Preparing Employees for Risk Management
The Evolving Digital Landscape in Finance
The digital world is changing rapidly, and employees are already embracing new tools. With the rise of generative artificial intelligence (AI) tools like ChatGPT and Google Gemini, as well as various data analysis and automation platforms, today’s workforce is accessing a vast array of public AI technologies. These innovations promise to enhance productivity and efficiency for organizations, including community banks and credit unions. However, they also pose significant risks, particularly concerning sensitive data handling and regulatory compliance.
Understanding AI Use in Your Institution
Whether it’s creating summaries for loan policies, drafting emails to clients, brainstorming marketing ideas, or analyzing spreadsheets, employees are increasingly turning to AI for support. Often, this is done with the best intentions to improve work efficiency rather than for any malicious purposes. Nevertheless, even well-meaning use of AI can lead to serious risks if proper safeguards are not established, exposing sensitive customer information or internal strategies to potential breaches.
The Risks of AI Adoption
The integration of AI into daily operations carries several risks, particularly regarding privacy:
- Data Leakage: Employees might unknowingly input sensitive personal data or internal documents into public AI platforms, which can then be stored and utilized to train future models.
- Shadow AI Use: Without stringent oversight, organizations may remain unaware of the AI tools in use, the data being shared, or any policies being overlooked.
- Compliance Gaps: Regulatory bodies are increasingly scrutinizing how financial institutions manage AI usage, and failure to take action could result in compliance issues.
- Inconsistent Controls: Outdated endpoint protections or lax data loss prevention settings can leave organizations vulnerable to accidental or intentional data breaches.
Establishing a Cybersecurity Framework
Instead of outright banning AI, community banks and credit unions should focus on governance. This means bringing together teams from cybersecurity, risk management, human resources, and business operations to create a cohesive framework. Here are some essential steps to start:
- Conduct an AI Use Assessment:
- Survey teams to determine how they are utilizing AI tools.
- Compile an inventory of known AI platforms and track browser-based usage.
- Identify potential risks associated with data exposure.
- Update the Acceptable Use Policy:
- Clarify what constitutes acceptable AI use within the organization.
- Provide specific examples of acceptable and unacceptable practices tailored to your operations.
- Create a Clear AI Use Policy:
- Specify approved tools (if any) and provide guidelines for responsible usage, including what data must never be entered into external AI systems.
- Ensure that your policy aligns with existing compliance, cybersecurity, and privacy standards.
- Enhance Endpoint Controls:
- Utilize data loss prevention (DLP) tools to monitor and manage potentially risky data movements.
- Restrict access to mass storage devices on company systems to prevent unauthorized data exports.
- Conduct regular monitoring of clipboard activities and network traffic for AI-related activities.
- Educate and Train Employees:
- Your workforce is the first line of defense. Ensure they understand the risks associated with AI use.
- Implement training specific to use cases and keep it updated as new tools and threats emerge.
How CLA Can Assist with AI Governance in Financial Institutions
CLA recognizes the challenges financial institutions face in balancing technological innovation with security. Our partnership with community banks and credit unions focuses on proactively assessing risks, modernizing digital infrastructures, and creating AI governance strategies that prioritize cybersecurity and data privacy principles.
Through our GoDigital for Financial Services initiative, we assist institutions in the following ways:
- Assessing Current Risk Posture: Evaluate risks associated with AI and digital tool usage.
- Identifying Shadow IT and Data Exposure Risks: Uncover hidden risks that may not be immediately visible.
- Designing and Implementing Policies: Create policies for AI use, data governance, and endpoint protections.
- Creating a Roadmap for Responsible AI Adoption: Support operational efficiency, growth, and customer trust.
- Modernizing Tech Stacks: Implement solutions that reduce costs and enhance compliance while mitigating fraud and cyber threats.
Our goal is to ensure that digital tools, including AI, become strategic assets rather than liabilities, allowing institutions to harness their powers while safeguarding customer trust and maintaining compliance with regulatory standards.
