Financial firms like hedge funds, private equity firms, and others are only beginning to explore the business applications of emerging AI tools—especially those driven by large-language models (LLM), characterized as generative AI. But while emerging AI tools offer countless potential benefits, they also pose notable cybersecurity risks in terms of data accuracy, data privacy, bias, and others.
“Many AI approaches have an explainability challenge, which means that humans have a tough time reverse-engineering how the AI came to a certain conclusion,” as GovInfoSecurity describes. “This ‘black box’ approach can make it difficult for organizations to understand the source of the information an AI model uses, and therefore to assess where and how to use the information.”
Additionally, AI breakthroughs are putting more powerful tools in the hands of bad actors. This warrants additional vigilance and risk mitigation among financial firms who can quickly fall victim to more complex attacks; or even more targeted or vigorous traditional attack methods.
In this article, we explore the opportunities and cybersecurity risks of emerging AI tools, both at the hands of attackers and financial firms themselves. We also make five recommendations as to how firms can position themselves for safety and success with regard to emerging AI technologies.
Rising AI Use Cases Warrant Further Assessment
As with other industries, there is no apparent ceiling to the rising use cases for AI in financial services. This includes emerging generative AI solutions. A recent report found that expanding generative AI use cases in financial services could deliver $200 billion to $340 billion in value annually, MIT Technology Review reports.
The existing and emerging use cases for generative AI in financial services are wide-ranging, touching on all aspects of business operations. Examples include:
- Fraud detection: By analyzing large data sets, AI algorithms can more accurately detect fraudulent activity and then make proactive recommendations to querying security teams.
- Investment analysis: Generative AI tools can help investors analyze vast amounts of data to make more informed investment decisions through query and response.
- Customer service: AI-powered chatbots and virtual assistants can improve customer experiences by providing personalized responses and more efficient support.
- Automated trading: AI tools can analyze market trends and make trades or trading recommendations faster than humans, potentially increasing profits for financial firms.
- Risk management and compliance monitoring: AI algorithms can analyze vast amounts of data in real-time, helping firms identify potential risks and remain compliant with regulations.
But as the use cases for AI in financial services continue to expand, so do the cybersecurity risks associated with these tools. A 2023 annual report from the U.S. Financial Stability Oversight Council found that “AI can introduce certain risks, including safety and soundness risks like cyber and model risks”; the Council advised both regulators and financial firms to “deepen expertise and capacity to monitor AI innovation and usage and identify emerging risks,” Reuters reports.
Firms Must Take Account of AI Cybersecurity Issues
Indeed, emerging AI technologies come with an array of potential pitfalls that financial firms must not ignore. These risks, ranging from issues with accountability to data breaches by malicious actors, pose significant challenges to firms’ cybersecurity. For example:
- Data inaccuracies: AI models can make mistakes, and those mistakes may be difficult to detect. Firms must ensure that their data is accurate and reliable before training AI algorithms.
- Data privacy concerns: As financial firms collect vast amounts of sensitive personal information, they must take extra precautions to protect this data from breaches or misuse by third parties.
- Bias in decision-making: AI systems can inherit the biases of their creators and the data they are trained on, leading to biased decision-making. Financial firms must actively work to eliminate bias in AI algorithms.
- Cybersecurity vulnerabilities: As cybercriminals become more sophisticated, financial firms must be vigilant in guarding against potential attacks on their new and untested AI systems.
- Model explainability: With complex AI models, it can be challenging for financial firms to understand how a decision is made. This lack of transparency can make it difficult to identify and address errors or potential biases.
- AI-powered cyber attacks: Bad actors can use AI to research vulnerabilities, carry out more sophisticated attacks, or dramatically increase the impact of traditional attack methods.
Financial firms must take a proactive approach to addressing these cybersecurity risks, ensuring that their AI tools are secure and reliable.
Firms Can Reduce AI-Related Cybersecurity Risks
Fortunately, there are emerging, robust risk management strategies firms can adopt to regularly monitor and audit AI systems, improve both explainability and accountability, and protect against more sophisticated cyberattacks. Here we share five straightforward strategies security and other executive leaders at financial firms can adopt.
Increase Scrutiny During AI Adoption
Financial leaders should develop a thorough understanding of how an AI model works before implementing it into their systems. This includes asking vendors for explanations behind the decision-making process and any potential biases in the data used to train the algorithm. Additionally, financial firms should conduct thorough audits of their AI systems, continually monitoring performance and adjusting as necessary.
Implement Robust Data Security Measures
Financial firms must put rigorous security measures in place as they leverage private data for AI applications. They need new measures beyond existing data security practices, no matter how robust; that’s because AI can create new vulnerabilities through its use of sensitive data. Firms can do so with technologies that monitor data entering and leaving the organization, as well as AI-specific security tools such as explainability software.
Establish Clear Protocols for AI Decision-Making
Financial firms must establish clear protocols for how AI systems are used in decision-making processes. This could include developing a human oversight committee to review decisions made by AI tools or implementing fail-safes and contingency plans in case of system errors. Firms should set up internal methods for transparency in team members’ use of AI tools; for example, they can use a central repository for AI model code and documentation.
Prevent and Avoid Perpetuating Bias from AI
The potential for bias in AI applications is well documented: AI may provide insights and recommendations that benefit only a subsection of the population, for example; humans tend to perpetuate biases presented by generative AI as well, Scientific American reports. Financial firms therefore have a responsibility to actively mitigate and eliminate bias from AI tools. This could include developing diverse and inclusive teams to work on AI projects, regularly testing for bias in algorithms, implementing ethical principles into the development process, and screening generative AI outputs via a diversity, equity, and inclusion (DEI) committee, among other methods.
Invest in Employee Training
Finally, financial firms must invest in training for employees to understand AI technologies and how they can impact cybersecurity. This includes educating staff on the potential risks associated with AI and how to identify and address them. Investing in formal tools for upskilling existing employees and training new hires can greatly enhance firms’ ability to mitigate cybersecurity risks associated with emerging AI tools, even without substantial investments in new cybersecurity technologies.
AI Security Begins with Responsibility in Adoption
Everyone in the industry—from financial regulators to FI employees—is responsible for the secure and responsible use of generative and other AI tools. The best way to begin protecting your own firm is by performing due diligence at the point of adoption. That means scrutinizing new and potential AI tools; but also, exploring potential vulnerabilities to AI-driven attacks when adopting these technologies or new business practices.
Partner with Option One Technologies as You Explore Opportunities with AI
Option One Technologies specializes in technology adoption for financial firms. Contact us directly to learn more about how we can help you meet your unique AI needs and strengthen your financial firm’s cybersecurity.
