is out of the ordinary. For instance, it could alert you about malicious operating system behaviors. You would know right away about crypto mining malware or other types of threats affecting your computer.

The smart malware designers make it so that your systems CPU usage gets off the charts only when youre not using the PC. Theres no way to spot such a thing while youre away from the keyboard. Unless you have AI-powered cybersecurity solutions to track it all for you 24/7.

Webmasters keep trying to fend off bot traffic and automated scripts. These are used for automatic data scraping and similar activities. For instance, someone could write a script to harvest every bit of contact details on the website. They can then send unsolicited offers to all those contacts. Even when they dont scrape contacts, no one wants bot traffic because it consumes valuable server resources and slows everything down for legitimate browsers. Thus, it harms the user experience.

The simple solution is to block a range of IP addresses. But by using a VPN (you can read more about it here) server or a proxy, a script can get around the obstacle. Now lets introduce some AI into the equation. By observing every browsers activity, it would be able to recognize repetitive behavior. It would associate it with an IP address thats currently browsing, then flag it. Sure, a script may discard an IP address and try with a new one. But the fingerprint left by its activities would remain since its rather much pattern-based. In the end, the new IP could be flagged much faster by automated observation.

Since they came to be, AI and machine learning have changed the world of cybersecurity forever. As time goes on, they will keep getting more and more refined. Its a matter of question when it will reach the point of becoming your cybersecurity watchdog, tailored to your needs.

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Why Unsupervised Machine Learning is the Future of Cybersecurity – TechNative

Not all Artificial Intelligence is created equal

As we move towards a future where we lean on cybersecurity much more in our daily lives, its important to be aware of the differences in the types of AI being used for network security.

Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars.

However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies.

Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning).

Supervised Learning relies on a process of labeling in order to understand information.

The machine learns from labeling lots of data and is able to recognize something only after someone, most likely a security professional, has already labeled it, as it can not do so on its own.

This is beneficial only when you know exactly what youre looking for, which is definitely not commonly the case in cybersecurity. Most often, hackers are using a method of attack that the security program has not seen before in which case a supervised system would be totally useless.

This is where Unsupervised Learning comes in. Unsupervised Learning draws inferences from datasets without labels. It is best used if you want to find patterns but dont know exactly what youre looking for.

This makes it useful in cybersecurity where the attacker is always changing methods. Its not looking for a specific label, but rather any pattern that is out of the norm will be flagged as dangerous, which is a much better method in a situation where the attacker is always changing forms.

Unsupervised Learning will first create a baseline for your network that shows what everything should look like on a regular day. This way, if some file transfer breaks the pattern of regular behavior by being too large or sent at an odd time, it will be flagged as possibly dangerous by the Unsupervised system.

A Supervised Learning program will miss an attack if it has never seen it before because it hasnt yet labeled that activity as dangerous, whereas with Unsupervised Learning security, the program only has to know that the action is abnormal in order to flag it as a potential threat.

There are two types of Unsupervised Learning: discriminative models and generative models. Discriminative models are only capable of telling you, if you give it X then the consequence is Y. Whereas the generative model can tell you the total probability that youre going to see X and Y at the same time.

So the difference is as follows: the discriminative model assigns labels to inputs, and has no predictive capability. If you gave it a different X that it has never seen before it cant tell what the Y is going to be because it simply hasnt learned that. With generative models, once you set it up and find the baseline you can give it any input and ask it for an answer. Thus, it has predictive ability for example it can generate a possible network behavior that has never been seen before.

So lets say some person sends a 30 megabyte file at noon, what is the probability that he would do that? If you asked a discriminative model whether this is normal, it would check to see if the person had ever sent such a file at noon before but only specifically at noon. Whereas a generative model would look at the context of the situation and check if they had ever sent a file like that at 11:59 a.m. and 12:30 p.m. too, and base its conclusions off of surrounding circumstances in order to be more accurate with its predictions.

The Artificial Intelligence that we are using at MixMode now is what is in the class of generative models in Unsupervised Learning, that basically gives it this predictive ability. It collects data to form a baseline of the network and will be able to predict what will happen over time because of its knowledge of what a day of the week looks like for the network.

