Red Hat: Leveraging AI and Data to Combat Cybersecurity Risk

“AI is already here, we've been using it for years,” says Monica Sasso. “Anytime you want to do anything real time, you have to use it."
We spoke with Red Hat’s Monica Sasso and Richard Harmon at MoneyLIVE Summit 2024 about how synthetic data can be used to improve cybersecurity in finance

During a climate of artificial intelligence (AI) leading to increased cyberattacks, businesses are intent on finding new and innovative ways to protect their data.

Red Hat is one such company that proposes the use of synthetic data to train AI models for cybersecurity risk management. In attendance at MoneyLIVE Summit 2024, we spoke with Red Hat’s Global Financial Services (FSI) Digital Transformation Lead, Monica Sasso, and VP & Global Head of Financial Services Richard Harmon, about these ideas.

Richard Harmon (left) and Monica Sasso (right), Red Hat

Help, don’t hinder: Using AI to tackle financial fraud

Moving forward, Monica Sasso believes that the focus of AI will be to speed up processes and improve accuracy.

“AI is already here, we've been using it for years,” says Sasso. “Anytime you want to do anything real-time, you have to use it. When you start to think about some of these efficiencies and put an operational resilience lens on it and integrate AI with some threat hunting software for example, you can begin to automate some of your cybersecurity activities.”

She says: “That's really where we're working with some of our customers on how they can start to use some of these newer tools. It is so critical because these things keep the lights on.”

Richard Harmon adds that there is also great potential for simulation, via the use of AI-enabled agents, but that it could also lead to greater cybersecurity risks.

“The regulators are very concerned about individual banks around say, concentration risk in the cloud or other kinds of concentration risk or correlation risk, but ultimately their biggest concern is the overall systemic risk,” he says.

“However, that's where my optimism is for analytics. AI machine learning data science could lead to the analytics starting to help and potentially make the system safer from criminal activity and manipulation.”

Harmon adds: “There is a societal challenge in terms of how much we invest in protecting ourselves. This is where the EU AI Act comes in, or even the upcoming Data Act. I think that's the properly balanced approach. Otherwise, you can de-risk everything and you get no innovation.”

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Capitalising on fast-moving technology

With technology continuing to move at a rapid pace, particularly within financial sectors, Sasso highlights that it could lead to decisions about AI being made without understanding what the integrations actually are.

Indeed, although businesses are keen to invest in AI, often they do not have a clear strategy in place or fully know their use cases for the technology.

“I always advise people to say, every decision you make today is going to create your tech debt of tomorrow,” Sasso says. “It’s about understanding what the drivers are, what the risk appetite is and why you want to do it.”

Likewise, Harmon highlights that AI is a huge focus for Red Hat. “We're this infrastructure layer of enabling customers to have all of their various AI applications running in a very consistent, simplified, secure, resilient manner," he notes.

“It's more about how you manage and simplify access for modellers. Every bank is modernising and monitoring its real-time payments system. The risk systems are real time, but it's also getting much more complex.”

Harnessing hybrid cloud and automation can allow banks to have a choice. “[Red Hat] is focusing on automation and how we can bring AI into some of our automation processes," Sasso says. 

Harmon agrees, saying that growing synthetic data will make things simpler and more transparent for the financial sector.

He adds: “AI is going to generate synthetic data that doesn't exist. In areas like financial crime, you can generate new types of crime that criminals haven't found, or more importantly, you create different ways of committing a certain type of crime.

“When you have the synthetic data, you can share data, but can't identify each other's customers. But most importantly, you can share data about fraud and risk.

“That's the future as a key driver. A middle ground.”


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