Secure data-sharing agreements—which define the terms, responsibilities, and restrictions for data sharing—open up possibilities for cross-organizational collaborations, where data from different entities can be aggregated and used to improve models.
Consider a scenario where multiple pharmaceutical companies specializing in oncology research join forces with academic institutions. These groups can securely pool anonymized data related to clinical trial results, safety profiles, and other critical information. They can then collectively train models to accelerate the identification and development of potential cancer treatments.
Dynamic role-based access control (RBAC) allows organizations to assign and adjust access permissions dynamically based on the evolving needs and roles of individuals within a business. RBAC ensures that individuals have access to content based on their specific roles, responsibilities, and the context in which they operate.
In addition, walled gardens can effectively secure GenAI systems by creating controlled environments that restrict access and privileges to authorized personnel. Within a walled garden, a company typically exercises strict control over data and algorithms, allowing limited or no access to third-party developers or external entities. These environments empower organizations to implement a range of flexible security measures, including multi-factor authentication, user identity management, and encryption mechanisms.
A walled garden on your own servers, or a secure tenant in a cloud environment, offers a safe way to consolidate enterprise data within a proprietary LLM. This approach ensures the secure integration of enterprise data within the generative capabilities of the LLM while effectively addressing cybersecurity requirements and preventing data leakage into the generally available LLM foundation model.
Enterprises can benefit from a walled garden in AI-powered data analytics, allowing them to consolidate and analyze proprietary data within a secure, controlled environment. This facilitates the extraction of valuable insights, pattern identification, and data-driven decision-making while ensuring privacy and security. Walled gardens enhance personalized customer experiences by leveraging AI algorithms to analyze customer data, preferences, and behavior, enabling tailored recommendations, personalized content, and targeted advertisements. Enterprises can also utilize walled gardens to develop industry- and company-specific AI applications, leveraging controlled environments to train models on proprietary datasets and incorporating domain-specific knowledge as a competitive differentiator.
Regardless of the approach to GenAI, organizations need clear visibility into how models are being used, what data they’re trained on, and what outputs they’re generating.
Thrive amid change
Evolving security measures are crucial to adapting to the risks of GenAI, but it’s equally important for businesses to adapt strategies and practices to effectively leverage this technology. One of the most critical aspects of this adoption is effective communication at every level of an enterprise, according to Boston Scientific's Sullivan.
The recent buzz around consumer-friendly tools like ChatGPT or Bard means many people have already seen the power of GenAI. “You’re not going to be able to control their usage that much,” Sullivan says, as the growing number of free tools and resources make the technology much more accessible.
Foster open and transparent conversations about GenAI with your team. Recognize that employees are already aware of the potential of GenAI and may have a desire to use it to improve their work processes and outcomes. Attempting to tightly restrict usage could be counterproductive. Instead, encourage a collaborative approach where employees and stakeholders are actively involved in experimenting with and adapting GenAI technologies.
As Sullivan explains, “We've got to just accept that AI is here. It's going to gain popularity and we need to meet people where they are.”