AiThority Interview with Paul Lipman, CEO at BullGuard

Could you tell us about your fascinating journey into tech space? How did you start at BullGuard?

I’ve been in technology for over 25 years and in security for over a decade. BullGuard has been in business since 2002, and I joined as CEO in 2016.

What is BullGuard all about?

BullGuard is a Cybersecurity company focused on the consumer and SMB markets. We’re focused on delivering exceptional security for our customers, coupled with great ease-of-use and service. This is pretty much summed up in our tagline “We keep you safe and we keep it simple.”

What kind of Consumer Security technologies and solutions are we talking about?

Everything to keep your devices and data secure. This includes endpoint anti-malware, advanced intrusion detection and prevention system, application filtering, a secure browser, vulnerability scanner, VPN and much else besides.

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Could you tell us about the current benchmarks in Big Data, AI ML and Analytics at BullGuard and how you train your Product team in managing these rather high-ended expectations?

The ultimate benchmark for us is testing the efficacy of our anti-malware technologies against raw new threats that have never been seen before (the industry term is “zero day” threats). We do this through real-time telemetry of what our customers are seeing on their devices, our automated research systems that collect millions of real-world threats, and through participation with independent threat testing agencies. We are constantly monitoring the evolution of the threat landscape, and ensuring that we are staying one (or two!) steps ahead. At the end of the day, what matters to our customers is staying safe online, and this is a highly motivating objective for our team.

What is the engine driving BullGuard’s extensive AI ML capabilities?

The engine is pure necessity. The sheer volume and complexity of modern threats can only be addressed through ML techniques.

Which markets and customer profiles are you currently selling this to, and who has been the response?

We sell to consumers and small businesses. The response continues to be extremely positive. In fact, small business is a recent market for us – and it’s a market that has been historically underserved by the industry. We are making it incredibly easy for small businesses to use our technology and this has been very well received.

We have found supervised Deep Learning models (developed on the Keras framework) to be most effective in terms of feature engineering for high precision and recall identification of malicious files. There are a few specific challenges that must be considered in the cybersecurity space. Firstly, our production models must quickly and automatically classify files and assign a score that is combined with other techniques to make a determination within milliseconds – so accuracy and speed are both important. Secondly, protection against continually evolving nature of cyber threats requires a high degree of heterogeneity in training data – which is best achieved by favoring highly current real-world data inputs (as opposed to simply rely on corpus data). Lastly, vendors must be cognizant of the risks of the ML model itself being attacked by adversaries that are seeking to bypass protections.

What are your future predictions for Digital Transformation based on AI, Data Science and IoT? What  is the practicality of these applications in Consumer Security markets compared to what we see or use today?

One of the most interesting threats on the long-term horizon is the implication of Quantum Computing for cybersecurity. That isn’t something that will have practical commercial implications near-term, but it’s coming faster than is commonly understood and the ramifications will be potentially dramatic. Quantum Computing has the potential to enable massive advances in neural network complexity and speed – and will open up opportunities to apply AI in new ways to previously intractable problems. The practicality of applications to consumer security in the near-term is limited, however this is likely to a binary step-change – most notably in cryptography, where the industry will adopt “post-quantum” crypto algorithms before quantum computing becomes mainstream.

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What is your future roadmap for the companies that are yet to build their own Data Science/ AI teams?

Before planning a roadmap, first ensure that you are crystal clear about ‘why’ you are intending to build a Data Science/AI team. Is this really your core competence, and will it give your company a strategic market advantage? Do you need to build your own, or can you leverage services that deliver the capabilities that you need? Think hard and strategically about the cost, time-to-market, core competence and strategic advantage questions.

What start-ups are you keenly following?

Q-CTRL, Robinhood and Zoox.

One technology that will be outdated by 2025 

Facebook.

Something you do better than others – the secret of your success?

Prioritization. Easy to say, far harder to do!

Tag the one person in the industry whose answers to these questions you would love to read

Elon Musk.

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Thank you, Paul! That was fun and hope to see you back on AiThority soon.