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  • AI offers substantial benefits for financial institutions, from enhancing experiences to optimizing operations.
  • Banks can achieve a 2-5X increase in interactions using AI tools.
  • Despite its advantages, AI introduces risks like bias, ethics concerns, and customer mistrust.
  • CISOs play a pivotal role in establishing ethical and legal AI use within financial services.
  • Governance structures need alignment with AI principles and adaptable frameworks.
  • Polymer DLP offers a data-centric security solution for responsible AI adoption, protecting privacy and compliance.

For financial institutions, AI’s diverse skill sets hold the potential to yield significant benefits. From enriching employee and customer experiences to optimizing backend operations, this flourishing technology promises to boost efficiency, accuracy and much more.

In fact, according to one study, AI-driven tools can empower banks to achieve a 2-5X increase in interaction and transaction volumes while maintaining the same staffing levels. 

On top of that, while some industries might scramble to collate the data needed to harness the power of AI, banks hold a distinct advantage. Afterall, AI thrives on data, and financial institutions inherently possess ample reserves of it.

However, as much as AI holds the promise of transforming facets such as customer service and fraud detection, it’s essential to acknowledge that this technology presents both potential risks and rewards–especially so in the realms of data privacy, ethics and cybersecurity. 

In this context, the Chief Information Security Officer (CISO) is emerging as a pivotal figure entrusted with the responsibility of establishing strategic boundaries for the adoption and proliferation of AI.

The evolving role of the CISO is financial services

In tomorrow’s financial services industry, CISOs will hold much more than just the role of security professionals. Soon, they will function as senior business executives with a distinct focus on security, risk management, and resilience. 

Much like other members of the board, they will proactively contribute to the overall growth and development of the organization. Far beyond being blockers of change, they will be looked upon as innovators–responsible for leading AI innovation while balancing all important risks. 

Striking the right balance between security and operational ease is not easy, especially as their role will adapt so quickly. To help their companies gain the competitive advantage promised by AI, CISOs must gain a deep understanding of the risks poised by AI, and chart the course to manage them.

Of course, to achieve this, understanding is key. With that in mind, here is an overview of the AI-related risks CISOs of financial services firms must know about. 

Need to know: The risks of AI in financial services 

The adoption of AI comes with inherent risks because of their relative immaturity and limited exposure to real-world scenarios–issues that have been compounded by the technology’s fast-paced revolution. 

While adopting AI is undoubtedly the way forward for financial services institutions, embracing these tools without understanding and addressing the risks is futile. 

  • AI bias: One of the most pronounced risks associated with employing AI in banking is bias. After all, all AI algorithms have an inherent human factor. It is people who create AI models, and these creators can introduce their own personal biases into these systems. These biases can multiply upon deployment, leading to concern about AI-generated outcomes.
  • Ethics: Financial institutions operate within regulatory frameworks that mandate explanations for credit-related decisions to prospective customers. This requirement poses a challenge when implementing tools based on deep learning neural networks, which uncover intricate correlations among numerous variables, often beyond human comprehension.
  • Customer mistrust: Trust is the name of the game in financial services, but misused or poorly deployed AI tools could erode it. For example, while customer-facing chatbots offer convenience and speed of use, they risk damaging trust if they exhibit errors or inaccuracies.
  • Regulatory and legal: Regulatory bodies are playing catch-up with the rapid advancement of generative AI and foundation models. CISOs must anticipate forthcoming regulations and proactively navigate them, including perspectives from existing regulatory bodies and the potential establishment of new entities dedicated to regulating AI. 
  • Data breaches: As generative AI advances swiftly, unstructured data is proliferating. Until organizations embed next-gen data loss prevention and compliance into these tools, they must exercise caution. On top of that, we must remember that, like all systems, generative AI platforms are vulnerable to cyber-attacks, incuding unique risks like training data poisoning and prompt injection attacks.

Actions for CISOs to take 

With so many risks to contend with, it’s clear that establishing proper governance structures is a must to ensure the ethical and legal use and acquisition of AI. CISOs play a pivotal role in this by setting up the necessary systems and controls to supervise AI applications. This involves establishing clear lines of accountability and ensuring senior management takes responsibility.

To kick off this journey, it’s essential to evaluate how your existing governance frameworks apply to generative AI. Your current policies might provide a foundation to build upon. Enhancing these frameworks with a set of well-defined AI principles can significantly bolster your governance strategy. On the other hand, you might consider adopting a comprehensive AI governance framework that can adapt to various usage scenarios, or you could opt for specific policies tailored to certain generative AI implementations. The right path will be determined by factors such as regulatory expectations, company culture, and implementation capabilities.

Taking a close look at your current processes related to procurement, development, implementation, testing, and ongoing monitoring of IT systems is a prudent step. This ensures a smooth introduction and utilization of generative AI. An adaptive governance approach should be embraced to navigate the ever-evolving technology landscape and the nuances of different AI systems.

Furthermore, don’t overlook the importance of reviewing training practices, record-keeping procedures, and audit protocols. This review process aids in effectively implementing policies, principles, and guidelines that promote responsible and effective AI governance.

How Polymer DLP can help

Responsible, ethical AI is the goal for all financial services organizations, but the limitless potential of these tools is a double edged sword, bringing about troubling risks in relation to compliance, ethics and data security. Thankfully, just as AI is advancing at the speed of light, so too are specialist security tools designed to mitigate AI security and compliance risks. 

Polymer data loss prevention (DLP) for AI empowers you to confidently leverage tools like ChatGPT while safeguarding privacy, security, and compliance. Infused with natural language processing (NLP), Polymer DLP enables seamless and intelligent redaction of in-motion Personally Identifiable Information (PII) and IP across Generative AI platforms and cloud apps like Slack, Teams, and Dropbox. 

For a deeper understanding of Polymer DLP for AI, check out our recent whitepaper.

Polymer is a human-centric data loss prevention (DLP) platform that holistically reduces the risk of data exposure in your SaaS apps and AI tools. In addition to automatically detecting and remediating violations, Polymer coaches your employees to become better data stewards. Try Polymer for free.


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