Securing GenAI: Vol 3 — Privacy, Security, and Compliance

Author(s): Leapfrog Technology

Originally published on Towards AI.

Written by Manu Chatterjee, Head of AI at Leapfrog Technology

Welcome to the third article in our series on Generative AI (GenAI) security in the enterprise. In our previous articles, we explored the broad security landscape of AI systems and took a deep dive into prompt injection attacks. Now, we turn our attention to the critical intersection of privacy, security, and compliance in the GenAI ecosystem.

Unlike traditional web applications, GenAI systems present unique security challenges that extend beyond conventional cybersecurity frameworks. These systems operate with data in fundamentally different ways — they can memorize training information, generate new content that may inadvertently reveal sensitive data, and face novel adversarial attacks that exploit their learning mechanisms.

In this article, we’ll explore how privacy, security, and compliance requirements must evolve to address GenAI-specific concerns. We’ll contrast these emerging challenges with standard cybersecurity practices to help organizations adapt their security strategies for AI-driven applications. By understanding these differences, security professionals can build more robust protection systems specifically designed for generative AI technologies.

Data privacy and protection in Generative AI

Data protection strategies unique to GenAI

Traditional data protection focuses on securing data at rest and in transit through encryption, access controls, and secure storage practices. While these remain important, GenAI introduces new complexity that requires additional layers of protection. Organizations must go beyond these conventional approaches by addressing:

Data memorization risks: Large language models and other generative systems can unintentionally memorize sensitive information from their training data (also fine-tuning). This creates a risk that, when prompted correctly, models might regenerate and expose this information during inference. For example, a model trained on customer support transcripts might inadvertently expose personal details when asked about similar scenarios.

Permission-based retrieval in RAG systems: Retrieval-Augmented Generation (RAG) and Graph RAG architectures enhance GenAI capabilities by connecting models to external knowledge sources. However, these systems introduce new privacy and security concerns. When a model retrieves information from document stores, databases, or knowledge graphs, it’s essential to enforce proper permission checks before that data is incorporated into responses. Without robust access controls at the retrieval layer, a user might gain access to sensitive information they’re not authorized to see. Organizations should implement security trimming on retrieved content based on user roles, ensuring that even approved users of a GenAI system can only access information appropriate to their authorization level. This permission validation must occur before retrieved data is passed to the language model for response generation.

Data anonymization approaches for privacy protection

When working with sensitive data in GenAI systems, organizations can employ various data anonymization techniques to protect individual privacy while preserving the utility of information for model training and inference. Organizations typically need anonymization when they want to leverage valuable patterns in sensitive datasets — such as patient health records, financial transactions, or legal case histories — without exposing protected individual information.

For instance, healthcare companies might anonymize patient records to train models that can identify disease progression patterns, while legal firms might anonymize case documents to build systems that can analyze legal precedents without revealing client identities.

Anonymization is particularly critical when aggregating data across multiple sources to identify broader trends and insights that would be impossible to discover by analyzing individual records in isolation. These approaches become essential when compliance requirements (like HIPAA or financial regulations) prohibit direct use of identifiable data, but still permit using properly anonymized versions for analysis and AI training.

These approaches aim to remove or transform personally identifiable information (PII) and other sensitive elements while maintaining the statistical or analytical value of the dataset. Here are key anonymization strategies specifically applicable to GenAI:

Synthetic data generation: One promising approach to reduce privacy risks involves training models on artificially created data that mimics the statistical properties of real data without containing actual sensitive information. Companies like MOSTLY AI and Syntegra provide platforms that can generate synthetic healthcare, financial, and customer data that maintains analytical usefulness while eliminating privacy concerns. With synthetic data, organizations can train powerful models without exposing real user information, effectively breaking the connection between training data and actual individuals.

Differential privacy in model training: Differential Privacy is a mathematical framework that adds carefully calibrated noise to training data or model updates to ensure that no individual data point significantly influences the model’s behavior. Apple pioneered the use of differential privacy at scale, allowing them to learn from user data without compromising individual privacy. Differential privacy provides formal guarantees about the maximum amount of information that can be learned about any individual in the dataset, making it particularly valuable for highly sensitive applications where even statistical patterns might reveal protected information.

Federated learning for distributed privacy: This approach enables model training across multiple devices or data centers without sharing raw data. The model travels to the data rather than data traveling to a central location. Google has implemented federated learning in Gboard (their mobile keyboard) to improve text prediction without sending users’ actual typing data to Google servers. By keeping sensitive data local and only sharing model updates, federated learning fundamentally changes the privacy equation, allowing organizations to benefit from diverse data sources without creating central repositories of sensitive information.

