Last year, hackers made off with $951 million from the Bank of Bangladesh. The record-breaking cyber-heist was no anomaly. It was just one in a series of sophisticated cyber-attacks targeting the financial sector. In 2014, criminals stole account information from 83 million JP Morgan customers. And again last year, a single Russian bank suffered 69 separate DDoS attacks. Cyber-attacks against the financial sector are relentless.
And finance isn’t just hit more often than other industries. It’s hit harder. For banks, the average cost per record stolen is $221, well over the average of $158. Driven by the prospect of a huge payday, hackers reserve some of their most sophisticated attacks for banks and other high-profile financial organizations.
To detect advanced attacks like these, we use unsupervised machine learning to identify deviations from normal network activity. Crucially, this approach lets companies detect threats from the inside. At Darktrace, some of the biggest vulnerabilities we’ve found started with a careless employee. Nowhere is this activity more troubling than in the financial services sector.
For example, at a top US investment firm, we detected strange communications between a company desktop and a Chinese cloud service. These communications were deemed highly anomalous and a major deviation from that user’s normal behavior. The employee in question was using the cloud service for legitimate work reasons, but this service came with a host of hidden risks — namely, it was secretly transmitting login details to an unknown third party. The leaked information could have led to a debilitating attack.
These attacks are alarming, but in the future, attackers won’t just try to steal data; they’ll try to change it. Since financial services rely on public confidence, they’ll be disproportionately affected by data manipulation. For instance, by subtly tweaking bank account information, an attacker could destroy the very integrity of the bank’s data. The bank would lose all credibility if the attack went public. Similarly, an attack could alter the mathematical models that inform boardroom decisions at a Wall Street company, thus forcing them to make bad investments.