AI represents the new frontier in the banking, financial, and insurance (BFSI) sector, optimizing and enhancing process automation, fraud detection, risk assessment, wealth management, and product development. In fact, research reveals that financial sectors' spending in AI is projected to hit a whopping $126.4 billion mark by 2028, a substantial increase from $35 billion in 2023.
However, as AI becomes a core component of the industry’s infrastructure, AI security in finance is now an existential necessity.
The stark reality of AI is that it is vulnerable and exploitable in ways that traditional frameworks and controls aren’t equipped to handle. Therefore, financial institutions that adapt to the evolving cybersecurity needs would lead the future with stability, while those that delay risk becoming the next costly lesson in the BFSI industry.
What is AI Security in Financial Services?
AI security in finance is the discipline of protecting financial institutions from all manner of cybersecurity threats. This includes traditional attacks like ransomware, social engineering, DDoS, and supply chain attacks, as well as AI-specific threats such as LLM poisoning, prompt injection, model theft, and sensitive data exposure.
AI cybersecurity involves a broad spectrum of best-practice measures, strategic frameworks, and tools. All the efforts are aimed at ensuring the integrity, confidentiality, and availability of financial data.
BFSI includes all types of banking institutions, financial services, insurance entities, credit unions, and investment firms. All these entities deal with vast volumes of highly sensitive information. By adapting to a modern, data and AI security approach, financial institutions can not only prevent cyber attacks, but they can also ensure the safe adoption of AI.
Why AI Security is Critical in Finance
A fintech research reveals that an average organization loses $98.5 million every year, with 54% of these losses attributed to cybersecurity threats. Such insights are clear indicators of why AI security is indispensable for ensuring the safe and accelerated adoption of AI in the BSFI sector.
Preventing Sensitive Data Leakage or Exposure
Financial institutions handle sensitive data constantly, and if it falls into unauthorized hands, it could be exposed to unauthorized employees and vendors or, in the worst-case scenario, to cybercriminals.
Take, for instance, an AI fraud detection application that analyzes customer profiles and behavior to report anomalies or suspicious activities. If the application lacks the same access policies as Know Your Customer (KYC) systems and shares KYC information with an analyst, along with general metadata, sensitive information could be exposed to individuals who aren’t entitled to access such data.
AI security ensures that proper guardrails are placed across data and AI pipelines, such as sanitization, encryption, least privilege access controls, input/output filtering via LLM firewalls, etc.
Deploying Agentic AIs Safely
Agentic AI, or autonomous AI agents, is the future of LLM that doesn’t require human intervention for learning, reasoning, and planning. In fact, McKinsey reports that the technology is projected to unlock up to $4.4 trillion across 60 different use cases, including but not limited to supply chain optimization, compliance, and customer service, to name a few.
While the technology is significantly promising, offering great value, the risks it brings to the table are nothing to scoff at. Surveys reveal that 53% of organizations cite AI agents to access sensitive information, whereas a staggering 80% reported these agents to have gone rogue and shared sensitive data.
A robust AI security framework ensures that appropriate policies are in place to prevent unauthorized access by AI agents, risk management programs are revised to address unprecedented AI threats, and security controls are implemented across the AI lifecycle.
Fostering Customer Trust
No organization can survive without unshakable customer trust and confidence. However, customer trust is fragile and thus requires immediate and continuous attention. Trust becomes even more substantial in the financial industry. AI security and governance not only help organizations protect their data, systems, and networks against cybersecurity breaches but also ensure that the AI applications are reliable and trustworthy.
For instance, ROT data could lead an AI credit agent to confuse debt with income due to outdated financial records, resulting in an inflated profile. An applicant with an inflated profile would have an undeservedly high credit score, leading to a high-risk loan.
Ensuring Compliance with Financial Regulations & Frameworks
As AI gained momentum in finance and across other industries, it spurred regulatory bodies globally to formulate and implement guardrails for the technology's safe deployment and use across financial systems and services.
Hence, amendments have been made in the existing regulations and frameworks to govern AI development, deployment, and use. New, AI-specific regulations and frameworks have also been introduced, such as the EU AI Act and NIST AI Risk Management Framework.
