In recent months, we’ve seen an explosion of AI coding assistants. They do everything from explaining code to writing unit tests. Engineering teams at every company want to use them. While these tools can improve test coverage and detect code smells, for many legal teams, they are a nightmare. The technology is new, and the legal risks untested.
When evaluating AI coding providers, look for four key terms that will mitigate the primary risks associated with the tools.
Be clear in your contract with your AI coding provider that your data (inputs and outputs) will not be used to train models.
When a model trains on your data, the model provider keeps your data for a period of time. If and when the model provider suffers a security breach, depending on the amount of sensitive data—such as personal or customer data—in your code, you may suddenly have a notifiable security incident on your hands.
Per best practices, code shouldn’t contain these types of data. But when it does, contractual and regulatory obligations may require you to notify affected users and customers and government agencies. Security breaches can also trigger customer termination rights, depending on your customer contracts.
In addition, data subject rights may apply to any personal data processed by the model....
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