How can RegTechs improve the overall security of in-house AI?
The vast and breakneck expansion of in-house AI solutions is leading to many companies scrambling to ensure such solutions are complaint and strongly secure. What role can RegTech play in this challenge?
According to Joseph Ibitola, growth manager at Flagright, as AI becomes more entrenched in financial institutions – especially in-house AI solutions tailored to specific business needs – the security of such systems is increasingly coming under scrutiny.
He said, “While AI brings incredible benefits, like advanced data processing and predictive analytics, it also opens up new attack vectors that can be exploited by bad actors. And that’s where RegTechs have a pivotal role to play.”
A key role RegTechs have to play in this respect, Ibitola believes, starts with data security. “AI systems are only as strong as the data they’re built on. RegTechs can help by providing encryption and secure data storage solutions that protect sensitive financial information from breaches or tampering. By integrating with in-house AI systems, RegTech platforms can monitor data flows, ensuring that sensitive data is handled according to the highest standards of security.”
RegTechs are also able to bolster AI security through continuous monitoring and anomaly detection, Ibitola underlines. In his view, in-house AI systems are dynamic in that they learn and evolve over time.
“But this also means they can be vulnerable to adversarial attacks, where hackers subtly manipulate data inputs to deceive the AI system”, said Ibitola. “RegTech solutions that use real-time monitoring can help identify these anomalies before they cause any real damage, ensuring that the AI’s decision-making process remains sound and trustworthy.”
There is also the issue of regulatory compliance. In the view of Ibitola, as more regulators focus on the ethical use of AI – particularly around issues of transparency and fairness – RegTechs can play a crucial role in ensuring that AI systems comply with these evolving regulations. “They can help document the decision-making processes of AI models, making them more transparent and auditable, which is crucial when dealing with regulators,” he said.
He continued, “Additionally, as AI-driven decision-making becomes more prevalent, regulatory scrutiny is increasing. Flagright helps firms navigate this emerging landscape by providing transparent, auditable processes for their AI systems. This ensures that financial institutions not only comply with regulations but also maintain trust with their clients and stakeholders.
“In short, while AI holds immense potential, the security concerns are very real. With Flagright’s RegTech solutions, financial firms can mitigate these risks, allowing them to confidently leverage AI without leaving themselves exposed to threats,” Ibitola remarked.
A crucial role
In the opinion of Emil Leach Kongelys, CTO at Muinmos, as regulations around AI use are established, RegTechs play a crucial role in supporting both regulators and AI users to ensure a sustainable ruleset is defined.
He detailed, “Developing in-house AI comes with significant responsibilities, including proper model training and ethical application. Another important aspect is to ensure that the datasets used to training the models are properly secured, and both data security measures and threat detection is required.
“RegTechs are already assisting financial institutions in protecting against malicious AI use, such as deepfakes, which are now sophisticated enough to evade human detection. This protection extends beyond financial firms, as there have been instances of industrial spies impersonating employees,” he added.
Meanwhile, 4CRisk.ai stated that financial firms should look to integrate with best-of-the-breed foundational small language models that are purposefully built to solve regulatory, risk, and compliance problems and with trustworthy AI principles.
“These models must be private to the financial firm, have zero data bias, be trained ethically, and more effective than general-purpose language models,” the firm concluded.
