Adaptive Identity Authentication

CyberNotes
3 min readOct 17, 2024

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The thin line between security and usability

Image from https://www.alvareztg.com/

Technology offers numerous advantages, but it has also enabled more audacious forms of theft. To be more specific, threat actors operate without remorse, targeting people indiscriminately such as cancer patients, the elderly, causing homelessness, and worst of all, showing no regret for their actions.

If we are to keep pace with the advancing technologies, then we have to keep up with the negative part of it as well: dealing with threat actors. Implementing adaptive identity authentication is one effective way to enhance our authentication protocols and keep these malicious individuals at bay!

What is adaptive identity authentication and why is it important?

This type of authentication dynamically adapts the level of user authentication depending on user analytics(user behavior, device information, location, time of access) and the potential risk associated with the transaction.

Lets look at an example:

  • Low-Risk Transaction: If a user makes a small purchase of $5 at a store they frequently visit, a simple username and password may suffice for authentication.
  • Medium-Risk Transaction: If the same user attempts a purchase from a new device or an unfamiliar IP address, the system might require additional security measures like a One-Time Password (OTP), Multi-Factor Authentication (MFA), or biometric verification to ensure the legitimacy of the transaction.
  • High-Risk Transaction: If the user tries to change sensitive account details, such as updating their password or making a purchase from a different country, the system could temporarily lock the account and prompt for further verification or approval before proceeding. This extra layer of security helps prevent unauthorised access and protects the user’s account from potential threats.

The False Positives

It’s important to acknowledge that SOC analysts are increasingly burdened with false positives generated by detection and prevention systems designed to simplify their tasks.

A false positive occurs when an activity is flagged as a threat/scam, even though it poses no actual risk. (not a scam)

Banks and credit card companies are well aware of this challenge and must carefully balance the need to secure customer accounts with maintaining seamless access to services. So, how can they find this balance?

The good news is that, thanks to the advancements in Machine Learning and AI, we are able to analyse intricate behavioural patterns such as the typing speed, and the ip address, the transaction patterns as well as the time-based analysis in real time.

Moreover, using a combination of something you are(such as fingerprint), something you have(OTP), and something you know(Password) is being increasingly used, with the aim of improving financial security.

In addition, it is important for companies to have clear set of policies that the customers are well aware of including the measures that the banks will take to protect their financial information. Regular audits and improvement of the performance of the policies, based on the preset metrics will also aid in making this a continuously improved process.

The good news is that a lot of financial and lending institutions have adapted this technology. And we are seeing credit card fraud becoming more and more difficult. However, there is always room for improvement and this is just the starting point.

The bigger Problem

The bigger problem, however, goes beyond credit card fraud. These lending institutions are increasingly turning into hotbeds for identity theft, giving threat actors the ability to open accounts under false pretence.

This introduces an entirely new set of challenges that deserves its own discussion in a separate blog post.

#cybersecurity #banking

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CyberNotes
CyberNotes

Written by CyberNotes

Data Science/Cyber - Student at Michigan State University.

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