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U.S. DEPARTMENT OF THE TREASURY RELEASES REPORT ON MANAGING ARTIFICIAL
INTELLIGENCE-SPECIFIC CYBERSECURITY RISKS IN THE FINANCIAL SECTOR
March 27, 2024
WASHINGTON – Today, the U.S. Department of the Treasury released a report
on Managing Artificial Intelligence-Specific Cybersecurity Risks in the
Financial Services Sector. The report was written at the direction of
Presidential Executive Order 14110 on the Safe, Secure, and Trustworthy
Development and Use of Artificial Intelligence. Treasury’s Office of
Cybersecurity and Critical Infrastructure Protection (OCCIP) led the development
of the report. OCCIP executes the Treasury Department’s Sector Risk Management
Agency responsibilities for the financial services sector.
“Artificial intelligence is redefining cybersecurity and fraud in the financial
services sector, and the Biden Administration is committed to working with
financial institutions to utilize emerging technologies while safeguarding
against threats to operational resiliency and financial stability,” said Under
Secretary for Domestic Finance Nellie Liang. “Treasury’s AI report builds on our
successful public-private partnership for secure cloud adoption and lays out a
clear vision for how financial institutions can safely map out their business
lines and disrupt rapidly evolving AI-driven fraud.”
In the report, Treasury identifies significant opportunities and challenges that
AI presents to the security and resiliency of the financial services sector. The
report outlines a series of next steps to address immediate AI-related
operational risk, cybersecurity, and fraud challenges:
1. Addressing the growing capability gap. There is a widening gap between
large and small financial institutions when it comes to in-house AI
systems. Large institutions are developing their own AI systems, while
smaller institutions may be unable to do so because they lack the internal
data resources required to train large models. Additionally, financial
institutions that have already migrated to the cloud may have an advantage
when it comes to leveraging AI systems in a safe and secure manner.
2. Narrowing the fraud data divide. As more firms deploy AI, a gap exists in
the data available to financial institutions for training models. This gap
is significant in the area of fraud prevention, where there is insufficient
data sharing among firms. As financial institutions work with their
internal data to develop these models, large institutions hold a
significant advantage because they have far more historical data. Smaller
institutions generally lack sufficient internal data and expertise to build
their own anti-fraud AI models.
3. Regulatory coordination. Financial institutions and regulators are
collaborating on how best to resolve oversight concerns together in a
rapidly changing AI environment. However, there are concerns about
regulatory fragmentation, as different financial-sector regulators at the
state and federal levels, and internationally, consider regulations for AI.
4. Expanding the NIST AI Risk Management Framework. The National Institute of
Standards and Technology (NIST) AI Risk Management Framework could be
expanded and tailored to include more applicable content on AI governance
and risk management related to the financial services sector.
5. Best practices for data supply chain mapping and “nutrition labels.” Rapid
advancements in generative AI have exposed the importance of carefully
monitoring data supply chains to ensure that models are using accurate and
reliable data, and that privacy and safety are considered. In addition,
financial institutions should know where their data is and how it is being
used. The financial sector would benefit from the development of best
practices for data supply chain mapping. Additionally, the sector would
benefit from a standardized description, similar to the food “nutrition
label,” for vendor-provided AI systems and data providers. These “nutrition
labels” would clearly identify what data was used to train the model, where
the data originated, and how any data submitted to the model is being used.
6. Explainability for black box AI solutions. Explainability of advanced
machine learning models, particularly generative AI, continues to be a
challenge for many financial institutions. The sector would benefit from
additional research and development on explainability solutions for
black-box systems like generative AI, considering the data used to train
the models and the outputs and robust testing and auditing of these models.
In the absence of these solutions, the financial sector should adopt best
practices for using generative AI systems that lack explainability.
7. Gaps in human capital. The rapid pace of AI development has exposed a
substantial AI workforce talent gap for those skilled in both creating and
maintaining AI models and AI users. A set of best practices for
less-skilled practitioners on how to use AI systems safely would help
manage this talent gap. In addition, a technical competency gap exists in
teams managing AI risks, such as in legal and compliance fields.
Role-specific AI training for employees outside of information technology
can help educate these critical teams.
8. A need for a common AI lexicon. There is a lack of consistency across the
sector in defining what “artificial intelligence” is. Financial
institutions, regulators, and consumers would all benefit greatly from a
common AI-specific lexicon.
9. Untangling digital identity solutions. Robust digital identity solutions
can help financial institutions combat fraud and strengthen cybersecurity.
However, these solutions differ in their technology, governance, and
security, and offer varying levels of assurance. An emerging set of
international, industry, and national digital identity technical standards
is underway.
10. International coordination. The path forward for regulation of AI in
financial services remains an open question internationally. Treasury will
continue to engage with foreign counterparts on the risks and benefits of
AI in financial services.
As part of Treasury’s research for this report, Treasury conducted in-depth
interviews with 42 financial services sector and technology related companies.
Financial firms of all sizes, from global systemically important financial
institutions to local banks and credit unions, provided input on how AI is used
within their organizations. Additional stakeholders included major technology
companies and data providers, financial sector trade associations, cybersecurity
and anti-fraud service providers, and regulatory agencies. Treasury’s report
provides an extensive overview of current AI use cases for cybersecurity and
fraud prevention, as well as best practices and recommendations for AI use and
adoption. The report does not impose any requirements and does not endorse or
discourage the use of AI within the financial sector.
In the coming months, Treasury will work with the private sector, other federal
agencies, federal and state financial sector regulators, and international
partners on key initiatives to address the challenges surrounding AI in the
financial sector. While this report focuses on operational risk, cybersecurity,
and fraud issues, Treasury will continue to examine a range of AI-related
matters, including the impact of AI on consumers and marginalized communities.
Read Treasury’s AI Report here.
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LATEST NEWS
March 27, 2024
Treasury Sanctions Actors Financing the North Korean Weapons of Mass Destruction
Program
Remarks by Secretary of the Treasury Janet L. Yellen at Suniva in Norcross,
Georgia
Treasury Sanctions Hamas-Aligned Terrorist Fundraising Network
U.S. Department of the Treasury Releases Report on Managing Artificial
Intelligence-Specific Cybersecurity Risks in the Financial Sector
March 26, 2024
READOUT: Secretary of the Treasury Janet L. Yellen’s Call with Coordinating
Minister of Maritime and Investment Affairs Luhut Binsar Pandjaitan of Indonesia
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