Within five days of its release, ChatGPT counted one million registered users. This was the fastest growth of any product in its category, prompting the rise of the GenAI era that saw countless products developed at the speed of light. As expected, regulation could only catch up with this incredible expansion pace.
What are the biggest compliance challenges LLMs (large language models) pose? And how can teams use data versioning to achieve compliance for LLMs? Continue reading to find out.
What Is LLM Compliance All About?
LLM compliance is based on data compliance, which includes adhering to legal, ethical, and industry-specific data rules. The primary goal of data compliance is to guarantee that data is managed ethically, transparently, and securely.
Organizations must address numerous critical areas of data compliance, including:
Legal Regulations
Several countries and regions have passed data protection laws to protect the privacy and security of people’s personal information. The General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States are notable examples.
Organizations that collect and process personal data must follow these rules, which often entail getting explicit consent, giving data subjects access to their information, and maintaining safe data storage and transit.
Industry Standards
Many industries, including finance, healthcare, and telecommunications, have developed particular data compliance standards to meet sector-specific difficulties. For example, in the healthcare industry, the Health Insurance Portability and Accountability Act (HIPAA) requires tight controls on managing patient health information.
Data Governance Policies
Aside from external requirements, corporations frequently develop their internal data governance policies. These rules establish guidelines and best practices for data management within the company. Data compliance initiatives should be aligned with these principles to ensure consistency and integrity throughout all data-related procedures.
Risk Management
Data compliance is directly related to risk management. Noncompliance carries considerable risks for a firm, such as legal issues, financial penalties, and reputational damage. Implementing robust data compliance methods mitigates these threats while ensuring long-term viability.
The Unique Compliance Challenges of LLMs
Massive scale
LLMs are trained on vast and diverse datasets. The output of these models is massive. Multiple submissions of the same question, a tiny modification to the prompt, training on somewhat different datasets, etc., can completely change the output. This increases the complexity of ensuring compliance.
The Black Box Nature of GenAI
Since LLMs are trained using deep learning techniques, these models are inherently incomprehensible to humans; no one can explain the reasoning behind or process for making these connections. Complication and complexity are two terms that help to clarify this.
Copyright Issues
Using authors’ works without their consent is risky, and this is unique to AI. Part of the reason for this secrecy is conventional industrial protection. Copyright disputes, negative press, and allegations of biased training data are all possible outcomes for companies that acknowledge using someone else’s work.
Because of these issues surrounding genAI, teams find it extremely challenging to build effective rules and compliance requirements.
Why Traceability and Transparency Are Key to Compliance
To meet compliance requirements, companies need to know where the data used for training came from and whether it complies with regulations.
This is where the concept of data traceability comes in.
Data traceability is all about the ability to check the accuracy, quality, and reliability of data using an audit trail of both the data and metadata. Traceability enables companies to track data and modifications back to their source, ensuring the data’s authenticity and integrity.
It is essential for regulatory compliance, particularly in businesses where data accuracy is critical, such as healthcare or banking.
Why is data traceability key for achieving LLM compliance?
1. Transparency and Accountability
Data traceability can provide a detailed record of who accessed the data, when, and why. This ensures accountability for data handling and consumption while detecting illegal access or questionable activities.
2. Compliance and Governance
Data traceability facilitates compliance with numerous data protection and privacy standards, including GDPR. By tracking their data consumption and processing operations, organizations can demonstrate compliance but also manage and control how data is used, ensuring it is done ethically and responsibly.
3. Data Integrity and Quality
Organizations can use traceability to preserve and improve the quality and integrity of data. By understanding where data came from, how it was used, and how it was transformed, they may recognize and manage concerns with data quality. This contributes to the trustworthiness of the data.
4. Risk Management
Data traceability can also help manage risks associated with data handling and use. For example, it can identify potential privacy issues (such as unlawful access or use), assess their impact, and direct mitigation actions.
5. Increased Consumer Trust
Organizations can earn trust by showing comprehensive data traceability, especially relevant for LLM applications like customer service chatbots. Customers can be confident that their data is managed appropriately, which increases their trust in the company’s products or services.
The Role of Data Versioning in Ensuring Compliance
Data version control is a systematic method for managing changes and updates to data over time. Like version control in software development, it allows you to accurately track, document, and preserve data modifications.
This process entails recording the history of data updates, assigning them to specific individuals, and allowing for easy retrieval of past states. Data version management is critical in cases with several data contributors, such as firms where data scientists, analysts, and engineers collaborate on datasets.
How Does Data Version Control Work?
At its foundation, data versioning is based on creating snapshots of data at various periods in time. Each snapshot is a version of the dataset, encapsulating its state and content at that precise instant.
When changes are made to the data, a new snapshot is created to document them. These images are grouped chronologically, resulting in a timeline of the dataset’s evolution.
To facilitate data version management, teams can use specialized tools and platforms created specifically for this purpose. These solutions include data version tracking, access control, and collaborative capabilities.
How Data Versioning Helps Address Compliance for LLMs
Data version control is critical to ensuring data compliance across several regulatory frameworks. By combining the capabilities of data version control with data compliance techniques – potentially via a comprehensive LLMOps tooling – companies can create a holistic approach to data management that tackles critical compliance concerns.
Auditing and Traceability
Data version control records a complete history of data changes, including who made them and when. This audit trail improves transparency and traceability, which are critical components of data compliance. In the case of a regulatory audit, firms can demonstrate compliance by demonstrating the data version history.
Data Retention Policies
Many data compliance standards highlight data retention, which entails collecting and maintaining only the data required for a given purpose. Data version control promotes data minimization by allowing businesses to track changes and identify superfluous additions to datasets.
Access Control and Data Security
Data compliance often requires strict access restrictions to protect sensitive information from unauthorized use. Data version control technologies can interact with current access control mechanisms, guaranteeing that only authorized users can edit specific datasets.
Data Validation and Quality Assurance
Data compliance relies on accurate and reliable data. Data version management makes data validation and quality assurance easier by allowing data scientists and analysts to examine different versions of data and evaluate their accuracy across time.
Companies must manage regulatory changes and upgrades to stay compliant. Data version control helps with change management by allowing companies to implement data updates in a controlled and documented manner.
Compliance in LLM Development: 3 Aspects of Data Versioning
1. Training with Sensitive or Regulated Data
Taking extra care about data compliance translates into benefits in LLM training. Data versioning helps ensure that LLMs trained on sensitive datasets (e.g., medical or financial data) adhere to privacy regulations.
2. Enabling Auditable Pipelines
Data versioning allows teams to create pipelines where each step, from data ingestion to model deployment, is fully auditable, ensuring end-to-end compliance.
3. Managing Updates and Model Drift
Data version control also helps address issues related to model drift, ensuring that any updates or retraining events comply with initial regulatory approvals. LLM observability tools come in handy here.
Wrap up
Organizations experimenting with LLMs need to meet multiple compliance standards. Data version control makes achieving compliance easier: it provides full visibility into data manipulations, allowing compliance teams to ensure that no unauthorized changes were made to sensitive datasets. By maintaining a well-documented history of data changes, enterprises can demonstrate adherence to various regulations and internal data governance principles.
