Originally published by Normalyze.

Written by Vamsi Koduru.

Artificial Intelligence is evolving at a breakneck pace, with new models and applications being deployed across industries daily. However, this rapid advancement has brought with it a host of compliance challenges.

As data security methods struggle to keep up with these technological strides, the responsibility falls heavily on data security specialists. They must ensure their organizations remain in compliance with ever-evolving regulations, even as AI-driven transformations continually reshape the data landscape.

Is AI a Data Security Compliance Issue?

To put it simply, yes.

As AI systems, particularly large language models (LLMs), become integral to business operations, they increasingly interact with vast amounts of sensitive and personal data.

This intersection of AI and data privacy has recently drawn the attention of key regulatory frameworks like the GDPR in Europe and the CCPA in California.

The list of regulatory compliance frameworks that seek to address how LLMs can and cannot interact with sensitive data grows every day. This list includes, but certainly isn’t limited to:

• GDPR (General Data Protection Regulation) in Europe enforces strict rules around data minimization, consent, and the right to erasure, all directly impacting AI operations.
• CCPA (California Consumer Privacy Act) mandates that businesses provide consumers with rights over their data, including access, deletion, and opt-out options. All AI systems must be designed to honor these rights, particularly when handling large datasets.
• HIPAA (Health Insurance Portability and Accountability Act) adds another layer of complexity for AI in healthcare. AI systems that process protected health information (PHI) must adhere to HIPAA’s stringent privacy and security standards to safeguard patient data.
• FERPA (Family Educational Rights and Privacy Act) protects the privacy of student education records. AI systems used in educational contexts must comply with FERPA by ensuring the confidentiality of student information.
• PIPEDA (Personal Information Protection and Electronic Documents Act) in Canada governs how private sector organizations collect, use, and disclose personal information in the course of commercial activities. PIPEDA emphasizes accountability, consent, and safeguarding personal data.
• COPPA (Children’s Online Privacy Protection Act) regulates the collection of personal information from children under the age of 13 by online services and websites. AI applications targeting or involving children must comply with COPPA’s requirements.
• NIST Cybersecurity Framework, while not a law, provides guidelines for managing and reducing cybersecurity risks, which can be critical for AI systems that process sensitive data.

Others include the European Union’s ePrivacy Directive, China’s PIPL, Japan’s APPI, Brazil’s LGPD, and many more.

Key AI Data Security Concerns

As AI technologies become more integrated into various industries, they bring a host of LLM data security concerns that can impact compliance. Here are some of the key issues:

Manipulability and Reverse Engineering
One of the critical concerns with AI models is their potential to be manipulated or reverse-engineered. Users can exploit vulnerabilities in AI systems to extract sensitive information, even if the data are thought to be protected.

Accidental Disclosure of Sensitive Information
AI models make mistakes, such as inadvertently providing sensitive information to users who did not request it. This issue has surfaced in real-world scenarios where AI models have generated unsafe or confidential content without intent.

The Black Box Problem
LLM models’ inner workings are relatively unknown, constantly changing, and too frequently unpredictable. This lack of transparency can make it challenging to ensure compliance with data protection regulations, as it is difficult to fully understand how data is processed and decisions are made.

In the same vein, while A.I. hallucinations are increasingly understood, their inherent unpredictability may lead to systems producing content that exceeds the boundaries of regulatory frameworks. An AI application might become convinced a user has more extensive permissions than he or she actually does—or it may fabricate information that appears to be sensitive, potentially leading to time- and resource-intensive complications.

Because of this, AI tools require multiple safeguards when accessing datasets that fall under regulatory frameworks. Traditional cybersecurity defense-in-depth strategies apply.

Regulatory Compliance Challenges

Ethical Concerns of Training on Private, Proprietary, or Personal Data
Training AI models often involves using vast datasets, which may include private, proprietary, or personal information. Organizations must carefully audit the sources of their training data and implement safeguards—without breaking compliance in the process—to ensure that data is used ethically and within relevant regulations.

Changing AI Data Protection Policies
The rapid adoption of AI technologies is reshaping the landscape of data protection policies themselves. As AI systems become more sophisticated, they prompt regulators to reconsider and evolve existing data protection laws to address new risks.

It’s vital that organizations stay proactive about these evolving policies, implementing LLM compliance and risk management strategies that are forward-thinking—not reactive.

Strategies for LLM Compliance and Risk Mitigation

Here are some approaches to help you ensure AI systems align with regulatory requirements and protect user data.

User Consent and Transparency

Of course, one of the simplest strategies for compliance is ensuring that users provide informed consent for the collection and use of their data. While this may not apply to all contexts, such as in healthcare under HIPAA, it is vital for more general applications involving client data.

