The domain of artificial intelligence (AI), particularly generative AI (GenAI), is brimming with intriguing potential for companies all over the world. conversational AI bots, open-source large language models (LLMs), and specialized models are now ubiquitous in organizations. However, the rapid advancement and widespread adoption of these tools is creating substantial challenges when it comes to data governance and legal compliance.
One recent study found that 46% of business leaders believe that their employees have inadvertently shared confidential information with ChatGPT, the most popular platform of its kind. While the urge to embrace these cutting-edge tools is understandable, it’s far more advantageous to address data privacy concerns early on than risk a huge data breach and compliance fine.
With that in mind, here, we’ll explore how to enable generative AI usage in your organization without crossing compliance boundaries.
Are there regulations around generative AI?
The compliance landscape in the USA is quite the patchwork. With no national data privacy law in place, individual states have taken it upon themselves to enact their own rules governing data protection for companies operating within their borders or serving their customers.
Currently, we’ve got five states with data privacy laws in effect or on the horizon for this year: California and Virginia (both already enacted as of January 1, 2023), Colorado and Connecticut (set to go into effect on July 1, 2023), and Utah (scheduled for enactment on December 31, 2023).
But, as we all know, each of these laws is unique. The California Privacy Rights Act (CPRA), for example, is known for its consumer-friendly nature, while the Utah Consumer Privacy Act (UCPA) leans more favorably towards businesses. In essence, there are subtle nuances within each law that make the task of complying with multiple regulations an absolute brain teaser.
On top of that, the US government is starting to dip its toes into the waters of AI regulation. The US Department of Commerce has just announced a call for public input on how to establish accountability measures for AI.
While the government contemplates crafting AI regulations, organizations can turn to existing regulatory frameworks for some much-needed guidance on AI usage. Typically, these regulations revolve around data usage and exchange, which means that any AI tool will require diligent monitoring of data flows to ensure compliance with regulatory and privacy standards.
First up, we have the Health Insurance Portability and Accountability Act (HIPAA). HIPAA makes sure that healthcare organizations handle patient data with utmost care and respect. It sets rules and standards to protect patient privacy and requires measures to prevent unauthorized access. Patients have rights under HIPAA, such as accessing their health records and requesting corrections. AI tools in healthcare must comply with HIPAA to safeguard patient privacy and data security. Developers and providers need to implement robust security measures, access controls, and empower patients to exercise their rights. Failing to comply with HIPAA can result in legal penalties and damage to one’s reputation.
Next, we have the Cybersecurity Maturity Model Certification (CMMC) 2.0, brought to you by the U.S. Department of Defense (DoD). This framework is all about beefing up cybersecurity for defense contractors and suppliers. It aims to protect sensitive defense data by setting specific cybersecurity requirements. CMMC is at the forefront of defining market standards for data protection. The upcoming CMMC updates will even require real-time monitoring of cloud-hosted applications, making a Data Loss Prevention (DLP) solution mandatory when using GenAI tools.
Now, let’s talk about ISO 27001-2. It’s the updated version of the ISO 27001 standard for information security management systems (ISMS). The new guidance focuses on data security and protecting data in the cloud. ISO 27001-2 covers various aspects like risk assessment, security controls, monitoring, and improvement. Many legal teams interpret these controls to include DLP solutions as part of their data governance framework.
Last but not least, we have the Gramm-Leach-Bliley Act (GLBA), a federal law that demands financial institutions prioritize customer privacy and security. The updated GLBA rules released in June 2023 introduced new requirements for data security controls. Financial institutions using AI systems must comply with GLBA to ensure customer information remains private and secure. This includes proper data retention, usage, and disclosures to customers.
How to meet compliance obligations while using AI
To meet compliance obligations while harnessing the power of generative AI, organizations must assemble a team capable of deciphering the regulations that apply to their operations. This team will be responsible for identifying any potential conflicts between different laws, devising clever strategies to navigate those conflicts, and staying up to date with emerging regulations that may impact their existing compliance plan.
Ideally, you’ll include legal counsel, a data protection officer (DPO), data management and security experts, a privacy officer, and trusty IT personnel. With this team in place, organizations can establish clear standards for the usage of generative AI while ensuring the utmost care for data protection.
On top of this, investing in data loss prevention (DLP) is crucial. And that’s where we come in. As leaders in DLP and compliance for cloud apps, we’re spearheading cloud data governance for GenAI, building upon our context-aware DLP capabilities to give organizations confidence in their data security and compliance posture while using tools like ChatGPT.
Here’s how our tool helps you meet compliance obligations.
Protect sensitive data in real-time with Polymer DLP for AI. Our advanced monitoring system scans and analyzes conversations, both initiated by employees and generated by ChatGPT, to prevent data exposure. Bi-directional monitoring ensures that sensitive data is never received by employees, even if inadvertently generated by ChatGPT.
Logs & audits
Enhance your data security with Polymer DLP for AI’s robust logging and audit features. Gain comprehensive insights into employee transactions, track policy violations, investigate data breaches, and monitor ChatGPT’s usage patterns.
E-discovery for GenAI interactions
Our solution enables organizations to efficiently conduct searches and retrieve relevant generative AI interactions when faced with e-discovery requests. Meet your legal and regulatory obligations, and facilitate investigations, audits, and legal proceedings with ease using Polymer DLP for AI.
User training & nudges
Our platform supports point-of-violation training, providing real-time nudges to users when violations occur. This approach has proven to reduce repeat violations by over 40% within days. Additionally, Polymer offers workflows that allow users to accept responsibility for sharing sensitive data externally when it aligns with business needs.
Move towards compliant AI today
The potential generative AI holds for businesses is awe-inspiring, but we mustn’t forget the importance of data security and compliance. Prioritizing these matters now is crucial to preventing any unintended consequences and realizing the full potential of AI.
By establishing a strong foundation, we can ensure that data protection becomes an integral part of the process from the very beginning. Rather than treating it as an afterthought, weave the principles of data protection into your strategy today, starting with Polymer for AI.
Read our whitepaper to learn more.