These regulations require financial entities to implement appropriate security measures, conduct regular security audits, ensure transparency of how AI is used, and implement strict authentication processes for both users, LLMs, and AI agents. Non-compliance can mean heavy regulatory fines and penalties, deteriorated market reputation, and broken customer trust.
The Top Challenges in AI Security
There are a number of challenges that security teams face in protecting financial services, solutions, and systems in the BFSI industry. Let’s take a look at ones that top the list.
Data & AI Models Visibility Crisis
In cybersecurity, visibility is critical. Breaches often stem not from external threats but from the internal lack of understanding regarding data and AI models. This lack of data visibility is one of the primary drivers of a weak cybersecurity posture, a troubling problem compounded by the widespread adoption of multi-cloud environments. To put things into perspective, 82% of cybersecurity professionals report gaps in finding and classifying data. Similarly, 75% of security experts assert that Shadow AI" will soon eclipse the complexity and threat level once associated with "Shadow IT.
Data & Model Integrity Risks
Financial services are integrating LLMs into their operations to enhance efficiency. Since use cases like fraud detection, credit decisioning, underwriting, and risk assessment are highly sensitive, they require high-quality and reliable data to train the models. However, adversarial attacks like data poisoning or model theft can contort model outputs and, consequently, lead to operational risks.
Limited Transparency & Explainability
In the BFSI sector, it is crucial to justify the rationale behind a decision made against a particular action. Consider the insurance claim service: an AI agent might automatically deny a customer's house damage claim based, for example, on image analysis, even though the final decision depends on a multitude of other factors, such as time, location, etc. Unless the insurer provides an understandable rationale for the decision, things could lead to litigation and fines. After all, data and AI regulations require AI systems to be transparent, fair, and accountable.
Manual, Error-Prone Compliance Processes
Many organizations, such as those in the financial industry, still depend on manual compliance processes, which are not only slow and laborious but also prone to human error. However, as AI in finance scales across different departments and functions like fraud detection, risk assessments, and credit scoring, manual audit and reporting fail to keep up with the increasing complexity of modern AI systems. This process may result in blind spots in AI security and governance, leading to inconsistent policies, delayed risk detection, and inaccurate compliance reporting.
Best Practices for AI Security in Financial Services
There’s no one-size-fits-all strategy when it comes to data and AI security, regardless of the industry. However, there are certain best practices or approaches that can certainly help organizations build a resilient and compliant AI security ecosystem.
- Every cybersecurity begins with gaining a comprehensive understanding of data. Discover and classify all the data, such as PII/NPI, PAN/PCI, or KYC/AML, across your structured and unstructured datasets in data lakes, multicloud, and SaaS environments.
- Identify risky exposure to sensitive data, which is often caused by misconfigurations, oversharing, excessive privilege, etc.
- Since more and more organizations are now using multiple AI models across their environment, it is imperative to gain complete visibility of all those models. Hence, detect and track all the AI/ML models, including deployment location, owners, their versions, and the training datasets. Widen the discovery net by including embedded AI components, vendor-provided models, or models hosted on multi-clouds.
- A complete overview of all your data and AI models can help significantly in establishing optimal policies and controls to protect data and AI interactions. For instance, security teams can create data masking policies based on roles or entities, limit the access control of both user identities, workloads, and AI models, or deploy LLM firewalls to filter input and outputs based on data context.
- Data theft, misuse, or exploitation is often caused by excessive privilege. Security teams must have a clear picture of users, roles, and models accessing sensitive financial data. Furthermore, teams must carefully monitor access patterns and behavior to flag overprovisioned users or models and stale accounts to right-size access. Access insights can also help teams to enforce policy-based controls to implement a zero-trust or least privilege access model.
- Leverage regulatory intelligence, common tests and controls, and automated reporting to streamline compliance.
- Reduce attack surface and optimize storage cost by identifying redundant, obsolete, and trivial (ROT) data. Automate archival or deletion of redundant PII/NPI, PCI, or KYC/AML datasets.
Securiti: Powering Safe Data & AI Innovation in Finance
Securiti helps organizations in the BFSI sector adopt AI and innovate with confidence, by scaling AI securely, reducing risk, and meeting compliance. Enable smarter decisions, open new markets, and cut operational costs through automated DataAI security and compliance.
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