Along with bolstering their AI data security, organizations should be transparent about how their AI systems operate, what data is being collected, and how it will be used. Simple agreements or privacy policies that clearly outline these aspects can help build trust with users and demonstrate a commitment to data privacy.

Scanning of Data Stores and Inputs

To ensure that data repositories and real-time inputs are free from sensitive information that could pose compliance risks for AI use cases, organizations can use the scanning capabilities provided by Data Security Posture Management (DSPM) tools. The Step-by-Step Guide to Improving Large Language Model Security walks you through the interfaces where DSPM tools provide protection:

• Data Store Scanning and Sanitization: DSPM tools can scan data stores for sensitive information, such as PII, and sanitize or redact it as necessary. Regular scanning helps maintain a secure data environment and reduces the likelihood of unauthorized access or data breaches.
• On-Demand Document Scanning: Before feeding documents into an LLM, on-demand scanners can evaluate them in real time to ensure they do not contain sensitive or confidential information.
• On-Demand Text Scanning: Similarly, on-demand text scanners can scrutinize prompts and responses in real-time, preventing the exposure of sensitive information through AI-generated content.

Data Minimization in LLMs

Data minimization is a key principle in data protection that involves collecting and processing only the data necessary for a specific purpose. In the context of LLMs, this can be achieved through several techniques:

• Limiting Training Data: When training LLMs, it is important to use only the data that is essential for the model’s performance. This reduces the risk of overexposure to sensitive information and helps comply with data minimization principles.
• Focused Data Collection: When operating, AI should only have access to retrieve data that directly contributes to its requested objectives.

Supplemental Privacy-Preserving Techniques

To enhance compliance with data protection regulations, organizations can adopt methods to help protect sensitive and/or personally identifiable information (PII) while still enabling the AI to function effectively:

• Differential Privacy: This technique adds statistical noise to data, making it difficult to identify individual data points while still allowing for meaningful analysis.
• Federated Learning: Instead of centralizing data, federated learning allows AI models to be trained on decentralized data sources, keeping the data on local devices rather than transmitting it to a central server.
• Homomorphic Encryption: This advanced encryption technique allows computations to be performed on encrypted data without needing to decrypt it first.
• Anonymization: This process involves removing or altering PII from datasets so that individuals cannot be identified.
• Pseudonymization: In cases where anonymization is not feasible, pseudonymization can be used to replace PII with pseudonyms or codes.

Keep in mind anonymization techniques are only supplemental lines of data security defense, and they do fully address the issues at the heart of LLM compliance, such as LLM training, prompt manipulation, and the need for comprehensive data risk scanning.

Additional AI Data Security Strategies

To further strengthen compliance and risk mitigation efforts, organizations can consider the following strategies:

• Regular Audits and Monitoring: Implementing continuous auditing and monitoring processes helps ensure that AI systems remain compliant over time. Regular sensitive data discovery and classification audits can identify potential vulnerabilities, such as stores of abandoned data, allowing for timely remediation.
• Visibility and Data Risk Transparency Tools: Investing in tools that provide visibility into the risk of data stores, their connected AI models, and their connected data stores can help organizations better understand how their systems make decisions.
• Ethical AI Governance: Establishing a governance framework that prioritizes ethical considerations in AI development and deployment can help mitigate risks related to data privacy and compliance. This includes setting clear guidelines for AI use, training staff on ethical practices, and engaging with stakeholders to address concerns.
• Third-Party Vendor Management: When using third-party AI solutions or data sources, it is essential to assess their compliance with data protection regulations. Organizations should ensure that vendors’ tools can adhere to the same standards of data privacy and security before incorporating these requirements into contracts and service agreements.

Frequently asked questions

1. How can we ensure our AI models comply with evolving data protection regulations like GDPR and CCPA?

Implementing robust data scanning tools, minimizing data use, and continuously monitoring AI interactions with sensitive information can help ensure compliance.

2. What are the biggest risks AI systems pose to data privacy and security?

Key risks include accidental disclosure of sensitive information, AI model manipulation, and the “black box” nature of LLMs, which can lead to unpredictable outputs and compliance challenges.

3. How can we minimize the amount of sensitive data used by AI systems without compromising performance?

Employing data minimization techniques, such as limiting training data and using privacy-preserving methods like differential privacy, helps reduce exposure while maintaining model effectiveness.

4. What tools are available to scan and secure data before feeding it into AI models?

Data Security Posture Management (DSPM) tools can scan, sanitize, and monitor data stores and real-time inputs to prevent exposure of sensitive information in AI applications.

5. How often should we audit and review our AI systems for data compliance and security risks?

Regular audits and continuous monitoring are essential. Organizations should implement a structured process for frequent reviews to identify and address vulnerabilities as regulations and technologies evolve.