How to Navigate Data Security in the GenAI Era

Since its mainstream emergence in 2022, generative AI has triggered a seismic shift in data management and security. It is estimated that one in four employees now uses genAI apps daily, often unbeknownst to their employer and IT team. This raises concerns, as genAI is designed with a voracious appetite for consuming both mundane and sensitive data.

Effectively securing your data as genAI becomes prevalent is a strategic imperative. Neglecting data security within the purview of genAI can lead to catastrophic consequences for your business. Our new ebook “Securing Gen AI for Dummies” equips you with useful information to balance the use of genAI tools in your organization within the framework of robust data security practices. 

Gen AI: A quick introduction

At the core of genAI apps, such as ChatGPT, Jasper, and Google Gemini, are large language models (LLMs) powered by advanced neural networks. LLMs enable artificial intelligence (AI) technologies, such as machine learning (ML), deep learning, and natural language processing (NLP), to interact in ways that closely mimic the human thought process and language patterns. At its very core, an LLM utilizes a large volume of sample data to train, recognise and interpret human language and data sets. And it is how an LLM gathers and utilizes new sample data that is at the very foundation of an organization’s data security challenge when using genAI powered apps.

While the efficiency and potential for innovation that come with genAI are alluring, there are significant concerns when it comes to data security and ethics. 

The security implications of genAI

The biggest security issue with genAI is the risk of accidentally exposing sensitive data. Employees using a genAI tool may give it important, proprietary, and confidential data without realizing the dangers of possible exposure. Employees circumventing security best practices only exacerbate the problem of data loss, coupled with the potential of creating copyright infringements and legal disputes.

At the same time, a data exposure incident in a third-party genAI application can reach an organization’s core applications connected to them leading to IP theft. GenAI has also opened up new avenues for risks such as data scraping where vast amounts of sensitive data are aggregated from various sources and misused. Another subtle yet potent threat is data poisoning where a genAI tool is intentionally fed malicious or inaccurate data to produce compromised or inaccurate outputs. 

Similar to data poisoning, predictive models in genAI that anticipate future outcomes from existing data patterns can become vulnerable to malicious exploitation, opening avenues for data distortion and theft. Another cyber threat to genAI are prompt injection attacks by attackers who use clever inputs to make AI models reveal sensitive information or take unauthorized actions. 

The emergence of AI-driven threats represent the dark side of genAI evolution. But organizations can set operational boundaries to ensure innovation with genAI flourishes within a robust framework of safety and privacy. 

Modern SSE technology helps secure genAI

The concept of zero trust is a cornerstone of modern cybersecurity technologies. In the context of genAI, this means not restricting the use of these tools, but instead ensuring every interaction with them is monitored. Keeping the safety of sensitive data top of mind, this means every interaction with a genAI tool must be continuously verified to confirm that it is not being inadvertently shared with a genAI platform, regardless of the type of tool or platform being used. 

Zero trust principles are the basis of a properly architected security service edge (SSE) platform that consolidates data, SaaS, browser, and private app security into one unified cloud-based platform. SaaS security solutions like cloud access security brokers (CASBs) are a critical component of an SSE platform that provide deep visibility into lurking dangers within cloud and SaaS services, including potential vulnerabilities with new genAI apps. These solutions not only identify genAI apps in use but also the associated risks, data protection concerns, user behaviors, and compliance implications help organizations make informed security enforcement decisions, tailoring their security protocols to address specific threats. Netskope’s industry leading SaaS security solution harnesses the power of genAI for rapid app discovery and risk categorization providing advanced insights into app risks, security posture, and behavior. 

As your organization explores the potential of generative AI, keep in mind that engaging with this technology is similar to nurturing a bright, inquisitive child full of limitless possibilities. It demands careful guidance, ensuring each interaction is both secure and well-informed, fostering healthy development every step of the way. For detailed guidance on how you can go about securing genAI in your enterprise, download a free copy of our newest dummies ebook “Securing Generative AI for Dummies”.