Artificial Intelligence (AI) Vs. Machine Learning (ML)

Some of the newest buzzwords floating around the tech world, Machine Learning (ML) and Artificial Intelligence (AI), are often used these days interchangeably but are actually quite different. With an ever-evolving tech world achieving breakthroughs at breakneck speeds, these types of confusions are becoming widespread. Here, we’ll break down some of the key differences between the two and explore the ways these advancements may affect the cybersecurity landscape going forward.

 

The Differences Between AI and ML

 

Probably the more familiar of the two, AI, is a more general term that encapsulates all of advanced computer intelligence. Implemented in a system in order to perform tasks that we typically associate with our human brains, AI is a broad term that includes systems used for tasks such as problem-solving, understanding a language, scheduling tasks, or recognizing and identifying objects or shapes.

 

ML, on the other hand, refers to a very precise way of achieving AI. The traditional way of creating an “intelligent” machine would be to give the piece of technology all the answers as you program it. This requires explicitly programming millions of lines of code that breakdown intricate rules and provide decision-making trees to refer to when deciding how to act and react in any given situation. ML instead relies on giving the machine a neural network that works on a system of probability. When fed large sets of data, this neural network predicts outcomes and decides on its own how to proceed in achieving its goal. The addition of a feedback loop means the machine can “learn,” through trial and error, which processes are best at achieving results and in turn readjust its algorithms to fine-tune its approach.

 

Vulnerabilities of AI and ML

 

Because the benefits of self-driving cars, voice-recognition software, and self-learning machines are so evident, the implementation of both ML and AI in business and our daily routines is inevitable. But utilizing these advancements will also come with their share of peril. Because both AI and ML require massive sets of data to become accurate and achieve positive results, they will most likely drive a spike in a particularly concerning type of cyberattack – data manipulation.

 

For the unfamiliar, data manipulation is a hacking technique in which instead of stealing data, cybercriminals change the details to make the data inaccurate. This malicious type of attack is hard to detect, can lead to a myriad of disastrous scenarios, and has already been highlighted by U.S. intelligence officials as a growing concern.

 

Preventing Data Manipulation

 

To combat this impending threat, businesses will have to develop a keen eye for detail, stay hypervigilant, and use a series of endpoint detection and response tools. Utilizing file integrity monitoring systems and carefully logging activity to track real-time data changes will become a necessity as the threat of data manipulation becomes increasingly prevalent.

 

MSP Knowledge and Power

 

If you run a business, it’s your job to stay ahead of the curve on the latest technological advancements and dangers. But understanding incredibly complex advancements like AI and ML and the risks that will arise with their implementation can be overwhelming. That’s why Managed Service Providers (MSP) are becoming increasingly valuable to medium and small-sized businesses in today’s hyper-technological climate. An MSP can provide business leaders with a committed team of professionals that ensure they have the most up-to-date knowledge, tools, and cybersecurity strategies to protect their bottom lines.

 

Looking to be savvy, cutting-edge, and completely secure? Contact Net Works and gift yourself the peace of mind that only working with an expert MSP can provide.

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