Common Machine Learning Security Risks and How to Overcome Them
When it comes to identifying imminent cybersecurity threats, machine learning seems like the ideal tool for the job. Machine learning can evaluate endpoints and detect malicious activity faster than we can.
What is machine learning in security? Why do organizations like Google rely on machine learning to keep their endpoints secure? On the flip side, what are the most common security risks faced by machine learning applications?
Why Is It Important to Consider Machine Learning Security Risks?
Artificial intelligence is a rapidly growing industry. Tech corporations, startups, and universities all contribute to the fast-paced advancement of machine learning and its diverse applications. But one aspect we don’t often hear about when it comes to machine learning is security.
Vulnerabilities in a machine learning application can have expensive, or even life-threatening, consequences. For instance, McAfee technicians were able to hack Tesla’s autonomous vehicles by simply adding a few inches of black tape to a road sign. As a result, the car interpreted a thirty-five miles per hour speed limit as eighty-five and began accelerating.
Even the most innocuous applications can leave organizations vulnerable. In 2019, Delta Airlines filed a lawsuit against their chatbot developer for a passenger data breach that occurred two years earlier. A machine learning hack resulted in extensive breaches of sensitive customer data, costing the Airline company millions of dollars.
Machine Learning Use Cases for Security
Machine learning is quickly becoming a staple in the cybersecurity industry. So, what role do high-powered algorithms play in protecting organizations against cyberattacks?
- Protecting Mobile Endpoints
When it comes to machine learning and mobile devices, we often hear about voice-based virtual assistants such as Google Now, Amazon’s Alexa, and Apple’s Siri. But machine learning is needed in the realm of security as well.
For instance, Google is leveraging machine learning to analyze threats against its mobile endpoints. Preventing cyberattacks on mobile devices is especially critical in the face of an increasingly remote workforce.
2. Enhanced Human Analysis
Every organization depends on a team of security experts to identify malicious attacks, survey the network, protect endpoints, and continuously assess potential vulnerabilities. Continuous assessment can be one of the most challenging aspects of IT security. That’s where machine learning comes in.
In 2016, the Computer Science and Intelligence Lab (CSAIL) at the Massachusetts Institute of Technology developed AI2, a security platform powered by machine learning that scans millions of logins per day, flagging any suspicious activity and forwarding it to a human analyst for inspection. CSAIL’s new platform led to an 85% attack detection rate.
Another major benefit of machine learning is automation. Whether we’re talking about security or manufacturing, machine learning has the potential to reduce the need for repetitive manual tasks, such as stocking shelves or surveying for ransomware.
A number of data security tools powered by machine learning and artificial intelligence allow security teams to conserve their resources and focus on high-level strategic issues and critical attacks.
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