While adopting AI can be positive from a business efficiency and competitive standpoint, there’s a lack of acknowledgement regarding the dangers of these models. Recent research indicates that 67% of senior IT leaders are prioritizing GenAI for their organizations within the next 18 months. A third of these leaders (33%) consider it their top priority. However, as things stand today, AI is like the wild west. Rogue AI models are exposing sensitive data without regulation, creating an environment for accidental data breaches and misuse.
Generative AI (GenAI) specifically is being adopted faster than policy and data security can keep up. With the current U.S. administration ushering in regulatory changes and uncertainty, the security landscape remains fragmented and unpredictable. Organizations should seek to adopt security strategies that are adaptable and future-proof.
As language learning models (LLMs) become more integrated into company systems through GenAI, they essentially become unintentional data exfiltration points because these tools (and in some cases, users) cannot currently decipher between what data is sensitive, and what is not.
So how should organizations shore up this exfiltration point? Well, as the saying goes, “With great power comes great responsibility”. GenAI is possibly the greatest example of that mantra, making it critical for organizations to implement policies that ensure the security of their sensitive data before, if, and when their employees may seek to adopt a GenAI model to leverage some of their undeniable workplace benefits.
The Double-Edged Sword of GenAI
GenAI models fall into two primary categories: indexing/crawling systems, and input-based systems. Examples of indexing systems would include systems like Copilot, which have access to and learn from everything within your Microsoft 365 environment. The second category refers to models like OpenAI’s ChatGPT, systems that learn based on information provided to it by the user.
Despite the value of an AI tool that adds business value such as automating tasks, enhancing creativity and driving efficiencies, each type has unique risks. Indexing systems that crawl environments for information cannot distinguish between sensitive information and other information. Similarly, not all users are aware of what information is too sensitive to be input into an easy-to-use tool.
The risks of data leakage don’t stop at the company level. Many organizations rely on third- and fourth-party suppliers to process or manage sensitive data, and these external vendors are increasingly adopting AI tools themselves. When a third-party supplier uses GenAI, they might inadvertently expose your company’s sensitive data to AI models over which you have no control.
For example, if a customer service provider uses AI to manage client interactions, sensitive customer data could be processed by the AI model and exposed in ways that were not anticipated. This creates an additional layer of risk: data is not only shared within the company but is also passed through external systems that might not have the same level of security protocols in place. Without a clear understanding of how third-party AI models handle data, companies might unknowingly expose sensitive information to malicious actors or unauthorized systems.
For these reasons, AI is essentially an unintentional exfiltration point that could lead to serious consequences. There are faults in GenAI models, and faults in how companies continue to adopt faster than their data security posture can keep up. In fact, Forrester reported that 60% of people use their own AI tools in the workplace, putting their company at further risk.
Mitigating Data Security Risks in the Age of GenAI
Security leaders have spent years waging war against an ever-growing list of challenges, including over-provisioned access, fostering a security-conscious culture, navigating complex regulatory compliance, insider threats, robust data privacy and protection, and the inherent risks associated with third-party collaboration. Now, the rise of AI has added fuel to the fire, exposing cracks in your data security strategy while regulators raise the stakes with stricter compliance mandates.
As organizations continue to embrace GenAI, it’s critical to implement a data-centric security framework to mitigate the risks of data leakage. Here are three suggested pillars to build this framework around:
- Establish Strict Access Controls
Organizations must ensure that sensitive data is only accessible to authorized individuals. By implementing role-based access controls, companies can limit who can interact with proprietary information, thereby reducing the risk of accidental or intentional data exposure.
- Educate Employees about Data Security
Even the best security protocols don’t always bypass human error. Employees should be given regular security awareness trainings that focus on the importance of safeguarding intellectual property and customer data, as well as the risks of inputting sensitive information into AI systems.
- Continuously Monitor AI Interactions
Organizations should implement continuous monitoring of AI interactions. This includes tracking how AI tools interact with company data—what data is being accessed, processed, and potentially shared. Monitoring systems can provide real-time alerts if data is exposed or mishandled, allowing companies to quickly respond to potential breaches before they escalate.
Tackling GenAI with Data Centric Security
The future of data security amid GenAI requires state and federal legislation to back it. Bills like the recent California AI bill may be a step in the right direction if we ever want to stop the wild west of GenAI data sharing, but much more needs to be done to create a legal framework that governs the use of AI and protects sensitive data across industries.
The current U.S. administration recently rescinded the prior administration’s AI executive order, which reflects a strategic move to deregulate the AI industry to encourage innovation and economic growth. However, that doesn’t come without consequences, and a lack of leadership around AI safety significantly increases the risk of long-term challenges that could outweigh these benefits.
Without safeguards in place, the accelerated deployment of AI technologies may lead to critical vulnerabilities in national security, such as heightened risks of cyberattacks and exploitation of sensitive systems. It also raises serious concerns about consumer privacy, algorithmic bias, and the ethical use of AI, as companies prioritize speed over responsibility. This also opens the door to the potential risks associated to eroding public trust in AI technologies, while creating a fragmented regulatory landscape that complicates compliance for businesses and fosters legal uncertainty.
GenAI is not just a tool of the future, it’s shaping the present. We all have an opportunity to push through technologies that will support data-centric security initiatives while ensuring a business can take advantage of the AI technologies they’re so interested in adopting. AI models are designed to process data, though sometimes carelessly. The future of data security in the age of AI will be shaped by those who act early, establish strong security practices, and push for meaningful regulation. Is your organization ready for what comes next?
