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Summary

  • AI tools like ChatGPT and Bard boost productivity but introduce privacy and security risks.
  • Data leakage, compliance challenges, and credential compromise are major concerns.
  • Organizations must implement strong data governance and security measures to mitigate risks while leveraging AI benefits.
  • Low-code bi-directional DLP tools can assist in preventing data leakage and boosting compliance.

Generative AI tools are reshaping the future of workplace productivity. Innovations like Bard and ChatGPT are already revolutionizing workflows, driving efficiency and growth across industries. According to Deloitte, generative AI is a powerful growth catalyst for businesses of all sizes. From streamlining customer service to enhancing HR operations, the possibilities are endless.

However, with great power comes great responsibility—and security risks. Generative AI introduces familiar threats like data leakage and theft, but in new and complex ways. Some organizations have reacted by banning these tools outright, while others charge ahead without considering the security implications. Neither approach is ideal.

But there’s a smarter path forward. By implementing robust data governance, organizations can harness the competitive edge of generative AI while minimizing cybersecurity risks.

In this guide, we’ll explore how to strike the right balance between innovation and security, ensuring your organization thrives in the AI-driven future.

Understanding generative AI

Generative AI is a cutting-edge branch of artificial intelligence that empowers machines to create original and valuable content, from images and text to music and beyond. 

Using neural networks that mimic the complex workings of the human brain, generative AI learns at an astonishing pace, solving problems in seconds that would take humans hours.

Fueled by vast amounts of crowd-sourced data, generative AI can tackle intricate tasks with lightning speed, making it a game-changer across multiple domains. Here’s a glimpse of its transformative applications:

  • Image synthesis: Generative AI crafts hyper-realistic images, revolutionizing art, design, and advertising with stunning visual creativity.
  • Text generation: From customer support to marketing, generative AI is redefining content creation, producing high-quality text that drives engagement and efficiency.
  • Music composition: Musicians are exploring new horizons with generative AI as their creative partner, composing unique melodies and harmonies in record time.
  • Data augmentation: By generating synthetic data, generative AI enhances machine learning models, boosting their performance and generalization for more accurate predictions.

Privacy and compliance challenges

Whether or not your organization officially endorses generative AI, chances are your employees are already using it. And who can blame them? As Deloitte research shows, generative AI tools can significantly boost productivity, creativity, and efficiency.

But with great power comes great risk. Let’s dive into the potential pitfalls and what’s truly at stake.

Data loss

Generative AI tools like ChatGPT thrive on user inputs—whether it’s text, images, or audio. When these inputs are harmless, everyday information, the risk is minimal. 

But when the data includes sensitive details like personally identifiable information (PII) or confidential source code, the risk of data leakage escalates sharply.

Here’s where the challenge lies: AI models such as ChatGPT learn and improve by processing the data they’re fed. This means that once confidential information is entered, it could become part of the model’s learning framework, potentially compromising data security. 

The very mechanism that makes these tools so powerful—their ability to learn from vast amounts of data—also makes it difficult to maintain ironclad security.

As these models continually evolve, the line between safeguarding sensitive information and harnessing AI’s potential becomes increasingly blurred. 

This creates a complex landscape where the benefits of generative AI need to be carefully balanced against the risk of exposing critical data. 

Compliance pitfalls 

As AI rapidly evolves, so too does the regulatory landscape. A patchwork of AI regulations is emerging, with initiatives like the EU’s AI Act and the US Executive Order on AI Safety and Security leading the charge.

But nothing is set in stone yet, and the growing number of regulations at both national and state levels adds to the complexity of deploying AI in full compliance.

Despite the regulatory guidelines still being incomplete and in flux, organizations that proactively adhere to best practices, such as the NIST AI framework, position themselves well to adopt AI responsibly and ethically. These early adopters are setting a strong foundation for navigating future regulatory demands.

Chief Information Security Officers (CISOs) must also recognize that established regulations, like the General Data Protection Regulation (GDPR), already extend their reach to include AI applications.

A notable example occurred in March 2023 when Italy’s Data Protection Authority, the Garante, issued an emergency order temporarily halting OpenAI from processing personal data of individuals in Italy. This decisive action was based on concerns about potential GDPR violations, including issues of lawfulness, transparency, and the protection of data subject rights.

This incident underscores the fact that it’s not just new AI-specific regulations that organizations need to watch closely. Established data privacy laws like GDPR, HIPAA, and CCPA also apply to AI, creating a multilayered regulatory environment that CISOs and risk management teams must navigate. 

Malicious actors

Third-party generative AI platforms are becoming increasingly accessible, but with that convenience comes serious risks—particularly around data theft. One of the most common security gaps is employees reusing passwords across multiple accounts, a habit that makes it all too easy for threat actors to hijack generative AI accounts.

This vulnerability is amplified in the absence of robust monitoring systems. Without vigilant detection and prevention measures, the threat of insider attacks looms large, with potentially catastrophic consequences for your organization’s data security.

To stay ahead of these risks, it’s crucial to implement strong security protocols. Think multi-factor authentication, real-time monitoring, and rapid response capabilities. By putting these safeguards in place, you not only protect sensitive information but also ensure your team can continue leveraging generative AI without compromising security.

Supply chain breaches

Supplier contracts are getting tougher, with stricter demands for certifications and proof of robust security measures before any partnership kicks off. But in today’s AI-driven world, tools like ChatGPT can throw a wrench into the gears of supply chain risk management for both suppliers and customers.

Here’s the risk: if an employee inadvertently shares sensitive client data with a generative AI tool, it could breach contract terms and trigger serious legal repercussions. This isn’t just a hypothetical scenario—it’s a real concern that could undermine trust, damage relationships, and expose your organization to significant liability.

