Navigating AI Security: How Access Controls Can Make a Difference

14 min readAug 28, 2024

As companies increasingly integrate Large Language Models (LLMs) into their daily operations, they face a crucial challenge: ensuring that their AI Agents and ChatBots don’t accidentally leak sensitive information. A key concern is prompt injection, where bad actors manipulate LLMs to spill secrets they’re supposed to keep.

To combat this, it’s not just about setting up firewalls or updating security patches — it’s about building a solid framework around who can access what information through the LLM. This means creating clear, enforceable rules that determine how, when, and to whom data is accessible.

This guide will walk you through the steps to tighten security around your LLMs. We’ll discuss how to tag your training documents with metadata that defines access levels and how to craft policy documents that guide the LLM on these rules. With these strategies, you can make sure your LLM serves your business effectively without compromising on security.

Understanding Prompt Injection Attacks

Prompt injection attacks are subtle yet significant threat in the AI world, especially with advanced systems like Large Language Models (LLMs), including OpenAI Assistant. This method involves crafting inputs that manipulate the AI into revealing confidential information it’s supposed to protect.

Crafty Inputs: Hackers craft questions that exploit weaknesses in the AI’s response mechanisms. They design these queries to appear harmless while subtly probing for sensitive data.
Learning from Responses: By observing how the AI responds to these crafted inputs, attackers refine their methods, making them more effective at extracting information they shouldn’t access.

Case in Point: The “Sandwich Attack”

A prime example of prompt hacking is the “Sandwich Attack,” highlighted in research by Bibek Upadhayay and Vahid Behzadan.

This technique involves embedding a malicious query among benign ones within a sequence of multilingual questions. This method exploits the AI’s weaker performance in less common languages, camouflaging the harmful intent among regular queries, thus deceiving the AI into compromising its safeguards.

Prompt hacking poses several significant risks to organizations using AI systems. These risks include:

Data Leaks: Unauthorized access to sensitive information can lead to significant data breaches.
Compliance Violations: Such breaches could result in legal repercussions if they violate regulations like GDPR.
Erosion of Trust: Discovering that an AI system can be manipulated may lead customers to lose trust in the organization’s ability to safeguard their information.

Think of security as a high-stakes game of cat and mouse, where the rule is to always stay one step ahead. While no defense can promise absolute safety, implementing strong access controls is your best move to outsmart potential threats. By ensuring that only the right people have access to your AI systems, you make it much harder for attackers to gain a foothold.

The Art of Access Control in AI

As AI becomes a core part of business operations, the challenge of protecting sensitive information intensifies. Access control isn’t just a barrier — it’s a strategic play that decides who gets in and who stays out. This section dives into the essentials of crafting effective access controls for leveraging Large Language Models (LLMs).

Imagine your AI Agent or ChatBot as the gatekeeper to a high-tech office building. When a customer makes a request, the AI needs to know which “rooms” it can access to fetch the right information. Just like a security guard deciding who can enter which rooms, access control ensures that your AI Agent only accesses and shares the data it’s authorized to handle, serving customer requests securely and accurately.

Types of Access Control

Role-Based Access Control (RBAC): Think of RBAC like your AI Agent having a predefined list of what it can access based on its “role.” For instance, if your AI is serving as a customer support agent, it might have access to order histories but not to confidential employee records. This method is simple and effective for broad control but lacks the flexibility needed for more nuanced situations.
Attribute-Based Access Control (ABAC): ABAC takes it further by allowing the AI Agent to make access decisions based on specific attributes, such as the user’s identity, the data type, or even the time of day. For example, an AI Agent might be able to access detailed customer data during business hours but have restricted access after hours. This approach offers greater flexibility and can adapt to different contexts.
Policy-Based Access Control (PBAC): PBAC goes beyond ABAC by using policies that define complex rules for access. Imagine your AI Agent needs to access financial data, but only if the user is a verified manager, during a financial review period, and within a secure network. PBAC allows you to set up such detailed conditions, ensuring that your AI Agent follows strict protocols before granting access..

Why PBAC Offers More Granular Control

While ABAC allows access decisions based on attributes, PBAC adds an extra layer by enabling you to define specific policies that combine multiple conditions. For example, ABAC might allow access to sensitive documents if the user has the correct clearance level. Still, PBAC could further restrict this access to only occur during specific times or under certain circumstances, like when the request comes from a secure device.

This level of control is crucial when dealing with sensitive data, as it ensures that access is granted only when all the necessary conditions are met. PBAC is particularly valuable in scenarios where you need to enforce complex business rules and ensure that your AI Agent operates within tight security parameters.

