Data security in financial services

By Puneet Mody VP & US Regional Head, Commercial Banking & Finance, Infosys

Financial institutions are under mounting pressure to modernise their infrastructure and services for AI. This transformation is no longer optional; it’s a survival imperative in an increasingly competitive market

Financial institutions face an unprecedented challenge: embracing AI innovation while maintaining robust security and regulatory compliance. Customers expect seamless digital experiences, hyper-personalised services, and real-time solutions – expectations largely shaped by their interactions with FinTechs and digital-native platforms.

However, evolution comes with its own set of complexities. The effectiveness and precision of AI systems depend significantly on the quality and relevance of the data they are trained on. Achieving optimal outcomes requires high-quality, reliable data. Financial services organisations must navigate extensive volumes of unstructured, complex, and disorganised data, including machine data, documents, and user-generated content. Banks must modernise while maintaining the security and integrity of vast amounts of sensitive customer data.

The challenge is magnified by the fact that financial institutions are among the most heavily regulated entities, subject to stringent compliance requirements that vary across jurisdictions. Fundamentally, data security for AI readiness includes two dimensions: strategic and tactical. The strategic dimension focuses on governance aspects such as long-term alignment with responsible AI, data security and organisational goals. The tactical dimension includes process and technical guardrails for safe development, deployment and use. Together, these form a comprehensive framework for responsible data and AI in enterprises.

Strategic

Effective data and AI governance in enterprises requires a structured framework built on clear leadership commitment and cross-functional oversight. It should involve a dedicated team that aligns ethics with business goals, avoiding conflicts of interest. This team acts as the central AI governance authority, ensuring collaboration, policy enforcement, guardrail construction, partnerships and collaboration, and ethical oversight. It must establish specific policies for AI development and deployment, incorporating guidelines for data quality, model validation, and risk assessment. A systematic approval and assessment process should be implemented, requiring thorough documentation and regular audits of AI systems for bias and performance. The framework should clearly define roles and responsibilities, implement monitoring mechanisms, and ensure compliance with regulatory requirements through regular reviews.

Essential components include staff training, incident response protocols, and detailed documentation of AI systems including their limitations and potential risks.

Tactical

Data security and privacy in Responsible AI involve implementing comprehensive processes to safeguard sensitive information and ensure ethical data handling. Organisations must enforce strict access controls, encryption, and data masking to protect against unauthorised access and breaches. Privacy-by-design principles should be embedded into systems, ensuring compliance with global regulations like GDPR and CCPA. Additionally, deploying privacy-enhancing technologies, such as differential privacy and federated learning, helps maintain data confidentiality while enabling secure AI innovation. A centralised monitoring involves establishing systems to oversee all AI projects, monitoring model health, performance, and the nature of data being used. This system acts as an early warning mechanism to address issues promptly. Continuous red teaming and adversarial testing is essential to uncover vulnerabilities in models and use cases.

The future of financial services will be increasingly shaped by AI and digital innovation. Success will depend on institutions’ ability to embrace these changes while maintaining the highest standards of security and compliance. This requires ongoing monitoring of regulatory changes and technology developments, coupled with regular updates to security and compliance frameworks.