A recent survey by BestVPN.com found 87.5% of consumer respondents are concerned about their data privacy online. Yet simultaneously, 46% have done nothing to adjust their privacy settings on social media.
Research from CA Technologies and Frost & Sullivan found half of all enterprises who publically reported a breach experienced a 50% drop in customer trust and a 47% drop in business results. 48% of customers will avoid services after they suffer a breach.
Taken together, this research sends a very clear message to enterprises: consumers and clients expect you to handle cybersecurity. You can (and should) consider information security a part of the economic contract at the foundation of everyday transactions and business processes.
In other words, your cybersecurity posture is not just for your own defense—it is a basic expectation from your customers in much the same way they expect you to protect their physical safety inside your physical locations.
Cybersecurity is a diverse field, and there are plenty of areas to invest your time and energy: privileged access management, digital perimeter security, threat detection, etc. However, one area we want to focus on here is a capability taking cybersecurity by storm: machine learning.
Machine learning is fast becoming a necessity in enterprise-level security analytics. Yet it is also potentially perilous; if improperly deployed and maintained, it can hamper your cybersecurity posture overall.
Machine learning is a sophisticated AI system designed to learn. More specifically, it learns anomalous behaviors and programs from your threat intelligence and from rules set by your IT security team. Through this intelligence, it enables easier threat detection across large data sets, alleviating some of the threat hunting responsibilities of your security team.
However, this capability is dependant on threat intelligence to adapt to unpredictable threat behaviors in real-time. Moreover, it is also dependant on the rules set by your IT security team. Without a solid feed or constant maintenance, your machine learning will be limited to the data it acquired previously.
When it relies on data from the past, machine learning can stagnate fast. Hackers and other digital threat actors are constantly innovating. Without the right support, your enterprise will fall into the classic trap of both warfare and cybersecurity: always fighting the last war.
Machine learning just isn’t prepared to match the human ingenuity and collective collaboration of those wishing your databases harm on its own. Sure, they can and will learn, but only within the data parameters you’ve set and only in response to what it actually encounters.
This is where your IT security team needs to step in. Only they can monitor and guide your machine learning capabilities with a full understanding of what kinds of digital threats your enterprise is likely to face. They can adjust your machine learning parameters when needed and evaluate what the system has learned and whether that could cause problems in the future.
Your enterprise will be responsible for cybersecurity and data privacy. Make sure every capability in your platforms is working at optimal levels…or be prepared to face the consequences.
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