Machine learning has created significant advancements for industries and set the pace for a future built on artificial intelligence (AI) technology. The endless possibilities and technological capabilities that machine learning has brought to the world have simultaneously created new security risks that threaten progress and organizational development.
Understanding machine learning security risks is one of our current technological time's most important undertakings because the consequences are extremely high, especially for industries such as healthcare where lives are on the line.
Let’s first discuss the types of machine learning security risks that you can encounter so that you can be better prepared to face them head-on.
Types of Machine Learning Security Risks
Since machine learning uses data, this accounts for a substantial part of the security risks. However, dozens of risks are associated with machine learning that can potentially threaten systems and reduce positive outcomes in machine learning models.
By educating yourself on the types of risks, you can take the first step in learning how to best protect systems from outside threats. For those looking to pursue a career in machine learning, it’s important to understand machine learning security risks to prepare your own capabilities and further your knowledge. Some of the more common types of risks attributed to machine learning are described below:
Data privacy attacks are incredibly common, where sensitive and private business, employee, client, or customer data is stolen. Think about eBay in 2014, where 145 million users were compromised, or LinkedIn in 2012 and 2016 when 165 million email and passwords were affected.
Data poisoning attacks change training data that affect the parameters of machine learning models. Bad data is inserted into your model causing it to learn something new that was not originally intended.
Transfer Learning Attack
In this scenario, trained models are at risk and potential attacks are launched that trick your machine learning models and alter their behavior.
Online System Manipulation
Systems connected online can be exploited, especially in a world where information is shared between users while machine learning models are trained.
How Can You Prepare for Risks?
Machine learning systems need to be secured before attacks occur rather than combating attacks after the fact. The process of engineering secure systems is integral to machine learning development, and someone with an interest in machine learning as a career must prepare for machine learning security risks with the right knowledge and education. Below are some of the processes for creating secure systems at the design level:
- Architectural risk analysis is a process that helps to create a system that can understand the types of risks involved, so that machine learning engineers are better prepared to combat those risks or avoid them altogether.
- Adversarial training helps to train your systems to recognize the potential threats mentioned above—such as poisoning attacks—so that your system understands what those threats look like to stop attacks before they start.
- Anomaly detection is used, for example, in data poisoning so that when something malicious is inserted into your training data, you can detect it. An attacker might create poisoning points called “inliers” which are very similar to your data distribution model. To go deeper, you can use micromodels to mark safe or suspicious training instances.
- Documenting and tracking how people are working is especially useful with online system attacks. Having information about who is working, their intended purpose, and at what times the algorithm is being used are recorded.
- System verification should be implemented at all times so that everyone working within the system can check the information and make sure that it is verified, or check for weaknesses within the system that can potentially be exploited.
Further Your Machine Learning Career and Education
Machine learning is as valuable as the accuracy of your algorithms. Understanding and mitigating the risk associated with machine learning development will help you maintain secure systems that increase the reliability of successful outcomes.
Simplilearn is a certified online bootcamp provider that can help you learn more about improving the security of your systems and machine learning outcomes. Check out the Machine Learning Certification Course for an introduction. For learners who want to dive deeper into machine learning, check out the Post Graduate Program in AI and Machine Learning, offered in partnership with Purdue University and collaboration with IBM.