Navigating the Waters of Generative AI in Enterprises: The Copilot Paradigm
By Tyler Young, CISO of BigID
Tyler Young
Microsoft’s Copilot launched last November with much fanfare as many enterprises are increasingly turning to advanced tools like Copilot and other generative AI to streamline processes and foster innovation. However, while there are a lot of benefits to using Copilot, before deploying any AI systems, IT leaders need to make their own evaluations and create safeguards to carefully harness the full potential of generative AI.
Here are the things IT leaders need to consider before deploying Copilot or any generative AI systems.
Why is a Closed-Loop AI Model Better?
Copilot stands out when it comes to security because it leverages closed loop AI models, in a single tenant deployment infrastructure. This essentially means customers using Co-pilot have the ability to train their AI model on their own data set in their own Azure environment, which eliminates some of the risk of sensitive data being input into a model and becoming shared across customers or geolocations.
By leveraging closed loop AI models, companies can protect themselves somewhat and have some safeguards in place to ensure their sensitive data is not exposed to another MSFT customer or used to train other open source models.
However, a closed loop AI model isn’t the only safeguard companies need to employ. There are other steps organizations should and need to take before deploying any generative AI systems, like Copilot or others.
Multi-layered Approach to Generative AI
The best strategy to have when deploying generative AI involves a multi-layered approach. One of those approaches is that companies should begin to purchase business licenses to AI models to provide sanctioned use of AI (limiting access based on user roles, etc.).
Another critical component of this evolving strategy is employee education and awareness. The human element often plays a significant role in data security. By ensuring that all staff, especially those interacting with AI systems, are well-versed in best practices for data management and aware of the potential risks, companies can create an additional layer of defense against data breaches. Training programs should be regularly updated to reflect new developments in AI technology and emerging security threats.
Enterprises should consider engaging in active collaboration with AI developers and security experts. This partnership can help organizations stay up to date and gain invaluable insights into the specific workings and vulnerabilities of generative AI systems. By understanding the mechanics behind these AI tools, enterprises can better tailor their data management and security measures, ensuring that they are both effective and specifically suited to the nuances of the AI technology they are utilizing.
Lastly, there’s an emerging need for continuous monitoring and auditing of AI interactions, which dataset is being used to train AI, who has access, etc. One of the biggest risks to generative AI systems is its need to be trained on vast amounts of data, either scrapped from the internet or inputted in by users, which introduces a whole host of security and privacy concerns.
In embracing generative AI technologies, enterprises need to have a forward-thinking data governance strategy. As the landscape of AI continues to evolve, so too must the approaches to managing and securing data. Organizations need to ensure they deeply understand the data that they are sending to AI models. They can accomplish this by having data repositories or staging areas where they can scan their data for sensitive information and remove this data before it’s sent to the AI model.
While this AI digital transformation is essential for a modern business, it does introduce new risks and requires a completely new approach to protecting their organization. Organizations need to prioritize data protection, invest in security measures, and take proactive steps to mitigate vulnerabilities.
Enhancing Data Security in the AI Era
While Copilot’s closed-loop AI model represents a significant step forward in secure generative AI deployment, it is not a silver bullet for all data security concerns in enterprises. A balanced and comprehensive approach, encompassing the purchase of business licenses, meticulous data management, and robust security protocols, is imperative for the safe and effective integration of generative AI into corporate environments.
As enterprises navigate this new terrain, a cautious yet proactive stance will be key to unlocking the transformative potential of AI while safeguarding their most valuable asset – data.
We need to embrace AI and develop a secure methodology for leveraging it. Companies need to look at controlled use of generative AI models internally to protect against any potential data leaks. By controlling access in terms of what data is fed into the LLM, who in the organization are allowed to use these chatbots, and what access these chatbot have, companies are better equipped to protect against these types of data leak.
Wanda Rich has been the Editor-in-Chief of Global Banking & Finance Review since 2011, playing a pivotal role in shaping the publication’s content and direction. Under her leadership, the magazine has expanded its global reach and established itself as a trusted source of information and analysis across various financial sectors. She is known for conducting exclusive interviews with industry leaders and oversees the Global Banking & Finance Awards, which recognize innovation and leadership in finance. In addition to Global Banking & Finance Review, Wanda also serves as editor for numerous other platforms, including Asset Digest, Biz Dispatch, Blockchain Tribune, Business Express, Brands Journal, Companies Digest, Economy Standard, Entrepreneur Tribune, Finance Digest, Fintech Herald, Global Islamic Finance Magazine, International Releases, Online World News, Luxury Adviser, Palmbay Herald, Startup Observer, Technology Dispatch, Trading Herald, and Wealth Tribune.