Page 102 - Cyber Defense eMagazine August 2024

5. User and Entity Behavior Analytics

AI fraud detection can also improve cybersecurity through User and Entity Behavior Analytics (UEBA).
This practice deploys AI to monitor how users and devices behave on a company network. When the
models detect suspicious behavior like unusual file transfers or login attempts, they lock the account and
alert security teams.

UEBA can stop cyberattacks from spreading after initial defenses fail to prevent them. It also helps work
around the 4 million worker shortage in cybersecurity, ensuring strained security workforces can still
provide 24/7 protection.

Considerations for Implementing AI Fraud Detection

As these use cases highlight, AI fraud detection has significant advantages. However, it requires attention
to a few best practices to reach its full potential.

One of the most common challenges in anti-fraud AI is its tendency to produce false positives. Machine
learning models often over-fit fraud definitions, which can lead to high false alarms. These cases may
worsen the alert fatigue that 62% of IT teams say is driving turnover.

Careful training reduces false positives. Organizations should provide plenty of data on both real fraud
examples and legitimate cases to drive more reliable AI results. Tweaking the model over time will also
help it better distinguish between real fraud and benign activity.

Data privacy is another issue that deserves attention. Tailoring behavior analytics to specific users
requires a considerable amount of sensitive user data. Consequently, AI fraud detection entails significant
privacy risks. Some users may not feel comfortable giving away that much information, and storing it
opens the door to far-reaching breaches.

In light of these risks, brands should be upfront about their AI use and allow users to opt out of these
services. They should also encrypt all AI training databases and monitor these systems closely for
intrusion. Regular audits to verify the model’s integrity are also ideal.

AI-Powered Fraud Detection Has Many Applications

While AI fraud detection is still imperfect, it’s a significant step forward compared to conventional
methods. Industries from finance to e-commerce to cybersecurity can benefit from this innovation.

As machine learning techniques improve, these applications will become even more impactful. Before
long, AI-powered fraud detection will reshape multiple sectors.

Cyber Defense eMagazine – August 2024 Edition 102
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