If anything strays from this baseline, the platform will alert whichever security team oversees it that there has been an irregularity detected in network performance that should be adhering to the baseline standard.

For example, It collects data as it goes and then it says I know whats going to happen on monday at 9: People are going to come in and network volume will grow, then at noon they gonna go for lunch so the network level will drop a bit, then theyll continue working until six and go home and the network level will go down to the level it is during the night.

Because of its predictive power, the Generative Unsupervised learning model is capable of preventing Zero-Day attacks, which makes it the best security method out there and has the fastest response time to any breach.

Semi-Supervised or Active Learning takes the best of both unsupervised and supervised learning and puts them together in order to make predictions on how a network should behave.

Active learning starts with unsupervised learning by looking for any patterns on a network that deviate from the norm, then once it finds one it can label it as a threat, which is the supervised learning portion.

An active learning platform will be extremely useful because not only is it constantly scanning for any deviations on the network, but it is also constantly labeling and adding metadata to the abnormalities it does find which makes it a very strong detection and response system.

Featured image: Pablo Lagato

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JP Morgan expands dive into machine learning with new London research centre – The TRADE News

JP Morgan is expanding its foray into machine learning and artificial intelligence (AI) with the launch of a new London-based research centre, as it explores how it can use the technology for new trading solutions.

The US investment bank has recently launched a Machine Learning Centre of Excellence (ML CoE) in London and has hired Chak Wong who will be responsible for overseeing a new team of machine learning engineers, technologists, data engineers and product managers.

Wong was most recently a professor at the Hong Kong University of Science and Technology, where he taught Masters and PhD level courses on AI and derivatives. He was also a senior quant trader at Morgan Stanley and Goldman Sachs in London.

According to JP Morgans website, the ML CoE teams partner across the firm to create and share Machine Learning Solutions for our most challenging business problems. The bank hopes the expansion of the machine learning centre to Europe will accelerate the deployment of the technology in regions outside of the US.

JP Morgan will look to build on the success of a similar New York-based centre it launched in 2018 under the leadership of Samik Chandarana, head of corporate and investment banking applied AI and machine learning.

These ventures include the application of the technology to provide an optimised execution tool in FX algo trading, and the development of Robotrader as a tool to automate pricing and hedging of vanilla equity options, using machine learning.

In November last year, JP Morgan also made a strategic investment in FinTech firm Limeglass, which deploys AI, machine learning and natural language processing (NLP) to analyse institutional research.

AI and machine learning technology has been touted to revolutionise quantitative and algorithmic trading techniques. Many believe its ability to quantify and analyse huge amounts of data will enable them to make more informed investment decisions. In addition, as data sets become more complex, trading strategies are increasingly being built around new machine and deep learning tools.

Speaking at an industry event in Gaining the Edge Hedge Fund Leadership conference in New York last year, representatives from the hedge fund and allocator industry discussed the significant importance the technology will have on investment strategies and processes.

AI and machine learning is going to raise the bar across everything. Those that are not paying attention to it now will fall behind, said one panellist from a $6 billion alternative investment manager, speaking under Chatham House Rules.

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This tech firm used AI & machine learning to predict Coronavirus outbreak; warned people about danger zones – Economic Times

A couple of weeks after the Coronavirus outbreak and the disease has become a full-blown pandemic. According to official Chinese statistics, more than 130 people have died from the mysterious virus.

Contagious diseases may be diagnosed by men and women in face masks and lab coats, but warning signs of an epidemic can be detected by computer programmers sitting thousands of miles away. Around the tenth of January, news of a flu outbreak in Chinas Hubei province started making its way to mainstream media. It then spread to other parts of the country, and subsequently, overseas.

But the first to report of an impending biohazard was BlueDot, a Canadian firm that specializes in infectious disease surveillance. They predicted an impending outbreak of coronavirus on December 31 using an artificial intelligence-powered system that combs through animal and plant disease networks, news reports in vernacular websites, government documents, and other online sources to warn its clients against traveling to danger zones like Wuhan, much before foreign governments started issuing travel advisories.

