Disruptive & Emerging Technologies

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AI in cybersecurity—what's your perception? Benchmark your answers against peers.

Top Answer : It's not a secret that engaging with startups and emerging technologies is crucial for every business. It's no longer about being good enough, it's about being the best and taking advantage of new products and innovations comes with the territory. My primary motivation to engage with startups is to deliver business value at Flex. My secondary motivation is to learn from people smarter than me (the entrepreneurs I meet) about the latest technologies, products, and solutions being created to shape the future of enterprises.

Our business priorities are very clear and we work as a team to figure out which technologies (and then companies) will help us drive our goals. This top-down approach is methodical - we have a budget and a structured approach on how to work with vendors (big or small) to select the right one. The bottom-up approach is equally important, and requires a team that is passionate about technology to make it work. One example for us is that, as a team, we spend a few hours every Friday morning learning about new technologies, products, and innovations that may be helpful for us. We then come together as a team and share our learnings. If one or more products stand out, we dig deeper and explore how we can leverage it at Flex.

Regardless of top-down or bottom-up, seeing a meaningful ROI is generally important to us. That doesn't always hold true, but these are rare exceptions, not the rule.
Startups we engage with have a few things in common:

• We get to know the team before we get to know the product because we're betting on the people and the vision as much as the product. How strong is their technology, sales, and executive team?
• Is the product easy to use and does it have an adoption strategy in place?
• And finally, in return for us coming in as an early customer, are they willing to have us shape their product and roadmap to fit our needs?

With thousands of startups, it can be daunting to figure out the right ones to engage with, but a few channels have been helpful to curate the best — my peer network (we exchange emails on a weekly basis), curated online IT communities that go beyond what traditional analysts provide, and working with select VCs like a16z, Lightspeed, and others.

Top Answer : NFT is art. Just like physical art is valued (for some), so is digital. Just like physical art can be copied using 3D scanners and 3D printers (to account for art texture / brush strokes), digital art can be right-clicked and saved but that doesn't make it 'the original'. Blockchain has enabled original digital art to be preserved and valued in a meaningful way. This will be accelerated further with Facebook and others investing deeply in VR where users will be able to visit the "original, digital Mona Lisa" and the Louvre (or whoever bought it) can monetize it. Organizations creating content or other digital assets can and should start thinking about how to play a role as part of their digital transformation. What does it mean for a breaking news article to be minted and sold in a marketplace? Does the user who bid on the page share ad revenue for that page? What would it mean for there to be an NFT created for every collectible manufactured? Lots to explore and ideate here.

Top Answer : Already have implemented.

Top Answer : I think AI will provide tremendous value in terms of vulnerability management as we start looking at larger fluid enterprises—meaning cloud-based ones that are restocked on a regular basis and expanding and contracting constantly—that truly can derive value. And even if there wasn’t a skill shortage, there's not enough daily processing power in human bodies to get to the same level of granularity. So I'm a huge advocate for figuring out how to do this properly. And if it’s done properly, then I will use it anywhere I can get it.

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How are decision-makers using AI technologies in 2021?

Top Answer : The way it has been evolving the last couple of years, I think it’s moving more towards optimizing on the cost of computing, which is going from EC2 Instance into Kubernetes and this is eventually going to go to a serverless architecture which means you don’t really need machines running. You just somebody to code and the code does the trick for you.

Top Answer : I applaud the progress made but we have to keep learning. If the software that’s produced devalues your worth based on your identity, you would consider that a significant emotional event.  The challenge I have is that I often get that approach without an understanding of the guiding principles we're going to use: What are the do's and don'ts that we will use until we have those guiding principles and what will we do to create those checks and balances? I see a lot of eagerness to put out AI because it exists and not much understanding of the potential impacts or wanting to examine them afterwards. That used to be the model that many less mature organizations had around security—that’s changed, but we need to make sure that change exists with AI. Let's keep learning and failing fast but let's also understand what the principles are regarding use, as well as the checks and balances we'll put in place while we learn. I'm not hearing much discussion about that from the same companies that have been working on AI principles for the better part of a year, yet they already have projects in flight for utilizing AI in their products. We need to get in front of that principle-based usage, or we'll end up both slowing AI from a reactionary standpoint—from people who don't understand it—and causing problems from which we won’t be able to recover until people or organizations have been victimized. That concerns me.

Top Answer : It's fascinating when you look at how the AI space is evolving; there are a lot of positives. But the government is really talking about regulation on the ethical side, which scares the heck out of me. You don't want to tamper down the innovation, but it will take us as IT leaders to put some guardrails around it. It's that ethical side of AI as an industry that we have to watch out for. Explainable AI is a newly emerging field that's pushing people to rethink how they'll use the data at every step before they actually get the results. It's a new concept started at MIT that Microsoft and Google have been investing in heavily. The Defense Advanced Research Projects Agency (DARPA) even has an open-source challenge on explainable AI. Their goal is to make sure that any federal system that uses AI or ML understands exactly what’s involved: What is my input? What happens to my intermediate rates? What happens to the bias? How is the system tracking with the data points and what is it putting out?

Top Answer : Microsoft Office 365. Since migrating from on-prem infrastructure to SaaS, we have seen tremendous productivity gains!

Top Answer : What will be interesting is that we won't trust these things until we have the data to trust them. If Tesla had all the data to say that 87% of the time that we witnessed a driver be presented with a pedestrian to hit or a wall to run into, they chose to hit the pedestrian. Would you feel better about that or not? Because at some point, Tesla is going to have that data, because this circumstance will happen enough times. And it's not just Tesla. Across the industry, you'll have the data because you'll know how people have reacted. You'll understand what human behavior has been, but I don't know if that'll make me feel better or not. I think that's going to be interesting. I definitely will look forward to the day that we can do that.

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Are tech leaders adopting workflow automation?

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