Compliance frameworks for GenAI

Traditional regulations like GDPR and HIPAA still apply to AI systems, but GenAI creates new challenges in how organizations interpret and implement compliance requirements:

GDPR (EU) — The “right to be forgotten” becomes complex when data may be embedded in model weights. Organizations must now consider how to handle requests for data deletion when that data might have influenced model training. Additionally, ensuring AI-generated responses do not inadvertently expose personal data requires new technical safeguards.

CCPA (California) — California’s privacy law grants consumers rights to know what personal information businesses collect and to opt out of sales of that information. With GenAI, businesses must address how models handle user opt-outs and personal data requests, especially when models might have been trained with that data.

AI Act (EU) — This pioneering European legislation creates a risk-based framework for governing AI. High-risk AI applications face stricter requirements around transparency, data governance, and human oversight. Organizations deploying GenAI must assess their risk category and implement appropriate controls based on use case sensitivity.

ISO/IEC 42001 — The first AI-specific management standard addresses governance and compliance issues unique to AI systems. It provides a framework for organizations to demonstrate responsible AI practices, including those related to data privacy and security in generative applications.

Challenges in Generative AI privacy

The unique capabilities of generative models introduce several specific privacy challenges:

Training on private data: Organizations often want to leverage proprietary, confidential, or user-submitted data to create valuable GenAI applications. However, this creates significant risks if models are not properly secured or if they memorize sensitive information. Financial institutions implementing AI assistants typically need comprehensive safeguards to ensure that confidential financial data used in training doesn’t appear in model outputs.

Inference-time privacy leaks: Even with secure training practices, models might generate outputs that reveal sensitive information based on patterns learned during training. These leaks can occur in subtle ways that are difficult to detect with traditional security monitoring. For instance, a healthcare GenAI system might inadvertently include identifiable patient details when generating treatment summaries if not properly designed. This is why it is critical in retrieval augmented generation (RAG) to make sure that the retrieval system adheres to proper user permissions for source data.

Hallucinations and privacy risks: GenAI models sometimes generate plausible-sounding but factually incorrect information. When these “hallucinations” involve made-up but realistic-looking personal data, they can create privacy risks even when no actual data leak has occurred. Organizations deploying GenAI systems for content generation must implement safeguards to detect and prevent fabricated information that could appear legitimate but create legal or reputational risks. One way to mitigate this is to log each call output from LLMs to provide an audit defense.

Data provenance and traceability: Maintaining clear records of where training data originated becomes essential for compliance and responsible AI development. This challenge grows exponentially with the scale of data used in large language models. OpenAI faced criticism for lacking transparency about the specific sources used to train its GPT models, making compliance verification difficult.

Access control and compliance for GenAI

Evolving access control for AI models: Traditional access control focuses on protecting data access based on user identity and roles. With GenAI, access control must extend to protect both the models themselves and the data they process, requiring enhanced security mechanisms:

Dynamic access control for AI workflows: Unlike static permissions for traditional applications, GenAI systems benefit from context-aware access controls that adjust permissions based on real-time model usage patterns and risk assessment. For example, a financial services company might implement stricter controls when an AI model is processing customer financial data versus when it’s generating marketing content.

Zero Trust model for AI APIs: Following the principle of “never trust, always verify,” organizations should ensure all requests to AI models are authenticated, authorized, and continuously validated. This approach is particularly important for GenAI APIs that might be accessed from various applications and services. Microsoft’s Azure OpenAI Service implements comprehensive authentication, rate limiting, and continuous monitoring to secure model access.

Model-based access restrictions: Beyond controlling who can access models, organizations need guardrails on what actions authorized users can perform. This includes preventing unauthorized fine-tuning or modifications to production AI models that might introduce backdoors or compromise performance. Hugging Face’s model hub implements version control and access permissions to track and control who can modify shared models.

Compliance risks unique to AI

GenAI introduces several compliance challenges that go beyond traditional software systems:

Model explainability and audits: Regulatory bodies increasingly require AI systems to be interpretable and auditable. This poses challenges for complex generative models where decision-making processes can be opaque. Financial institutions using AI for lending decisions face requirements to explain automated denials under regulations like the Equal Credit Opportunity Act.

Ensuring data subject rights with AI: When AI systems influence decisions about individuals, organizations must provide mechanisms for those individuals to exercise their rights to challenge decisions, correct inaccurate data, and understand how automated decisions were made. Healthcare providers using GenAI for patient care recommendations must enable patients to access and correct their health information under HIPAA.

