Bolstering Security in Banks and Financial Institutions with FinTech Solutions

FinTech solutions are mostly oriented towards improving efficiency, effectiveness or data quality. However, there are other roles they can play. With good platforms and apps, you can also bolster your cybersecurity. How? Read this article to find out!

Table of Contents

• Bolstering Cybersecurity with Threat-Resistant App Systems
• Bolstering the Security of Large Language Models (LLMs)
  • LLMs for Cybersecurity
  • LLMs’ Cybersecurity
• The Takeaway

Bolstering Cybersecurity with Threat-Resistant App Systems

Each application in your environment is a gateway for potential intruders. This is why bolstering their security should be a priority. How do you ensure that the apps are safe?

First, you need to start with the hardware. If you are using local technology, you need to protect it from being penetrated from the inside. Here, the most common threat is phishing, and the best way to protect against it is to educate your team.

Another option is to opt for cloud services. In this scenario, you need to research the databases your system will be using. Ideally, they should adhere to the strictest security standards, and your data should be stored in a unique database (meaning that no other business uses the same one, as this can cause a chain reaction if a leak occurs).

The second aspect that you need to consider is data encryption. Hackers mainly exploit the so-called encryption gaps – elements in your system that are not encrypted. You need to eliminate them to prevent information leaks, even if they occur in those parts of the system that seem unlikely to be exploited.

Naturally, these are just the tip of the iceberg when it comes to bolstering security in banks and financial organisations. In reality, you still need to think about:

• regulatory compliance regarding FinTech, data management, and relevant technologies,
• multi-factor authentication methods that increase the security on the user’s side,
• alerts and automated reaction mechanisms,
• regular updates against emerging threats,
• pen-testing.

Bolstering the Security of Large Language Models (LLMs)

What about LLMs, machine learning and AI? Can they bolster your cybersecurity, and how do you secure them? Let’s answer these questions one by one.

LLMs for Cybersecurity

Using AI-powered LLMs enables you to increase your cybersecurity based on previous threats, both to your systems and to others. You must collect threat intelligence and historical data on cyberattacks and feed it to your system. As a result, you get an intelligence reaction model that will alert your cybersecurity team whenever it detects any suspicious activity.

The main advantage of this approach is that it provides better insight into potential threats. AI can analyse patterns that humans wouldn’t see, making it much more accurate and effective for threat detection.

LLMs’ Cybersecurity

How do you protect your LLMs? The actions depend on the type of threats you may face, with the most common being:

• Prompt injection:
  • privilege control,
  • input validation,
  • trust management.
• Denial of Service (DoS) attacks:
  • rate-limiting,
  • resource limits,
  • action queues.
• Data poisoning:
  • verification and validation of data sources,
  • sandboxing,
  • diverse datasets.

The Takeaway

As you can see, bolstering security in digital banks requires a lot of effort. What is more, you likely won’t find anybody willing to share their approach. This is because you should never explain the technology and solutions you use for your cybersecurity since disclosing such information could provide potential hackers with the information they need to breach your system. Nevertheless, if you are looking for secure apps and LLMs for banks and financial institutions, contact us at Ailleron – we will design and develop them for you!

You might also read: When is cloud migration beneficial and how to dodge potential pitfalls?

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