Artificial intelligence (AI) and machine learning (ML) are hot trends right now, and justifiably so. If leveraged effectively, they can take businesses to the next level. These solutions give them the power to understand their customers better and see patterns, trends and opportunities that were difficult — if not impossible — to identify in the past.

The issue: When organizations adopt new technology, security is generally not priority number one. This is often the case with AI and ML. In the rush to adopt these innovations, cybersecurity is set aside so businesses can begin enjoying the benefits of them as soon as possible.

This is a big risk when it comes to AI and ML. That’s because they require more data, and more complex types of it, than other technology. Add to this the fact that artificial intelligence and machine learning systems are usually developed by mathematicians and data scientists who are good at what they do, but aren’t cybersecurity experts. On top of this, the data volume and processing requirements for AI and ML make it necessary for cloud platforms to be used to handle much of the work, which adds another layer of cyber security vulnerability.

Most adopters of AI and ML know they face hacking and security risks, but many aren’t sure how to deal with them. According to a survey from Deloitte released earlier this year, 62 percent of adopters view cybersecurity risks as a major or extreme concern, but only 39 percent of them report feeling prepared to handle them.

AI and ML data related risks.

As mentioned above, AI and ML systems require a lot of data to generate insights. This includes:

  • Training data that allows the systems to build predictive models
  • Testing data to assess how effective the models are
  • Transactional or operational data that powers the systems.

Most companies are aware of the value of — and vulnerabilities associated with — transactional and operational data. However, training and testing data also has sensitive information associated with it, which means it must also be protected.

The good news is that many of the things organizations do to protect the data used by more standard business technology systems can be applied to AI and ML solutions. These include anonymization, tokenization and encryption. In addition to these things, access to the systems should be limited to those who really need it, people should be required to log-in in order to work on them and use should be closely monitored.

Another important consideration is to only use the data absolutely needed to power the systems and generate meaningful insights. Many data scientists are tempted to pack their solutions with all the information possible to figure out what they can do with it. They’re starting with the data and using it to build a system that does “something” with it.

Instead, AI and ML systems should be created with specific purposes in mind and loaded only with the data required to accomplish them. This will help limit the damage that could happen should a cyber incident occur.

Another thing to pay attention to is how AI and ML solutions interact with other software, systems and technology. Even if the actual AI and ML systems are secure, the connections that feed them with data, and receive information from them, are often vulnerable. Hackers frequently exploit these to breach the systems. This is especially true for connections to third party data sources.

A final vulnerability related to these systems is the people who work on them. Scientists and mathematicians generally know very little about cyber security and protecting data. Providing them with the same training as your other employees will go a long way toward preventing a hack. If you don’t have a company-wide cyber security education program AND you’re developing AI or ML systems, it’s time to implement one. Your organization is too advanced to leave data protection to chance.

Additional ways to secure AI and ML systems.

AI and ML solutions are becoming a part of the everyday way of doing business for many companies. Usage of them will increase, they will become more powerful and use more and more data. This will leave companies that aren’t prepared to handle the unique security needs of them vulnerable to data breaches that could be costly and harm their reputations.

Here are some added steps you can take to protect your AI and ML systems:

  • Maintain a complete inventory of all of them, along with the data that runs through them.
  • Make AI and ML a part of your overall risk management efforts.
  • Put an experienced professional in charge of managing AI and ML system risks. If you can’t find one, turn to the experts at GeeksHD to get the help you need.
  • Regularly monitor and test your systems looking out for vulnerabilities and unusual activity. It’s a smart idea to leverage an external resource like GeeksHD to audit your systems and conduct ongoing vulnerability testing.
  • Do your due diligence before working with any AI vendors to make certain they have solid reputations and knowledge of system security.
  • Set up a committee to ensure AI and ML security policies and procedures are being carried out and to update them as needed.

Focusing on the security of your AI and ML systems will help ensure that they will power your operation to the next level and not bring it down.