They further used global airline ticketing data to correctly predict that the virus would spread to Seoul, Bangkok, Taipei, and Tokyo. Machine learning and natural language processing techniques were also employed to create models that process large amounts of data in real time. This includes airline ticketing data, news reports in 65 languages, animal and plant disease networks.

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We know that governments may not be relied upon to provide information in a timely fashion. We can pick up news of possible outbreaks, little murmurs or forums or blogs of indications of some kind of unusual events going on, Kamran Khan, founder and CEO of BlueDot told a news magazine.

The death toll from the Coronavirus rose to 81 in China, with thousands of new cases registered each day. The government has extended the Lunar New Year holiday by three days to restrict the movement of people across the country, and thereby lower the chances of more people contracting the respiratory disease.

However, a lockdown of the affected area could be detrimental to public health, putting at risk the domestic population, even as medical supplies dwindle, causing much anger and resentment.

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Google shows off far-flung A.I. research projects as calls for regulation mount – CNBC

Google senior fellow Jeff Dean speaks at a 2017 event in China.

Source: Chris Wong | Google

Artificial intelligence and machine learning are crucial to Google and its parent company Alphabet. Recently promoted Alphabet CEO Sundar Pichai has been talking about an "AI-first world" since 2016, and the company uses the technology across many of its businesses, from search advertising to self-driving cars.

But regulators are expressing concern about the growing power and lack of understanding about how AI works and what it can do. The European Union is exploring new AI regulation, including a possible temporary ban on the use of facial recognition in public, and New York Rep. Carolyn Maloney, who chairs the House Oversight and Reform Committee, recently suggested that AI regulation could be on the way in the U.S., too. Pichai recently called for "clear-eyed" AI regulation amid a rise in fake videos and abuse of facial recognition technology.

Against this backdrop, the company held an event Tuesday to showcase the positive side of AI by showing some of the long-term projects the company is working on.

"Right now, one of the problems in machine learning is we tend to tackle each problem separately," said Jeff Dean, head of Google AI, at Google's San Francisco offices Tuesday. "These long arcs of research are really important to pick fundamental important problems and continue to make progress on them."

While most of Google's projects are still years out from broad use, Dean said they are important in moving Google products along.

Here's a sampling of some of the company's more speculative and long-term AI projects:

Google's robotic kitten helps it understand locomotion.

Google's D'Kitty is a four-legged robot that the company says learned to walk on its own by studying locomotion and using machine learning techniques. Dean said he hopes Google's research and development findings will contribute to machines learning how physical hardware can function in "the real world."

Using braided electronics in soft materials, Google's artificial intelligence technology can connect gestures with media controls. One prototype showed sweatshirt drawstrings that could be twisted to adjust music volume. The user could pinch the drawstrings to play or pause connected music.

Google's tech-woven fabric can control music.

A new transcription feature in Google Translate will convert speech to written transcript and will be available on Android phones at some point in the future. Natural language processing, which is a subset of artificial intelligence, is "of particular interest" to the company, Dean said.

Google Translate currently supports 59 languages.

Google Health announced new research Tuesday, showing that when the company's AI is applied to retinal scans, it can help determine if a patient is anemic. It can also detect diabetic eye diseases and glaucoma, Dean said. The company hopes to analyze other diseases in the future.

Google examines eye health

Google is using sensing tools to track underwater sea life. Using sound detection and artificial intelligence, the company said it can now detect orcas in real time and send messages to harbor managers to help them protect the endangered species.

Google announced Tuesday that it's teaming up with organization DFO and Rainforest Connection to track critically endangered Southern Resident killer whales in Canada. The company's also in the early stages of working with the Monterey Bay Aquarium to help detect species in the ocean nearby.

Google's artificial intelligence can detect certain sea animals based on sounds.

Google's working on a project called MediaPipe, which analyzes video of bodily movements including hand tracking. Dean said the company hopes to read and analyze sign language.

"Video is the next logical frontier for a lot of this work" Dean said.

Google is working on an AI project that detects sign language.

