Azure OpenAI: Security for GenAI and LLM
Generative AI (Gen AI), especially when involving Large Language Models (LLMs) like GPT, introduces a unique set of security risks. These risks encompass traditional cybersecurity concerns and extend to ethical, privacy, and manipulation challenges. Understanding these risks is crucial for developers, users, and policymakers to mitigate potential threats effectively.
The rise of Generative AI (GenAI) applications powered by Large Language Models (LLMs) has transformed the landscape of natural language processing, offering unprecedented capabilities in content creation, summarization, translation, question-answering, and conversational interactions.
This post, “Azure OpenAI: Security for GenAI and LLM,” is part of a series for LLM Security in Azure. This post focuses on Gen AI and its LLM security for Azure with approach, data, and processing.
Generative AI and LLM Security in a Nutshell
Generative AI (Gen AI) and Large Language Models (LLMs) have revolutionized how we interact with technology, offering unprecedented opportunities for innovation and creativity. However, from an attacker’s perspective, these technologies also open new avenues for exploitation. Here’s a very brief overview of security concerns associated with Generative AI and LLMs from the viewpoint of potential attackers:
Data Poisoning and Bias Injection: Attackers might attempt to manipulate the training data of LLMs to introduce biases or trigger specific malicious responses. This could be done by feeding misleading information or deliberately biased data into the model’s learning phase, potentially leading to compromised decision-making or automated generation of harmful content.
Model Inversion Attacks: By making repeated queries and analyzing the responses from LLMs, attackers can perform model inversion attacks. These attacks aim to infer sensitive information about the training data or extract proprietary features of the model itself, posing a threat to privacy and intellectual property.
Adversarial Attacks: Generative AI models are susceptible to adversarial attacks, where slight, often imperceptible alterations to input data can cause the model to make incorrect predictions or generate inappropriate content. Attackers could exploit this vulnerability to bypass content moderation systems or to generate misleading information.
Misuse of Generated Content: The ability of LLMs to generate realistic text, images, or audio can be misused to create deepfakes, propagate misinformation, or conduct social engineering attacks. Attackers might use these capabilities to undermine trust in digital content, manipulate public opinion, or impersonate individuals.
Exploiting System Vulnerabilities: As with any software, the infrastructure hosting Generative AI and LLMs might have vulnerabilities that attackers could exploit. This includes targeting the cloud environments, APIs, or other interfaces through which these models are accessed, aiming to gain unauthorized access to the models or the underlying data.
Additionally, addressing the complexities associated with LLMs involves comprehensive security and ethical measures. Here are the critical points as bullet points:
Protecting the Confidentiality and Integrity of Data
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- Implement robust data encryption standards to secure data at rest and in transit.
- Use access control mechanisms to ensure only authorized personnel can access or modify the training and querying data.
- Employ data anonymization and pseudonymization techniques to protect sensitive information in the dataset.
Ensuring the Availability and Reliability of LLMs and Their Services
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- Deploy robust infrastructure with redundancy and failover capabilities to maintain service continuity.
- Regularly update systems to address vulnerabilities and ensure the security of LLM platforms.
- Implement performance monitoring and scaling strategies to handle varying loads and maintain response times.
Preventing the Misuse or Abuse of LLMs by Malicious Actors or Unintended Users
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- Enforce strict usage policies and terms of service that prohibit misuse and outline acceptable uses of LLMs.
- Integrate content filtering mechanisms to prevent generating harmful, illegal, or unethical content.
- Use authentication and rate-limiting to restrict access and prevent abusive behaviors.
Monitoring and Auditing the LLMs’ Outputs and Behaviors for Quality, Accuracy, and Compliance
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- Establish continuous monitoring systems to track LLMs’ performance and output quality, identifying anomalies or deviations from expected behaviors.
- Conduct audits of LLM outputs for accuracy, fairness, and compliance with ethical standards and regulations.
- Implement feedback mechanisms to allow users to report concerns or inaccuracies in LLM outputs for further review and correction.
Managing the Ethical and Social Implications of the LLMs’ Outputs and Impacts
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- Engage in ethical review processes to evaluate the potential impacts of LLMs on society, considering aspects such as bias, fairness, and societal harm.
- Promote transparency in AI by documenting and communicating the design choices, training data sources, and limitations of LLMs to users and stakeholders.
- Foster an ongoing dialogue with the broader community, including ethicists, policymakers, and the public, to address concerns and adapt practices in response to evolving ethical standards.