Bias and fairness compliance: Ensuring models meet regulatory and ethical fairness standards is critical, especially when GenAI systems influence decisions in sensitive domains like hiring, lending, or healthcare. Amazon discovered gender bias in an AI recruiting tool they developed and ultimately abandoned the system when they couldn’t fully eliminate the bias.

GenAI-specific threats in access control

Several novel threats target the access control mechanisms of GenAI systems:

Model theft and intellectual property risks: Organizations invest substantial resources in developing and fine-tuning GenAI models, making them valuable intellectual property. Without proper protections, competitors might attempt to extract these models through API access. Several major AI companies have implemented various digital watermarking and model protection techniques to help identify unauthorized copies or uses of their proprietary models.

Prompt injection attacks: As we explored in our previous article, attackers can craft specific inputs that manipulate AI models to bypass security controls and produce unintended outputs. These attacks can be particularly effective against poorly secured GenAI interfaces. Major AI providers now implement prompt sanitization and filtering to reduce these risks.

Data poisoning attacks: Malicious actors might introduce tainted data into training sets to influence model behavior in subtle ways that are difficult to detect. For example, a competitor might try to poison a financial sentiment analysis model to misclassify news about their company. Most top tier model providers implement rigorous data validation and monitoring to detect anomalous training data, however one must be careful if using models from organizations for which you do not have a business agreement as the liability of malicious LLM outputs is less clear.

Advanced privacy techniques for GenAI

Several approaches can enhance privacy protection in GenAI deployments:

Embedding-based privacy: This approach protects user data by converting it into vector embeddings without storing the original information. These embeddings capture semantic meaning while obscuring specific details, reducing privacy risks in recommendation systems and similar applications. Pinterest uses embedding-based methods to provide personalized content recommendations without maintaining detailed user profiles.

Redaction and context filtering: Automatically identifying and removing personally identifiable information (PII) from both AI model inputs and outputs helps prevent privacy leaks. Healthcare organizations use sophisticated named entity recognition to redact patient identifiers before processing medical text with GenAI systems. There are several privacy vault providers such as SkyFlow, Microsoft Presidio, and Private AI.

Secure multi-model architectures: Complex GenAI applications often use multiple specialized models working together. Ensuring these models interact securely without leaking information between them requires careful design. Companies implementing recommendation systems frequently deploy layered AI architectures with information barriers between components to enhance privacy protection.

Post-processing filters for AI outputs: Implementing filtering mechanisms to detect and remove sensitive or non-compliant generated content provides an essential safety net. These filters can screen for personal information, harmful content, or outputs that might violate regulatory requirements. OpenAI implements multiple layers of content filtering on all GPT outputs to prevent misuse.

RBAC-controlled recall: When using retrieval-augmented generation (RAG) systems that access external knowledge sources, the security context of retrieved data should be checked against proper roles before being sent to the language model. This ensures that even authorized users can’t use the AI to access information beyond their permission level. Enterprises implementing Microsoft’s Azure AI Search with OpenAI enforce security trimming on search results to maintain access controls.

Tools & services

• Data sanitization platforms: OpenMined’s PySyft provides privacy-preserving machine learning tools, while Google AI’s privacy toolkit offers differential privacy implementation for machine learning.
• Bias detection and fairness audits: IBM AI Fairness 360 provides a comprehensive set of metrics to check for unwanted bias, while Google’s What-If Tool enables visual investigation of model behavior across different demographic groups.

GenAI model security and compliance

Adapting security models for AI

Traditional security frameworks need significant adaptation to address the unique characteristics of generative AI:

Traditional vs. AI-specific threats: While traditional systems focus on protecting against unauthorized access and data breaches, GenAI systems must also defend against adversarial examples, model inversion attacks, and other AI-specific techniques. These novel threats require specialized detection and mitigation approaches that conventional security tools don’t address.

Red teaming AI systems: Conducting adversarial testing against AI models helps identify vulnerabilities before they can be exploited. This involves specialized teams attempting to manipulate model behavior in ways that could cause harm or privacy violations. Anthropic employs dedicated red teams to probe their Claude model for weaknesses, helping to improve its robustness before release.

Fine-tuning restrictions: Organizations must implement controls to ensure deployed AI models cannot be manipulated through unauthorized fine-tuning, which could introduce backdoors or compromise model security. Trusted execution environments and cryptographic verification can help maintain model integrity throughout the deployment lifecycle.