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UoB uses machine learning and drone technology in wildlife conservation – Education Technology

The university's new innovations could transform wildlife conservation projects around the globe

The University of Bristol (UoB) has partnered with Bristol Zoological Society (BZS) to develop a trailblazing approach to wildlife conservation, harnessing the power of machine learning and drone technology to transform wildlife conservation around the world.

Backed by the Cabot Institute for the Environment, BZS and EPSRCs CASCADE grant, a team of researchers travelled to Cameroon in December last year to test a number of drones, sensor technologies and deployment techniques to monitor the critically endangered Kordofan giraffe populations in Bnou National Park.

There has been significant and drastic decline recently of larger mammals in the park and it is vital that accurate measurements of populations can be established to guide our conservation actions, said Dr Grinne McCabe, head of field conservation and science at BZS.

In related news: Sustainable Livestock Systems Collaborative seeks to solve the food crisis through tech

Bnou National Park is very difficult to patrol on foot and large parts are virtually inaccessible, presenting a huge challenge for wildlife monitoring. Whats more, the giraffe are very well camouflaged and often found in small, transient groups, said Dr Caspian Johnson, conservation science lecturer at BZS.

Striving to uncover the best method for airborne wildlife monitoring, BZS reached out to Dr Matt Watson from the UoBs School of Earth Sciences, and Dr Tom Richardson from the Universitys Aerospace Department, as well as a member of the Bristol Robotics Laboratory (BRL). The team forged successful collaborations using drones to monitor and measure volcanic emissions to create a system for wildlife monitoring.

A machine learning based system that we develop for the Kordofan giraffe will be applicable to a range of large mammals. Combine that with low-cost aircraft systems capable of automated deployment without the need for large open spaces to launch and land, and we will be able to make a real difference to conservation projects worldwide, said Dr Watson.

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AI Is Coming to a Grocery Store Near You – Built In

For the consumer packaged goods industry CPG for short the Super Bowl presents both an opportunity and a challenge. The National Retail Federation estimates that almost 194 million Americans will watch Super Bowl LIV.The report claims that each one will spend an average of $88.65 on food, drinks, merchandise and party supplies. Really.

To secure valuable shopping cart space, big food and beverage brands like PepsiCo, Anheuser-Busch InBev and Tyson Foods pull out all the stops, offering promotions on soda, beer and hot dogs designed to be so tempting that they stop consumers in their tracks. Once a promos set, brands need to ensure they have the right amount of product in the right places. Its a process known as demand forecasting, where historical sales data helps estimate consumer demand.Getting that forecast right can make or break the success of a campaign.

Demand forecasts play an important role in a CPG brands day-to-day operations, but they take on a special significance during events like the Super Bowl, where billions of dollars are a stake. If a forecast underestimates demand, brands cede sales to competitors with readily available products. Companies that overestimate demand run the risk of overstocking the wrong store shelves or watching inventory expire in distribution centers.

Increasingly, the brands that come out on top are victorious because of technology. At Kraft Heinz, for example, machine learning models do much of the heavy lifting to generate accurate demand forecasts for major events like the Super Bowl.

What you got probably five, seven years ago were a lot of the consulting firms pitching you on what AI can do, said Brian Pivar, senior director of data and analytics at Kraft Heinz. Now, youre seeing companies build these things out internally they see the value.

For the worlds biggest food and beverage brands, growth means mergers and acquisitions, with big brands often buying smaller competitors that have cornered the market on emerging trends. Acquiring a startup food brand isnt easy, but its much less complex than merging two multinationals that manage critical sales, supply chain and manufacturing processes using customized software platforms.

Thats the world Pivar stepped into when he arrived at Kraft Heinz in late 2018, three years after the merger of Kraft Foods and H.J. Heinz created the worlds fifth-largest CPG brand. In the years since, the company has doubled down on artificial intelligence technologies, including machine learning. But Kraft Heinz, like many other companies in its space, is still playing catch up.

Theres a lot of opportunity to leverage AI to help us make better and smarter decisions.

Even when companies like Kraft Heinz want to move full-steam ahead and incorporate the latest tech into their operations, they still face challenges. Chief among them is the ability to implement technical builds successfully.