Azure OpenAI Service
Azure OpenAI Service is a cloud-based service offered by Microsoft, integrating OpenAI’s powerful artificial intelligence models, including versions of the GPT model, Codex, and DALLE, into Microsoft’s Azure cloud platform. This service enables developers, businesses, and researchers to easily incorporate advanced AI capabilities into their applications without the need for extensive machine learning expertise or infrastructure.
What Data Does the Azure OpenAI Service Process?
The Azure OpenAI Service, a cloud-based offering by Microsoft integrating OpenAI’s advanced natural language models (like GPT-3, Codex, and potentially others as they become available), processes a wide range of data types to provide various AI-powered capabilities. The types of data it can process include, but are not limited to:
- Text Data: This is the most common type of data processed by Azure OpenAI Service. It includes anything from simple queries, paragraphs, and documents to complex datasets. The service can generate text, translate languages, summarize documents, answer questions, and much more based on the text input it receives.
- Code: The Azure OpenAI Service can understand and generate programming code with models like Codex. This capability is used for code completion, explanation, conversion between programming languages, and generating code snippets from natural language descriptions.
- Structured Data: Though primarily focused on unstructured text, the service can work with structured data by generating natural language descriptions or analyses of the data. For example, it can take data in tables or databases and generate summaries and insights or even SQL queries based on natural language instructions.
- Conversational Data: Azure OpenAI Service can process and generate conversational responses, making it suitable for building chatbots, virtual assistants, and other conversational AI applications. This involves understanding the context of a conversation, maintaining a dialogue state, and generating appropriate responses.
- Educational Content: The service can process educational material to provide tutoring and explanations and generate educational content in various subjects.
- Business Documents: It can also process business-specific documents, such as reports, emails, contracts, and policy documents, to provide summaries, generate responses, or automate certain tasks like drafting emails or generating reports.
Note: Azure OpenAI service is part of the Azure AI, which includes other services such as Azure Cognitive Services, Azure Machine Learning, Azure Bot Service, etc. By using Azure OpenAI service, you can leverage the power of large-scale models with the reliability & high security of Azure.
Data Types
The Azure OpenAI Service processes a variety of data types, focusing on enhancing and personalizing the AI experience for its users. Here’s a breakdown of the types of data it processes:
Prompts and Generated Content
The service processes text prompts that users submit. These prompts can be questions, statements, or any text input from which the user expects a generated output. The service then generates content based on these prompts. This content can be in the form of text completions, chat responses, images, or embeddings. The process involves using different operations like completions, chat completions, images, and embedding operations to produce the desired output.
Augmented Data with Prompts
When utilizing the “on your data” feature, Azure OpenAI Service can augment the prompts with relevant data retrieved from a user-configured data store. This means the service can access specific data provided by the user, such as documents, databases, or any structured data, and use this information to enhance the generated content. This augmentation helps in producing responses or content that is more relevant and grounded in the specific context or data of the user.
Training & Validation Data
Users have the option to provide their own training and validation data to the Azure OpenAI Service. This data consists of prompt-completion pairs, which can be used for the purpose of fine-tuning an OpenAI model. Fine-tuning allows users to customize the model’s responses to better align with their specific needs, terminologies, or scenarios. By providing this tailored data, users can guide the AI to understand and generate outputs that are more closely aligned with their expectations or the nuances of their domain.
This approach allows Azure OpenAI Service to offer a highly customizable and versatile platform for generating AI-driven content, ensuring that outputs can be closely tailored to meet the specific needs and contexts of its users.
Data Processing
Azure OpenAI Service processes data in several ways, primarily through its integration with various AI models provided by OpenAI. The service leverages these models to analyze, interpret, generate, and transform data based on the specific tasks it’s assigned.
Data Input
- Users submit text data to the Azure OpenAI Service for text generation, summarization, translation, or analysis tasks. This data can come from various applications, websites, or other Azure services.
- The data input can be customized according to the specific requirements of the task, including setting parameters for content filtering, output length, and style.
Processing and Model Interaction
- Once the data is received, the Azure OpenAI Service processes it using the selected model. Azure OpenAI offers access to various OpenAI models, including the latest versions of GPT, Codex, and others.
- The service leverages Azure’s robust computing infrastructure to ensure efficient processing, even for complex queries or large volumes of data.
