How AI Training Data Can Be A Security Threat To Your Company?
The race into Artificial Intelligence (AI) flying field is becoming more expeditious with new and more innovative developments across the world. And such competitive approach also posing a new threat to companies working on such projects.
Every coin has two sides — AI is also brining such risk which needs to understand timely. Actually, I’m talking about the training data that is primarily used in model training and development. Though, there are many security issues with AI-based model development and applications developed using this technology.
But training data is the information you upload into the algorithm to create your AI model is highly vulnerable towards the hackers and fraudsters, if they manage to access that.
“If such crucial data landed into wrong hands, it can be mischievously manipulated to crash your computer networking system or breach the privacy and security of your company”.
Your AI-based application is also equally susceptible to hacks or data leaks, hence you need to protect your data and we have discussed right here what are the major issues you need to consider while working on such AI-based projects.
Are you storing your data at a safe place?
If you are using cloud-based AI services where you upload your labeled data on such online data servers. You don’t know if this cloud is directly under control of your service provider, or do they own the entire stack. Moreover, always remember that maybe your cloud is secured, but not necessary it is used in a secured manner.
As per the global research and advisory firm, Gartner’s prediction up to 2022, at least 65% of cloud security failures will be occurred due to customer’s fault.
Hence, you need to make sure use the secured cloud-based AI services and also need to audit threats and risks that your employees' usage may pose to the cloud.
Who possess your training data?
This is a big question — Who owns your training data? Actually, when you use a cloud-based machine learning algorithm to develop an AI-based model, you have to upload your training or testing data into the service’s algorithm.
Let’s take an example — Suppose you are using an NLP look through your call data which a proprietary data providing a competitive edge is a very sensitive data that you are legally bound to protect from unauthorized users.
Here, you need to make sure you do not permit any rights to your data and that you will own the final model created with that data. So, carefully check the “terms of service”, license agreements and other fine print before signing up for using any such cloud-based AI technology.
And if you find anything unfavorable in their “terms of service” or ambiguous you can terminate the service and go with other reliable service providers.
Similarly, you also need to make sure you are not using unmoderated publicly available datasets for your AI or ML training data, as this can open you up to malevolent data source that can seriously poison your application or fail the model.
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Do you clean up the data from such servers?
Once your AI model training is completed, it is necessary to delete all vestige from the servers used in training. Finally, you are going to use the AI application and whenever you kick-off your AI project, it’s easy to carry off with the possibilities it opens out.
Usually, managers emphasize speed and innovation but take your time to understand what is going on behind the scenes. This will definitely help you in long-run in securing your application from mishandling while ensuring the privacy of the customer’s data.
Acquiring the machine learning training data is not difficult but keeping it secured and private during the model development is very important for every company enthusiastic in such developments.
Thus, consider such aspects while working on such projects, especially when you use a cloud-based algorithm to build an AI model and stay ahead in the competitive market without losing your privacy and data safety.
This article was originally published at www.vsinghbisen.com