With the exponential advancement of artificial intelligence (AI) technology, speech recognition, and natural language processing (NLP), organizations have unlocked great developments in the power of voice data. However, as with any growing technology, many concerns remain.
Users have questions they need answered. How is their data being handled? Is it being used for training AI programs without their consent or knowledge? And can they be sure their sensitive information is safe?
Companies like AssemblyAI, which specialize in AI-powered speech-to-text and speech understanding, are aware of these concerns and questions. So, let’s look at the security concerns behind speech AI and how they’re addressed.
AI Security Concerns
As AI-powered speech-to-text becomes more popular, potential security concerns also grow. The biggest concerns focus on three factors: confidentiality, integrity, and availability.
Essentially, this means that customers want to make sure their data can only be accessed by those they allow, the data is being used for its intended purposes, and it’s available to them when they need it. We’ll cover these topics in the next section.
When we break those concerns down, perhaps the biggest element is how data is saved and utilized. Audio files and other voice data can often contain sensitive or personal information, such as social security numbers, financial data, or proprietary data. Users need to know that this data will remain secure and that it won’t be used as part of the AI tool’s training data.
Security Measures to Consider
How can companies assure users that their personal and proprietary information is safe? The right security measures can make all the difference.
First, consider the standard security frameworks for your industry. Each industry has a set of cybersecurity frameworks that provide a set of standards and best practices, such as the CSA Cloud Controls Matrix for cloud computing or PCI DSS for managing payment information. These standard frameworks create a good starting point for security, but they are not a silver bullet. Standard frameworks provide industry-standard controls to help prioritize controls to be implemented depending on how audio files are intended to be used.
One key control that is present in multiple recognized frameworks is encryption. Encryption is vital for ensuring all voice data remains confidential and protected. There are a few different forms encryption can take, such as end-to-end encryption (where the data is encrypted on the sender’s side and decrypted on the recipient’s side) and Transport Layer Security (TLS). Organizations should ensure that encryption protocols algorithms used are current, and difficult to exploit.
Role-based access controls help protect sensitive data by ensuring that access is granted to only authorized individuals. Enabling role-based access locks out anyone who doesn’t need the data for their jobs, thus reducing the risk of it being improperly accessed or misused.
Ensuring that role-based access controls are properly implemented helps organizations limit the reach of prying eyes, and acts as a strong control against data theft. Coupled with logging and data loss prevention (DLP), organizations can maintain the integrity and non-repudiation of actions taken on data that they are entrusted with.
Finally, it’s recommended to have clear data retention and usage policies. People remain critical facets to any security program, and policies help ensure that everyone is aware of their baselines roles and responsibilities. Customers should know how their data is stored, encrypted, and used so they are knowledgeable and aware of their shared responsibilities in protecting the data of their end users.
Evangelizing transparency helps organizations improve relationships and holistically understand risks associated with using services. Ultimately, risks need to be measured against business value, so knowing how AI-powered speech-to-text services use audio and how they protect it is crucial to securely integrating AI into product offerings.
Data Security Practices to Look For
With all this in mind, what are some of the best practices for speech-to-text data security? AssemblyAI specializes in AI-powered speech recognition, and they have some key recommendations when looking at speech-to-text APIs.
The first thing to consider is what happens to both the voice or video data and the transcriptions. Any voice or video files should be deleted when they are no longer needed to ensure they’re not left vulnerable, while the transcription files should be encrypted to keep them secure (with the option to delete them at your discretion).
Additionally, look at how the speech-to-text APIs handle sensitive data. AssemblyAI, for instance, can identify and redact sensitive information within the transcription to ensure sensitive information is not unintentionally exposed. Any AI-powered speech-to-text API should include data handling policies and procedures so you know exactly how data is safeguarded.