The landscape of financial cybersecurity is evolving rapidly with the integration of artificial intelligence (AI) into the financial services sector. A recent report from the Treasury Department has underlined the urgent need for collaboration between various stakeholders to address the growing cybersecurity risks associated with the use of AI.
The report sheds light on the widening gap in capabilities brought about by AI adoption. While larger financial institutions have the capacity to develop their AI systems, smaller players are struggling to keep up. This situation exposes them to cyber threats, especially as they often rely on third-party AI solutions. To bridge this gap, there is a call for enhanced collaboration and information sharing among different entities involved in the financial sector.
One of the critical issues raised in the report is the lack of data sharing for fraud prevention, particularly impacting smaller financial institutions. The insufficient access to data hinders their ability to build robust AI defenses against fraud, unlike their larger counterparts who have extensive data repositories for training AI models. To tackle this challenge, Narayana Pappu, the CEO of Zendata, suggests that startups could provide services for data standardization and quality assessment. Techniques like differential privacy can enable secure data sharing between financial institutions without compromising individual customer information.
Marcus Fowler, the CEO of Darktrace Federal, emphasizes the ever-changing nature of cyber threats and the growing complexity of the digital environments that need safeguarding. Highlighting the use of AI by malicious actors, Fowler stresses the importance of defensive AI in shielding organizations from these evolving threats.
The recommendations put forth in the report advocate for streamlining regulatory oversight, expanding standards within the financial services sector, creating ”nutrition labels” for AI vendors to enhance transparency, improving the explainability of complex AI systems, setting training and competency standards, standardizing terminologies in the AI domain, addressing digital identity concerns, and fostering global collaboration in AI regulations and risk mitigation strategies.
Despite the increasing adoption of AI and machine learning for fraud prevention within financial institutions, the high costs associated with developing these tools have limited their widespread implementation. Many institutions opt for external vendors to provide AI and ML solutions, with only a small fraction developing their solutions in-house. To overcome these obstacles, the report stresses the importance of increased collaboration and knowledge sharing.
In conclusion, the integration of AI into the financial services industry presents both opportunities and challenges. Collaborative efforts among government bodies, industry players, and startups are crucial to fortify the cybersecurity defenses of smaller financial institutions against emerging threats. By addressing issues related to data sharing, regulatory oversight, transparency, and competency standards, the financial sector can effectively harness the potential of AI while safeguarding against risks.
### FAQ
Q: Millaisia päähuolia käsitellään valtiovarainministeriön raportissa?
A: Raportissa korostetaan kasvavan tekoälyn käytön tuomia kyber-turvallisuusrisktejä talouspalvelualalla, erityisesti suuren ja pienen laitoksen välisen kyvykkyyseron kasvua.
Q: Miten datan vähäinen jakaminen vaikuttaa pieniin talouslaitoksiin petoksen torjunnassa?
A: Rajallinen pääsy dataan hankaloittaa heidän kykyään kehittää tehokkaita tekoälypetosten torjuntajärjestelmiä, toisin kuin suuret laitokset, jotka voivat hyödyntää massiivisia datavarastoja mallien kouluttamiseen.
Q: Mitä suosituksia raportissa on, jotta turvataan taloudellinen kyberturvallisuus?
A: Raportissa ehdotetaan säännösten valvonnan virtaviivaistamista, standardien laajentamista talouspalvelualalla, ”ravitsemustietojen” luomista tekoälytoimittajille, monimutkaisten tekoälyjärjestelmien selittävyyden parantamista ja kansainvälistä yhteistyötä tekoälyä koskevissa säännöissä ja riskienhallintastrategioissa.
Q: Mikä on startup-yritysten rooli datan standardoinnin ja laadun arvioinnin parantamisessa?
A: Startups voivat tarjota innovatiivisia ratkaisuja, kuten datan standardointia ja laadun arviointipalveluja, hyödyntäen tekniikoita kuten differentiaalinen yksityisyys helpottamaan turvallista datan jakamista talouslaitosten välillä.
Q: Miten talouslaitokset tällä hetkellä käyttävät tekoälyä ja koneoppimista petosten torjunnassa?
A: Talouslaitokset hyödyntävät sisäisiä petostenestojärjestelmiä, ulkoisia resursseja ja uusia teknologioita kuten tekoälyä ja koneoppimista. Kuitenkin näiden työkalujen kehittämisen kustannukset muodostavat merkittävän esteen laajalle käyttöönotolle.
The source of the article is from the blog newyorkpostgazette.com