Tools & services

• Adversarial testing frameworks: MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) provides a knowledge base of adversarial tactics and techniques, while the Adversarial Robustness Toolkit helps developers evaluate model robustness.
• AI model integrity auditing: Google Vertex AI Model Monitoring continuously checks for data drift and model degradation, while Microsoft’s Responsible AI Toolkit provides tools for model assessment, model debugging, and error analysis.

AI deployment security best practices

Securing AI APIs and model endpoints

The interfaces through which users and applications access GenAI models represent critical security boundaries:

Rate-limiting AI API calls: Implementing strict limitations on how frequently models can be queried helps prevent abuse, model extraction attacks, and denial-of-service. OpenAI implements tiered rate limits based on user subscription levels to prevent API misuse while accommodating legitimate usage patterns.

Tokenization for model outputs: Replacing sensitive information in model outputs with placeholders or tokens helps prevent inadvertent leaks. Financial institutions use tokenization when implementing GenAI assistants that might otherwise expose account details or transaction information.

AI model isolation: Running critical AI models in separate, hardened environments provides additional protection against compromise. This architectural approach limits the potential impact if one component is breached. Google Cloud’s AI Platform implements strong isolation between tenant workloads to prevent cross-contamination.

Tools & services

• AI API security: Cloudflare AI Gateway provides DDoS protection and rate limiting specifically designed for AI APIs, while AWS API Gateway security filters can implement custom validation logic for AI model inputs.
• AI response filtering: Microsoft Content Safety API screens text and images for harmful content, while Google Cloud DLP (Data Loss Prevention) can identify and redact sensitive information in model outputs.

Summary

Ensuring privacy, security, and compliance in generative AI requires a paradigm shift from traditional cybersecurity approaches. Organizations must address model-specific risks, adversarial attacks, and inference-time privacy concerns while adapting compliance frameworks to AI-driven decision-making.

The unique characteristics of GenAI — including data memorization, complex attack surfaces, and the ability to generate new content — demand specialized protection strategies. By integrating AI-specific security tools, governance frameworks, and continuous monitoring, enterprises can securely deploy GenAI applications that respect privacy and maintain regulatory compliance.

Conclusion

The evolving landscape of generative AI security demands a tailored approach that goes beyond conventional cybersecurity. Organizations must embrace new frameworks, adversarial testing, and privacy-enhancing techniques to mitigate AI risks effectively. By staying ahead of emerging threats, enterprises can deploy GenAI responsibly while ensuring compliance and data integrity.

The future of generative AI in the enterprise depends on a strong foundation of privacy, security, and compliance. By adhering to the principles and best practices outlined in this framework, organizations can build trust with their stakeholders and unlock the full potential of AI technologies. Continuous education, training, and adaptation to evolving regulatory landscapes will be crucial for long-term success.

As we look ahead to the next installment in our series, we’ll examine practical implementation strategies for securing GenAI in large-scale enterprise deployments, with a focus on integrating these systems with existing security architectures and governance frameworks.

Resources for further reading

Technical resources and documentation

• NIST AI Risk Management Framework
• AWS Machine Learning Security Documentation
• Google Cloud AI Principles

Industry leaders & vendors

• OpenAI’s Approach to AI Safety
• Anthropic’s Constitutional AI
• Hugging Face’s Ethical AI Guidelines

Research and publications

• Carlini, N., et al. (2021). “Extracting Training Data from Large Language Models.” USENIX Security Symposium
• Nasr, M., et al. (2019). “Comprehensive Privacy Analysis of Deep Learning.” IEEE Symposium on Security and Privacy
• Dwork, C. (2008). “Differential Privacy: A Survey of Results.” Theory and Applications of Models of Computation

Community and forums

• Cloud Security Alliance (AI)
• Partnership on AI
• AI Ethics Forum on Stack Exchange

Articles and blog posts

• Privacy and AI Coworkers
• VentureBeat: “Is Zero Party Data part of the future of AI privacy?”
• Forbes: “Building Trust and Compliance in the age of AI”

Technical guides and whitepapers

• Deloitte: “Trustworthy AI™ Framework”
• Microsoft: “Responsible AI Resources”
• IBM: “Trusted AI”

Research papers

• Papernot, N., et al. (2018). “Scalable Private Learning with PATE.” ICLR
• Shokri, R., et al. (2017). “Membership Inference Attacks Against Machine Learning Models.” IEEE Symposium on Security and Privacy
• Veale, M., et al. (2018). “Fairness and Accountability Design Needs for Algorithmic Support in High-Stakes Public Sector Decision-Making.” CHI Conference on Human Factors in Computing Systems

To explore more insightful AI blogs, visit www.lftechnology.com/blogs

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Published via Towards AI