I still remember the days when the software development industry was in its infancy. Many people were concerned about software vulnerabilities and exploits, and they were right back then as hackers took advantage of these exploits and started fulfilling their malicious designs. Every data breach and cyber-security attack was extensively covered by mainstream media, both print and electronic.

The focus is more on purging individual bugs than identifying the root cause of the problems. A few years later, we realized that the only solution would be to build secure software. A few decades later, software security has become an integral part of cyber-security programs.

With today’s software and apps using machine learning and artificial intelligence, it is important to secure machine learning and artificial intelligence systems you are using. Don’t get me wrong machine learning can do a much better job than humans at tasks such as image classification, translation, play and win complex games such as chess, Go along with other video games.

Despite its advantages, some businesses are still reluctant to use machine learning based systems due to security risks attached to them. If you adopt machine learning in a haphazard way, you are more likely to increase your security risk manifold. That is why it is important for businesses thinking about adopting machine learning to understand the security risks attached to it.

In this article, you will learn about five common machine learning security risks and what you can do to mitigate those risks.

Machine Learning Security Challenges

One of the biggest hurdles in securing machine learning systems is that data in machine learning systems play an outside role in security. This makes it even more difficult to secure your machine learning systems. In most cases, data sets which a machine learning system is trained in account for 60% risk while learning algorithms and source codes account for 40% risk.

That is why it is important for businesses to divert all their energies towards architectural risk analysis. According to a report, architectural risk analysis is an important first step businesses need to take to protect their machine learning systems. The report further highlights more than 70 risks associated with machine learning systems. Protecting data which has become an integral part of a machine learning model is another big challenge.

1. Fooling the System

One of the most common attacks on machine learning systems is to trick them into making false predictions by giving malicious inputs. Simply put, they are optical illusions for machines, which show them a picture which does not exist in real world and force them to make decisions based on that. The coverage and attention are large, which makes it a much bigger threat than other machine learning security risks. This type of attack usually targets machine learning models.

2. Data Poisoning

Machine learning systems depend on data for learning purposes. That is why it is important for businesses to ensure reliability, integrity, and security of that data otherwise, you might get false predictions. Hackers know that and try to target data used by machine learning systems. They manipulate, corrupt and poison that data in such a way that it brings the entire machine learning system down to its knees.

Businesses should pay special attention and minimize the risk. Machine learning experts should prevent the damage by minimizing the amount of training data cyber criminals can control and to what extent. What is even worse is that you will have to protect all the data sources as attackers can manipulate any data source you might be using for training your machine learning systems. If you fail to do that, the risk of your machine learning training going haywire increases drastically.

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5 Common Machine Learning Security Risks and How to Overcome Them
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