Content Filtering and Compliance
- Azure OpenAI includes built-in content filtering features to help users comply with policies and regulations. It screens the input and generated content for potentially unsafe or inappropriate material, protecting against misuse.
- Users can configure the level of content filtering based on their needs, adjusting for more or less strict content moderation.
Data Output
- The processed data is returned as output to the user. This output can take various forms, depending on the task—from the generated text for content creation to summaries, translations, or other forms of the analyzed text.
- The output is designed to be directly usable or easily integrated into downstream applications or services.
Security and Privacy
- Azure OpenAI Service is built with a strong emphasis on security and privacy. It adheres to Azure’s comprehensive security framework, which includes data encryption at rest and in transit, network security, and identity and access management.
- Users can control their data, manage access, and monitor usage through Azure’s security and governance tools.
Integration and Scalability
- The service is designed for easy integration with other Azure services, allowing for the creation of complex, scalable applications that leverage AI capabilities alongside other Azure resources like storage, databases, and analytics services.
- Scalability is a crucial feature, with the Azure infrastructure supporting dynamic scaling based on demand, ensuring that applications remain responsive and cost-efficient.
The Inferences and Training Approach
Inferences
- Model Selection: Azure OpenAI Service allows users to select from various models depending on their needs. For example, GPT-3 for natural language understanding and generation, Codex for code, and DALL·E for image generation.
- Task Execution: Once the data is input and the model is selected, the service executes the specified task. This could involve generating new text, creating an image, or suggesting code completions.
- Response Retrieval: The results are then sent back to the user via the API. These results are based on the model’s inference, derived from its trained knowledge base.
Training
- Pre-trained Models: The models available in Azure OpenAI Service are pre-trained by OpenAI on diverse and extensive datasets. Depending on the model, this training involves unsupervised learning on a vast corpus of text, images, or code.
- Fine-tuning: Although the core service uses pre-trained models, OpenAI and Azure provide capabilities for users to fine-tune models on their specific datasets. This allows for more tailored responses and improved accuracy for specialized tasks.
- Continuous Learning: While the initial models are static, OpenAI continually researches and develops enhancements. Newer versions of models may be introduced over time, incorporating the latest AI research and feedback from real-world usage.
Azure OpenAI Service combines the cutting-edge AI capabilities of OpenAI’s models with the robust cloud infrastructure of Azure, allowing for efficient data processing, sophisticated inferences, and leveraging a state-of-the-art training approach that includes broad and specialized knowledge acquisition.
This integration ensures that users can harness powerful AI tools in a scalable, secure, and compliant manner, catering to a wide range of applications and industries.
The diagram below illustrates how data is processed and covers three different types of processing:
- How the Azure OpenAI Service processes your prompts to generate content.
- How the Azure OpenAI Service creates a fine-tuned model with your training data.
- How the Azure OpenAI Service and Microsoft personnel analyze prompts, completions, and images for harmful content and for patterns suggesting the use of the service in a manner that violates the Code of Conduct or other applicable product terms
This image is part of the Data, privacy, and security for Azure OpenAI Service from Microsoft Learn.
The Inference Process
- A “Your App” block indicates where prompts (inputs) are generated.
- These prompts are sent to the “Azure OpenAI Completions, Embeddings, Images APIs,” where the input is processed.
- There are two paths from this block:
- One leads to “Synchronous Content Filtering” and then returns the response to “Your App.”
- The other bypasses content filtering and directly sends completions, embeddings, or responses to “Your App.”
Surrounding the inference process is the “Azure OpenAI Service boundary,” within which:
- Asynchronous processes like abuse monitoring and a 30-day retention window for prompts and responses are highlighted.
- There is a note indicating that customers can apply to modify abuse monitoring features through a provided URL.
The Finetuning Part
- “Customer Data” is used as the input for finetuning.
- This data is uploaded to “Azure OpenAI Files and Finetuning APIs.”
- It goes through a “finetuning job” process and is stored in “Files storage,” which is specified as resource-specific and double encrypted.
- The output of this process is a “Finetuned Model.”
The flowchart emphasizes secure data processing and options for content filtering and abuse monitoring within the Azure OpenAI service framework.
References
Data, privacy, and security for Azure OpenAI Service – Azure AI services | Microsoft Learn
Develop Generative AI solutions with Azure OpenAI Service – Training | Microsoft Learn
Security Best Practices for GenAI Applications in Azure – TechCommunity
