Artificial Intelligence (AI) continues to reshape industries globally; the need for secure and reliable AI systems is becoming increasingly critical. Security failures or data mismanagement can have severe consequences, especially in financial services and regulatory compliance. As a result of the rise of AI, in November 2023, the National Cyber Security Centre (NCSC) published their Guidelines for secure AI system development. In this article, we’ll explore these guidelines, showing how secure design, development, deployment and maintenance practices result in the safe use of AI in RegTech.

Secure Design

The first principle encourages building AI systems securely from the outset. This means prioritising robust, ethical and well-protected models, addressing potential threats and vulnerabilities before they become problems. A secure design must consider ethical implications, removing biases and ensuring models are fair and impartial.

Models must be resistant to manipulation and be able to handle sensitive data appropriately. Designers must consider how their AI will interact with different environments. This involves anticipating how vulnerabilities might be exploited. Developers must be able to explain the decisions made by the model in order to build trust and ensure that users understand the reasoning behind recommendations or actions.

Secure Development

Once a system has been designed, the next phase is development. The emphasis here is on high quality, unbiased data. AI systems will only be as good as the data they are trained on. Developers need to employ rigorous data validation techniques to ensure reliability, as poor data quality can lead to flawed decisions and significant compliance risks.

Transparency is also crucial during the development phase. Developers must have a comprehensive understanding of how their models work and be able to explain decisions. So called black-box models that lack transparency are problematic in regulated environments where decisions must be justifiable and verifiable.

Throughout development, frequent testing and validation are essential. Developers should place the emphasis on ‘real’ data, supplemented with accurate ‘pseudo’ data from initial testing to refined models. The data must reflect real-world conditions, as misrepresentative data can lead to models that perform poorly when exposed to live environments.

Secure Deployment

Deploying AI models securely is a critical step that demands careful planning and a thorough risk assessment. Before any system goes live, it must undergo comprehensive testing to ensure it functions as expected under strain. Stress testing determines how well the model handles unexpected or unusual data inputs.

Once deployed, AI systems need continuous monitoring. Unlike traditional software, AI can degrade over time or become less effective as data patterns change. Real-time monitoring is necessary to identify any performance issues or security vulnerabilities. Organisations must be prepared to update models rapidly in response to new threats or regulatory changes.

Having back up and contingency plans is crucial. AI models, no matter how well designed, can fail or produce unintended results. Therefore, it is crucial to have backup software solutions – perhaps a non-AI enabled solution. Backup systems ensure business continuity and that compliance obligations are met even if Artificial Intelligence system encounters issues.

Secure Operation and Maintenance

The final principle focuses on the long-term operation and maintenance of AI systems. The AI must remain effective and secure through routine security checks and performance evaluations to identify and address any emerging vulnerabilities. As new threats arise, AI models need to be updated or refined to maintain security.

Proactive issue resolution is another key component. Having a dedicated team responsible for monitoring the system and addressing problems swiftly must include preparation to replace or decommission outdated models while considering data security and the regulatory process.

Data security remains a top priority through the operational phase. Robust measures such as data encryption, access controls and regular audits must be in place to protect sensitive information. In the context of RegTech, where data breaches or unauthorised data use can lead to significant financial and reputational damage, maintaining a high level of data security is essential.

Conclusion

The NCSC guidelines provide a comprehensive framework for ensuring that AI models are built, deployed, and maintained with security and reliability in mind. By focusing on security, institutions can mitigate risks and harness the power of AI effectively. These principles are not just about compliance but are also crucial for building AI systems that users can trust. As AI continues to evolve, keeping security and ethical considerations at the forefront will be essential for the long-term success of AI in RegTech.