To navigate this evolving landscape, it’s essential to reinforce your supply chain agreements with clear guidelines on AI usage.

Establish strict protocols around data sharing, educate your team on the potential risks, and ensure all parties understand the boundaries. By doing so, you can protect your business from unintended breaches and maintain strong, compliant partnerships in the age of AI.

Technical approaches to protect privacy

Many organizations might not realize that security vendors are now harnessing the power of generative AI to create specialized tools that secure generative AI environments. 

For example, next-gen data loss prevention (DLP) tools are specifically tailored for platforms like Bard and ChatGPT, designed to significantly reduce the risk of data exposure in these generative AI tools.

Here’s a closer look at how these advanced DLP tools work:

  • Bi-directional data discovery and redaction: These tools swiftly scan your generative AI applications, identifying sensitive data in both prompts and responses. When such data is detected, the tools can automatically redact it or block user actions based on your organization’s usage policies, ensuring that confidential information doesn’t slip through the cracks.
  • Enhanced audit efficiency: Need to conduct an audit or respond to an e-discovery request? Next-gen DLP tools make it easy to search and access relevant generative AI interactions, streamlining the audit process and ensuring compliance with regulatory requirements.
  • Active learning for security awareness: These tools don’t just prevent data leaks; they also educate. If a compliance or security policy breach occurs, the DLP system can issue immediate training nudges to employees, turning potential security lapses into learning opportunities and reinforcing best practices on the spot.
  • Internal visibility: Gain deep insights into employee actions with robust logging and auditing features. This level of visibility helps you detect and respond to repeat offenders, compromised accounts, and insider threats before they lead to data breaches.

And the best part? Many of these tools are incredibly easy to deploy, often requiring no code or low code, allowing you to secure your generative AI environment in just minutes.

Future perspectives and evolving regulations

The AI compliance landscape in the USA is anything but straightforward. With no overarching national data privacy law, states have stepped in to create their own regulations governing data protection for companies operating within their borders or serving their residents.

As of now, five states have data privacy laws either in effect or on the horizon for this year: California and Virginia (effective January 1, 2023), Colorado and Connecticut (effective July 1, 2023), and Utah (scheduled for December 31, 2023). Each state’s law is unique, adding layers of complexity to compliance.

For instance, the California Privacy Rights Act (CPRA) is known for its consumer-friendly stance, whereas the Utah Consumer Privacy Act (UCPA) is more business-centric. These nuances make navigating multiple regulations a complex puzzle for businesses.

On top of state-level regulations, the federal government is beginning to explore AI oversight. The US Department of Commerce has recently called for public input on establishing accountability measures for AI, signaling potential future regulations.

In the meantime, organizations can look to existing regulatory frameworks for guidance on AI usage. These frameworks typically focus on data handling and privacy, requiring diligent monitoring of data flows to ensure compliance. Here’s how some key regulations intersect with AI:

  • Health Insurance Portability and Accountability Act (HIPAA): In the healthcare sector, HIPAA mandates the secure handling of patient data. AI tools used in healthcare must adhere to HIPAA’s privacy and security requirements, including robust security measures, access controls, and patient rights to access and correct their health records. Non-compliance can result in significant legal penalties and reputational damage.
  • Cybersecurity Maturity Model Certification (CMMC) 2.0: Managed by the U.S. Department of Defense, CMMC 2.0 is designed to enhance cybersecurity for defense contractors. It sets specific requirements for protecting sensitive defense data and will soon require real-time monitoring of cloud-hosted applications. This means that a data loss prevention (DLP) solution will become essential for managing AI tools in this sector.
  • ISO 27001-2: The updated version of the ISO 27001 standard focuses on information security management systems (ISMS) with an emphasis on data protection in the cloud. ISO 27001-2 includes risk assessment, security controls, and continuous monitoring. Legal teams often interpret these controls to include DLP solutions as part of a comprehensive data governance strategy.
  • Gramm-Leach-Bliley Act (GLBA): This federal law requires financial institutions to prioritize customer privacy and data security. The latest GLBA updates, released in June 2023, introduce new data security controls. Financial institutions using AI systems must comply with GLBA to ensure that customer information remains protected, including proper data retention, usage, and disclosures.

How Polymer DLP can help 

in the complex world of generative AI, securing sensitive data while complying with regulations like GDPR is crucial. Polymer DLP is the ideal solution for achieving this balance, offering advanced features designed specifically for AI environments.

One of Polymer DLP’s key strengths is its bi-directional monitoring capability. This technology provides real-time protection by scanning and analyzing interactions involving generative AI tools such as ChatGPT.

It ensures that sensitive data is never exposed, whether it is being input by employees or inadvertently generated by the AI. This proactive approach prevents data leaks and maintains stringent security controls.

The tool’s robust logging and auditing features further enhance data security. Polymer DLP offers comprehensive insights into employee transactions and AI usage patterns, allowing organizations to track policy violations, investigate potential breaches, and ensure transparency. This detailed oversight is vital for meeting GDPR obligations and other regulatory requirements.

When it comes to e-discovery, Polymer DLP excels by simplifying the process of retrieving relevant generative AI interactions. This capability is essential for handling legal and regulatory inquiries efficiently, making audits and investigations more manageable.

Additionally, Polymer DLP supports real-time user training through its point-of-violation nudges. This feature significantly reduces repeat policy violations—by over 40% in just a few days—by addressing issues immediately and fostering responsible data handling practices.

By integrating Polymer DLP, organizations can effectively secure their generative AI environments and maintain compliance with GDPR. To learn more about how our solution can enhance your data protection strategy, read our whitepaper today.

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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