CPG companies dont always have strong data foundations, said Pivar. So what you see sometimes is a data scientist spending most of their time getting and properly structuring data. Lets say 80 percent of their time is spent doing that and 20 percent is spent building ML or AI tools instead of the reverse, which is what you want to see.

When Pivar came to Kraft Heinz, his first order of business was to develop a five-year strategy that gave leadership visibility into both his teams goals and their roadmap. Instead of hiring a crew of data scientists right off the bat, Pivar instead brought on data engineers to ensure that his team had the necessary foundation to build advanced analytics. The company also spent four months evaluating and testing cloud partners to find the perfect fit.

In the two years since Pivar joined Kraft Heinz, his team has built machine learning models to generate more accurate demand forecasts around major events with distinctive promotions, like the Super Bowl. Prior to Pivars arrival, these forecasts were generated manually in spreadsheets like Excel.

Machine learning can do amazing things.Read more about the latest ML technology.

His team also built atool that relies on recent sales data and inventory numbers at stores and distribution centers to predict when supermarkets will need to be resupplied and with what products, along with insights about the cause of low stock.

Were looking across the business, from sales to our operations and supply chain teams, said Pivar. Within all of those spaces, theres a lot of opportunity to leverage AI to help us make better and smarter decisions.

Kraft Heinz isnt the only big player in the CPG space thats incorporating AI across its business. Frito-Lay, a PepsiCo subsidiary, is working on a project that uses computer vision and a custom algorithm to optimize the potato-peeling process.Beer giant AB InBev uses machine learning to ensure compliance and fight fraud.And Tyson Foods is considering the viability of using AI-powered drones to monitor animal health and safety.

Even grocery stores are getting in on the action. Walmart has built a 50,000-square-foot store in Levittown, New York, filled with artificial intelligence technology.

Walmarts Intelligent Research Lab, or IRL, is both a technology testbed and a fully functioning store covering 50,000 square feet.The store is filled with sensors and cameras and can automatically alert store associates when a product is out of stock, shopping carts need collecting or more registers are necessary to quell long lines.Theres enough cable in IRL to scale Mt. Everest five times, and the store has enough computing power to download 27,000 hours of music per second.

CPG brands are still figuring out how best to leverage artificial intelligence, which means that, at least in the short term, the shopping experience might not change drastically. But that doesnt mean consumers wont be driving change at least, according to Shastri Mahadeo, founder and CEO of Unioncrate.

Unioncrate is a New York-based startup whose AI-powered supply chain-planning platform generates demand forecasts based on consumer activity and the factors that impact purchasing decisions. For Mahadeo, AI has the potential to both save brands money and reduce waste by aligning production decisions with consumer demand.

If a brand can accurately predict what a retailer is going to order based on what consumers are going to buy, then theyll produce whats needed so they dont have money tied up in working capital, said Mahadeo. Similarly, if a retailer can accurately predict what consumers will buy, then they can stock accordingly.

In addition to streamlining back-end processes related to manufacturing and supply chain management, Pivar said that within 10 years, AI could be used to create a more personalized shopping experience, one where brands customize promotions to consumers with the same focus seen on platforms like Instagram or Facebook.

What does that mean to CPG? asked Pivar. Were still figuring that out, but thats where I see things going.

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Blue Prism Adds Conversational AI, Automated Machine Learning and Integration with Citrix to its Digital Workforce – what’s up

LONDON andAUSTIN,Texas, Jan. 29, 2020 /PRNewswire/ --Looking to empower enterprises with the latest and most innovative intelligent automation solutions, Blue Prism(AIM: PRSM) today announced theaddition of DataRobot, ServisBOTandUltimato its Technology Alliance Program (TAP) as affiliate partners. These partners extend Blue Prism's reach by making their software accessible to customers viaBlue Prism's Digital Exchange (DX), an intelligent automation "app store" and online community.

Blue Prism's DX is unique in that, every week new intelligent automation capabilities get added to the forum which has resulted intens of thousands of assets being downloaded, making it the ideal online community foraugmenting and extending traditional RPA deployments. The latest capabilities on the DX includedealing with conversational AI (working with chatbots), adding automated machine learning as well as new integrations with Citrix. With just a few clicks users can drag and drop these new capabilities into Blue Prism's Digital Workforceno coding required.

