The AI Privacy Risks & Mitigations Large Language Models (LLMs) report (released by The European Data Protection Board – EDPB) puts forward a comprehensive risk management methodology for LLM systems with a number of practical mitigation measures for common privacy risks in LLM systems, according to ”edpb.europa.eu”.
In addition, the report provides use cases examples on the application of the risk management framework in real-world scenarios:
– first use case: a virtual assistant (chatbot) for customer queries;
– second use case: LLM system for monitoring and supporting student progress;
– third use case: AI assistant for travel and schedule management.
Large Language Models (LLMs) represent a transformative advancement in artificial intelligence. These general purpose models are trained on extensive datasets, which often encompass publicly available content, proprietary datasets, and specialized domain-specific data. Their applications are diverse, ranging from text generation and summarization to coding assistance, sentiment analysis, and more.
Some LLMs are multimodal LLMs, capable of processing and generating multiple data modalities such as image, audio or video.
LLMs are advanced deep learning models designed to process and generate human-like language. They rely on the transformer architecture5, which uses attention mechanisms to understand context and relationships between words. While most state of the art LLMs rely on transformers due to their scalability and effectiveness, alternatives6
exist based on RNN (Recurring Neural Networks) such as LSTM (Long-short Term Memory) and others that are actively being researched7. For now, transformers dominate general-purpose language models, but innovations in architectures such as those introduced by DeepSeek’s models, may reshape the landscape in the future.
Privacy Concerns
The growing adoption of AI agents powered by LLMs, brings the promise of revolutionizing the way humans work by automating tasks and improving productivity. However, these systems also introduce significant privacy risks that need to be carefully managed:
To perform their tasks effectively, AI agents often require access to a wide range of user data, such as:
– Internet activity: Browsing history, online searches, and frequently visited websites.
– Personal applications: Emails, calendars, and messaging apps for scheduling or
communication tasks.
– Third-party systems: Financial accounts, customer management platforms, or other
organizational systems.
This level of access significantly increases the risk of unauthorized data exposure, particularly if the agent’s systems are compromised.
AI agents are designed to make decisions autonomously, which can lead to errors or choices that users may disagree with.
Like other AI systems, AI agents are susceptible to biases originating from their training data, algorithms and usage context.
As AI agents grow more capable, users will need to consider how much personal data they are willing to share in exchange for convenience. For example, an agent might save time by managing travel bookings or negotiating purchases but requires access to sensitive information such as payment details or login credentials. Balancing these trade-offs requires clear communication about data usage policies and robust consent mechanisms.
Applications of LLMs
LLMs are employed across various applications, enhancing both user experience and operational efficiency. This list represents some of the most prominent applications of LLMs, but it is by no means exhaustive. The versatility of LLMs continues to unlock new use cases across industries, demonstrating their transformative potential in various domains.
– Chatbots and AI Assistants: LLMs power virtual assistants like Siri, Alexa, and Google
Assistant, understand and process natural language, interpret user intent, and generate
responses.
– Content generation: LLMs assist in creating articles, reports, and marketing materials by generating human-like text, thereby streamlining content creation processes.
– Language translation: Advanced LLMs facilitate real-time translation services.
– Sentiment analysis: Businesses use LLMs to analyze customer feedback and social media content, gaining insights into public sentiment and informing strategic decisions.
– Code generation and debugging: Developers leverage LLMs to generate code snippets and identify errors, enhancing software development efficiency.
– Educational support tools: LLMs play a key role in personalized learning by generating educational content, explanations, and answering student questions.
– Legal document processing: LLMs help professionals in the legal field by reviewing and summarizing legal texts, extracting important information, and offering insights.
– Customer support: Automating responses to customer inquiries and escalating complex cases to human agents.
– Autonomous vehicles: Driving cars with real-time decision-making capabilities.
The EDPB launched this project in the context of the Support Pool of Experts programme at the request of the Croatian Data Protection Authority (DPA).
Objective
”This document provides practical guidance and tools for developers and users of Large Language Model (LLM) based systems to manage privacy risks associated with these technologies.
The risk management methodology outlined in this document is designed to help developers and users systematically identify, assess, and mitigate privacy and data protection risks, supporting the responsible development and deployment of LLM systems.
This guidance also supports the requirements of the GDPR Article 25 Data protection by design and by default and Article 32 Security of processing by offering technical and organizational measures to help ensure an appropriate level of security and data protection.
However, the guidance is not intended to replace a Data Protection Impact Assessment (DPIA) as required under Article 35 of the GDPR. Instead, it complements the DPIA process by addressing privacy risks specific to LLM systems, thereby enhancing the robustness of such assessments.”
The report helps Data Protection Authorities (DPAs) to have a comprehensive understanding and state-of-the-art information on the functioning of LLMs systems and the risks associated with LLMs.
Read the full report on ”edpb.europa.eu”.
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