Todd Moore | Vice President, Data Security Products, Thales
The past year has seen a stratospheric improvement in artificial intelligence (AI) technology. The launch of the first AI chatbot tool in December 2022 saw AI transformed from a niche, quasi-dystopian sci-fi trope to one of the most talked about topics on the planet. In just a few months, the chatbot gained 100 million users, countless headlines, and even its first security breach.
Since then, a debate has raged on the impact AI could have on our daily lives. Some have noted AI’s potential to drive revenues and cut costs, streamline workflows, and free up time for people to focus on more important things. Others have warned that AI could put thousands of staff out of work, debilitate critical thinking skills, and even give rise to a new generation of have-a-go-hackers.
While it is currently unclear whether AI will be a force for good or evil, Thales believes that generative AI has the potential to transform the cybersecurity landscape. Innovations in AI-driven security can help detect and prevent threats, streamline security workflows, and democratize cybersecurity knowledge, empowering more individuals to help protect their organizations. Generative AI is fueled by collaboration. Machine learning (ML) tools leverage data to learn and improve without significant human intervention. The more data users input into a machine learning tool, the more it improves. This concept benefits AI cybersecurity tools; the more security intelligence an ML tool has, the better it is at detecting and responding to threats. With this in mind, we are proud to announce an extension of our partnership with Google Cloud. Google Cloud’s leading AI capabilities will further enhance the performance of Thales CipherTrust Intelligent Protection, accelerating our generative AI strategy.
The Thales CipherTrust Data Discovery and Classification Machine Learning (DDC ML) tool is an ML feature set to enhance conventional discovery and classification typically achieved via regular expression pattern matches. ML techniques augment by deploying semantic context to discover and classify sensitive information from various corporate document repositories while ensuring sensitive data remains within these established and secure premises. With ML, documents are organized into categories and subcategories, using named entity recognition to discover a range of sensitive information types. The tool combines these results with the regular, expression-based output into consolidated reports.
The Thales partnership with Google Cloud means our DDC ML tool will soon support vector-based similarity search with some enhancement from Google Vertex AI and labeling for data privacy features.
To learn more about how Thales and Google Cloud are working together to enhance generative AI security features , read the new blog post from Google Cloud.