"Blue Prism's vision of providing a Digital Workforce for Every Enterprise is extended with our DX community, which continues to push the boundaries of intelligent automation," says Linda Dotts, SVP Global Partner Strategy and Programs for Blue Prism. "Our DX ecosystem is the catalyst and cornerstone for driving broader innovations with our Digital Workforce. It provides everyone with an a la carte menu of automation options that are drag and drop easy to use."

Below is a quick summary of the new capabilities being brought to market by these TAP affiliate partners:

DataRobot: The integration of DataRobot with Blue Prism provides enterprises with the intelligent automation needed to transform business processes at scale. By combining RPA with AI, the integration automates data-driven predictions and decisions to improve the customer experience, as well as process efficiencies and accuracy. The resulting business process improvements help move the bottom line for businesses by removing repetitive, replicable, and routine tasks for knowledge workers so they can focus on more strategic work.

"The powerful combination of RPA with AI what we call intelligent process automation unlocks tremendous value for enterprises who are looking to operationalize AI projects and solve real business problems," says Michael Setticasi, VP of Alliances at DataRobot. "Our partnership with Blue Prism will extend our ability to deliver intelligent process automation to more customers and drive additional value to global enterprises."

ServisBOT:ServisBOT offers the integration of an insurance-focused chatbot solution to Blue Prism's Robotic Process Automation (RPA), enabling customers to file an insurance claim with their provider using the convenience and 24/7 availability of a chatbot. This integration with ServisBOT's natural language technology adds a claims chatbot skill to the Blue Prism platform, helping insurance companies increase efficiencies and reduce costs across the complete claims management journey and within a Blue Prism defined workflow.

"Together we are providing greater efficiencies in managing insurance claims through chatbots combined with AI-powered automation," says Cathal McGloin, CEO of ServisBOT. "This drives down operational costs while elevating a positive customer experience through faster claims resolution times and reduced friction across all customer interactions."

Ultima: The integration of Ultima IA-Connect with Blue Prism enables fast, secure automation of business processes over Citrix Cloud and Citrix virtual apps and desktops sessions (formerly known as XenApp and XenDesktop). The new IA-Connect tool allows users to automate processes across Citrix ICA or Microsoft RDP virtual channels, without needing to resort to screen scraping or surface automation.

"We know customers who decided not to automate because they were nervous about using cloud-based RPA or because running automations over Citrix was simply too painful," says Scott Dodds, CEO of Ultima. "We've addressed these concerns, with IA-Connect now available on the DX. It gives users the ability to automate their business processes faster while helping reduce overall maintenance and support costs."

Joining the TAP is easier than ever with a new self-serve function on the Digital Exchange itself. To find out more please visit:https://digitalexchange.blueprism.com/site/global/partner/index.gsp

About Blue PrismBlue Prism's vision is to provide a Digital Workforce for Every Enterprise. The company's purpose is to unleash the collaborative potential of humans, operating in harmony with a Digital Workforce, so every enterprise can exceed their business goals and drive meaningful growth, with unmatched speed and agility.

Fortune 500 and public-sector organizations, among customers across 70 commercial sectors, trust Blue Prism's enterprise-grade connected-RPA platform, which has users in more than 170 countries. By strategically applying intelligent automation, these organizations are creating new opportunities and services, while unlocking massive efficiencies that return millions of hours of work back into their business.

Available on-premises, in the cloud, hybrid, or as an integrated SaaS solution, Blue Prism's Digital Workforce automates ever more complex, end-to-end processes that drive a true digital transformation, collaboratively, at scale and across the entire enterprise.

Visit http://www.blueprism.com to learn more or follow Blue Prism on Twitter @blue_prism and on LinkedIn.

2020 Blue Prism Limited. "Blue Prism", "Thoughtonomy", the "Blue Prism" logo and Prism device are either trademarks or registered trademarks of Blue Prism Limited and its affiliates. All Rights Reserved.

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