The wave of growth and innovation propelled by artificial intelligence and machine learning is undeniable. However, the true potential of the AI revolution, from deep learning to predictive analytics, can only be unleashed when models—including those based on neural networks, logistic regression, and generative adversarial networks—can be trained and deployed on the best existing data. This includes utilizing sensitive data that might be outside of your organization, making accessing and using it fraught with security and privacy challenges.
Enterprises already have difficulties acquiring data for statistical analytics, let alone for use in AI. With AI Vendors’, growth is hampered for the same reasons; clients’ concerns about protecting sensitive data, and their own concerns around IP protection. As a result, AI vendors struggle to deliver the outcomes they promise.
What if your teams could build artificial intelligence models with the best practices in data security and privacy, utilizing sensitive data, even if it’s not owned by your organization? What if you could deploy and monetize those models while protecting your IP? What if you could prove the models work on your customers’ sensitive information earlier in your sales process, to help show value and create a competitive advantage? This is the promise of Privacy Protected AI Collaboration.