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  • Generative AI holds immense transformative potential but also introduces substantial data security risks.
  • Risks include insider breaches, AI-based cyberattacks, and potential compliance fines.
  • To embrace generative AI securely, organizations must prioritize novel security solutions tailored to AI innovations.
  • Key investments include data loss prevention, behavioral analytics, and active learning.
  • Acting now to bolster generative AI data security can provide a competitive advantage and ensure innovation remains positive, not detrimental.

The impact of generative AI on the business world is undeniable. This transformative technology is poised to revolutionize industries much like the internet did, with promises of heightened productivity and cost efficiency across the board.

But for all its potential, generative AI also creates immense data security and compliance risks that could damage an organization’s reputation, trigger hefty compliance fines, and even erode the bottom line. 

Organizations must prioritize cybersecurity to fully capitalize on the potential of generative AI while safeguarding against these pitfalls. This means striking a delicate balance between embracing innovation and ensuring robust security measures are in place.

Here’s a closer look at what to do. 

Generative AI: The data security risks 

Much has been said about the transformative power of generative AI. From marketing to customer service to fleet management, AI tools can supercharge efficiency, productivity, and accuracy. 

With so much focus on the benefits of AI, it’s easy to overlook the drawbacks. Adopting generative AI tools unlocks several data security risks that organizations must contend with, which we’ll look at in more detail below. 

Insider breaches

Generative AI tools operate by analyzing a variety of user inputs, spanning text, images, and audio. While queries containing public information pose minimal risk, those with sensitive data, such as personally identifiable information (PII) or confidential source code, present a significant threat of information leakage.

This risk stems from the inherent nature of tools like ChatGPT. As these AI models strive for greater accuracy and usefulness, they continuously learn from the data they are fed. Consequently, once entered, confidential information becomes ingrained in the neural network’s framework, posing considerable challenges for security control.

Unfortunately, many employees are already sharing confidential data with platforms like Bard and ChatGPT. As Fast Company research shows, 62% of employees have entered information about internal processes into generative AI tools, 48% say they’ve entered non-public company information into these tools, and 38% say they’ve put customer information into them.

This trend raises serious concerns for businesses regarding the inadvertent exposure of confidential information on AI platforms, and the compliance fines and reputational damage that come with it. 

Remember, too, that these concerns are not merely theoretical. Last year, an engineer at Samsung accidentally uploaded sensitive code to ChatGPT, resulting in the unintended exposure of confidential information.

AI-based cyberattacks 

AI’s efficiency benefits don’t just help businesses – it can help organized crime gangs, too. 

From lowering the barrier to entry for novice cybercriminals to enhancing the sophistication of phishing emails and data poisoning attacks, there are endless ways cybercriminals can use generative AI for evil doing. 

On top of that, we must remember that generative AI tools rarely operate in a silo. Where  third-party applications use a generative AI API, there is always the potential risk of compromise. 

If a hacker managed to hijack a third-party application, they could then access  sensitive information or even execute actions on behalf of users.

Compliance fines 

Regulatory bodies are scrambling to enact AI-focused regulations that properly protect consumer data. While nothing is set in stone yet, organizations must act now to ensure they acquire adequate governance over data created and ingested by generative AI applications.

However, doing so isn’t easy for several reasons: 

  • Poor visibility: Enterprise data in today’s landscape often exists in unstructured formats, scattered across various platforms including emails, cloud applications, and databases. The task of locating and organizing this dispersed data is far from straightforward, presenting a considerable challenge for organizations. Complicating matters further is the critical need to identify sensitive information within this vast expanse of data and ensure it receives adequate protection.
  • Data quality: AI models’ opaque operations add an extra layer of complexity to data governance, particularly in tracking the origin and lifecycle of data. A recent article from The Washington Post illuminates a concerning revelation regarding Google’s Bard, indicating that 45% of its training data was sourced from unverified origins. 
  • Data mapping: Even in the absence of generative AI, achieving successful data mapping remains a formidable challenge. Manual processes, inherent complexity, and data silos all contribute to the difficulty in establishing a comprehensive and reliable mapping process. With the introduction of generative AI further complicating matters, organizations are increasingly recognizing the imperative of adopting robust strategies and tools to navigate the intricacies of modern data governance effectively.

How to embrace generative AI securely 

To achieve generative AI data security, organizations cannot do what they have always done. Novel innovations require novel security solutions. 

In essence, organizations will need to fight fire with fire: using generative AI to secure generative AI. This is the only way to achieve the speed and precision needed in the AI-driven business world. 

Of course, all AI initiatives are an investment. Adopting expensive AI-infused security tools that take months to rollout only increases the risk of pilot failures. That’s why companies should focus on low-risk, high-value generative AI security tools to begin with. 

Here’s what we recommend investing in as an urgent priority to bolster generative AI data security:  

  • Data loss prevention: Harnessing the power of natural language processing (NLP) significantly enhances the efficacy and precision of data loss prevention (DLP) solutions. Through NLP, organizations can streamline the process of discovering, categorizing, and safeguarding unstructured data within collaborative SaaS applications and AI-driven tools. 
  • Behavioral analytics and anomaly detection: Generative AI can analyze user behavior at lightning speed and in real-time, establishing a baseline of typical activities. This enhances the capability of security tools to swiftly identify potential threats and instances of data exfiltration in real-time.
  • Active learning: Generative AI is transforming the fabric of security awareness programs by delivering point-of-violation training to users. For instance, Polymer DLP offers active learning to users engaging in risky behavior, resulting in a notable reduction in repeat violations within just days. This proactive approach to user education empowers organizations to create a culture of security, while mitigating the risks of shadow AI.

Don’t wait to secure generative AI 

The tools to fortify generative AI are already out there. Don’t wait to bolster your data security. Take action now and you can gain a competitive advantage over other players in your sector, ensuring that generative AI leads to meaningful innovation instead of harmful data breaches. 

Polymer is a human-centric data loss prevention (DLP) platform that holistically reduces the risk of data exposure in your SaaS apps and AI tools. In addition to automatically detecting and remediating violations, Polymer coaches your employees to become better data stewards. Try Polymer for free.


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