The Biggest Security Innovation in FinTech

Why analyzing cyber behavior is the best defense against modern bank theft

fintech data breach security analytics

No matter how many resources are at their fingertips, cybersecurity experts consistently struggle to keep up with the overwhelming amount of threats and vulnerabilities standing in their way. Cybersecurity is a hectic grind with tens of thousands of security events cropping up across hundreds of different applications each day. These applications usually run with numerous open vulnerabilities. And typically, more than a few users who tap into these applications engage in some sort of risky behavior that needs to be curtailed.

Banks have always been targets for theft. We have gone from thieves with horses and bandanas, to thieves using keyboards and Internet connections. The goals of both are similar: Steal from the bank, and enrich yourself. The motivations for both are often the same: monetary enrichment. While theft today may not always involve hard currency, it is still valuable.

  • Customer or employee PII enables identity theft
  • Attacks against money-transfer services such as Swift can redirect legitimate money transfers
  • Often just the breach of data from an institution can cause monetary loss to shareholders in drops of stock prices

Historically, to react to these thefts, banks added vaults, alarms, and guards. While this reduced the effectiveness of attacks, thieves found other ways to steal. Often theft became an inside job. An employee was recruited or coerced into attacking the bank from the inside.

Today, financial institutions are facing insider threats in a similar way. An employee account is compromised by some means, then used to execute the theft. Less frequently, an employee will go bad and use internal privileges to execute the theft. Regardless of the way the attack is implemented, the goals are the same. Since the goals are the same, the change in behaviors related to this theft are similar.

Digital Footprint Unique Behavior Interset

The goal is to move through the network, find what they are looking for, and steal it. When these activities happen, there is change in the behaviors of the cyber entities used to carry out the attacks. When these behaviors change, we have the best opportunity to identify the attempted theft.

To identify these changes in behavior, a baseline of behavior needs to be created connecting one cyber entity to another. By having this baseline of behavior, we can now detect when there is change. To see this, massive amounts of data must be analyzed in real time, and comparisons made to what is expected behavior. Only true online machine-learning systems can achieve this. Interset has a threat-analytics platform that will allow the detection of these insider threats, so financial loss is minimized or does not occur.

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When: Oct. 11, 2017
Where: Westin Times Square (New York City)
What: ISE Northwest Northeast Executive Forum and Awards 2017