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VideoAI Governance for Financial Institutions: Key Risks, Challenges and Steps to Take Now
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Artificial intelligence (AI) adoption is accelerating across almost every industry, and financial institutions are no exception. What started as experimentation with generative AI has quickly evolved into banks, credit unions, wealth managers and other financial institutions embedding AI into business processes, customer interactions, lending and investment decision-making, fraud detection and cybersecurity programs.
As AI becomes more deeply embedded in daily operations, many financial institutions are discovering that governance, oversight and risk management practices are not keeping pace. In a heavily regulated industry, the conversation is no longer centered on whether AI should be used. Rather, it focuses on how institutions can use AI responsibly while maintaining accountability, protecting sensitive customer information and managing emerging risks.
Why AI Governance Has Become a Business Imperative
AI governance is rapidly becoming a business imperative for financial institutions, not just a technology initiative. Internal audit, risk, compliance and cybersecurity functions all have an important role in establishing accountability, oversight and controls that support responsible AI use across the institution, particularly given heightened expectations from regulators.
Part of the challenge is that generative AI and large language models operate differently from traditional technology solutions. Rather than following predefined rules, they generate outputs based on patterns, probabilities and the data on which they were trained. While this capability creates significant opportunities for efficiency and innovation, it also introduces risks related to data privacy, credit and investment decisions, accuracy, lending bias, transparency and regulatory compliance.
The Growing Risk of Shadow AI
Another challenge is the rise of shadow AI, or the use of AI tools outside approved policies and governance processes. For financial institutions, shadow AI hinders the ability to know what data (often including nonpublic personal information) is being shared, where information is being stored and how outputs are being used in business decisions.
Further, unlike traditional technology implementations, employees can often begin using AI tools with little or no involvement from IT, security or compliance teams. In many cases, adoption starts with a desire to improve efficiency, automate routine tasks or accelerate decision-making, making it difficult to maintain visibility into how AI is being used across the institution.
Shadow AI further demonstrates that without clear policies and monitoring mechanisms, financial institutions may have limited insight into whether sensitive information is being entered into AI platforms, how AI-generated outputs are influencing lending, investment or account decisions, or if third-party AI tools align with security and regulatory requirements. These resulting gaps can create operational, compliance and reputational risks that are difficult to identify until problems emerge.
Applying Familiar Risk Management Principles to New Technology
Although AI introduces new considerations, the underlying governance and risk management principles remain largely unchanged. Financial institutions do not need to build entirely new oversight models from the ground up. They can leverage existing governance, risk and control processes, including established third-party risk management, model risk management and vendor oversight programs, to address AI-related risks.
For internal audit and risk professionals, familiar activities such as identifying risks, evaluating controls, testing effectiveness and reporting results continue to serve as the foundation for effective oversight. The objective remains the same: understand how technology is being used, assess associated risks and establish controls that support responsible use.
Leading frameworks, including NIST AI Risk Management Framework, OWASP GenAI Security Project publications and ISO 42001, reinforce many of the principles already found in enterprise risk management programs. They emphasize accountability, risk assessments, control implementation, ongoing monitoring and continuous improvement, providing a structured approach for managing AI risks as adoption expands.
Questions Leaders Should Be Asking Now
Regardless of where your financial institution is in its AI journey, leadership should have clear answers to key governance and risk management questions. The answers can help identify gaps and prioritize next steps.
- Do we know where AI is being used across the institution, including customer-facing, lending, investment and back-office functions
- Have we defined an AI risk appetite and acceptable use expectations that align with our regulatory obligations?
- How are we protecting nonpublic personal information (NPI), customer data and proprietary information entered into AI tools?
- Have AI-specific risks been assessed before deployment, including model risk, accuracy and fair lending or bias implications?
- Are third-party and vendor-provided AI tools subject to appropriate due diligence and ongoing oversight?
- How are AI-generated outputs validated before they inform lending, investment or account decisions?
- How are we identifying and managing shadow AI, or the use of AI tools outside approved policies and processes?
- Do employees have clear policies and training to support responsible AI use across the institution?
- Are we prepared to demonstrate our AI governance, controls and documentation to regulators and examiners?
- Do governance and oversight structures exist to monitor ongoing use, consumer protection concerns and emerging risks?
Building an AI Governance Roadmap
As financial institutions expand their use of AI, governance efforts often start with gaining visibility into where AI tools are being used and who is responsible for oversight. Without a clear understanding of AI adoption across the institution, it becomes difficult to assess risks, establish appropriate controls or monitor ongoing use.
A practical first step is to develop policies that define acceptable use expectations, followed by evaluating how AI-related risks fit within the broader enterprise risk management framework. This includes: (a) understanding how data, often including nonpublic personal information, moves into and out of AI solutions, (b) assessing potential impacts before deployment and (c) applying established third-party and vendor risk management processes. Institutions should also fully understand how AI providers are introducing new security measures, compliance requirements and risk response considerations.
With AI technologies and regulatory expectations evolving rapidly, policies, controls and training programs should evolve with them to support responsible use, satisfy examiners and address emerging risks.
How Weaver Can Help
Financial institutions that realize the greatest value from AI are often those that establish governance early, before risks become embedded in day-to-day operations and draw regulatory scrutiny. By creating clear policies, defining accountability and implementing oversight processes, institutions can support innovation while building trust with customers and regulators, and managing enterprise risk effectively.
Whether your institution is establishing foundational governance practices or strengthening existing processes, Weaver can help assess current-state governance, identify risk and control gaps, and develop a roadmap that supports responsible AI adoption, regulatory readiness and long-term resilience. Contact us to learn more.
Authored by Pree Wakharkar.
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