Setting Up Access Control for AI

Define Access Needs : Start by identifying which data your AI agent will handle and who should have access to it. This involves classifying data based on its sensitivity and mapping out user roles. For example, customer support agents might need access to order histories but not financial records.

2. Implement Control Mechanisms: Choose an access control model that suits your AI agent’s operating environment. For straightforward tasks, Role-Based Access Control (RBAC) might be enough. However, for dynamic environments where access needs frequently change, Attribute-Based Access Control (ABAC) or Policy-Based Access Control (PBAC) could be more appropriate. These models offer the flexibility needed to handle complex scenarios and ensure that access is granted based on real-time factors.

3. Integration with AI When integrating access control with AI, particularly in systems using Retrieval-Augmented Generation (RAG), you focus on tagging and structuring your documents so the LLM can recognize and respect access levels.

Document Tagging and Structuring: Begin by tagging your documents with metadata that indicates the access level required to retrieve or use the information. For example, a customer’s order history might be tagged as “General Access,” while sensitive financial data is tagged as “Restricted Access.” These tags help the AI agent understand the sensitivity of the information.
Leveraging RAG: In a RAG setup, when a user makes a request, the AI first retrieves relevant documents based on the query. By respecting the tags you’ve applied, the AI ensures it only accesses and returns information that the user is authorized to see. This method keeps the AI’s responses aligned with your security policies without modifying the model itself.
Structured JSON Responses: The LLM can be configured to return structured JSON responses that include not only the requested information but also metadata about the access level of the data retrieved. This structured output allows you to analyze the AI’s response before it is presented to the user, adding an additional layer of security. For instance, if a user requests data beyond their access level, the AI’s response can be flagged or altered before final delivery.
Creating an Access Level Tool: On top of this, you can build an access management tool that interprets these JSON responses to enforce access control dynamically. This tool would review the structured data returned by the AI, compare it against the user’s access permissions, and determine whether the information should be displayed, redacted, or require further approval. This system ensures that even as the AI provides intelligent, context-aware responses, it remains within the boundaries of your access control policies.

High Level Design ( by Auhtor)

Integrating Metadata for Attribute-Based Control

Incorporating metadata into training documents can significantly enhance data security and access control. Metadata essentially acts as data about data, providing additional details that help manage and control access effectively.

It can include details about the data’s origin, sensitivity level, intended use, and access restrictions. By embedding this metadata directly into training documents, organizations can enforce security policies automatically and maintain data integrity across AI interactions.

Creating Metadata-rich Training Documents

Defining Metadata Attributes: Determine what attributes are essential for your organization’s data management needs. Common attributes might include confidentiality level, data type (e.g., personal, financial, operational), and access permissions.
Embedding Metadata: Integrate these attributes into your AI’s training documents. For instance, each document can be tagged with metadata fields like {“confidentiality”: “high”, “data_type”: “personal”, “access_level”: “HR_only”}.
Automation Tools: Utilize tools and software that can automate the process of metadata tagging to ensure consistency and accuracy. These tools can analyze document content and assign metadata based on predefined rules.

Example of Metadata Implementation

Consider a company that uses OpenAI Assistant to process customer interactions and internal requests:

Customer Interaction Logs: Metadata tags such as {“data_type”: “customer_interaction”, “access_level”: “customer_service”} can be used to ensure that only customer service agents can access these logs.
Internal Financial Reports: These documents might include tags like {“confidentiality”: “high”, “data_type”: “financial”, “access_level”: “finance_department”} to restrict access to the finance department only.

Benefits of Using Metadata

Enhanced Security: By controlling document access through metadata, sensitive information is better protected against unauthorized access.
Improved Compliance: Metadata helps ensure that data handling complies with legal and regulatory standards, reducing the risk of fines and legal issues.
Efficiency in Data Management: Automated metadata tagging streamlines the process of managing access to large volumes of data, making it easier and more efficient for organizations to control who sees what information.

Integrating metadata into AI training documents is a powerful way to enhance the security and efficiency of your AI applications. By carefully defining, embedding, and managing metadata, organizations can ensure that their AI systems operate within the confines of established data access and security policies.

The Role of Policy Documents

Policy documents set the rules that govern data access based on metadata tags embedded within training documents. They ensure that the AI system adheres to predefined security protocols, granting or denying access according to the stipulations outlined.

Designing Policy Documents

Identifying Requirements: Understand and define what data needs protection and who should have access to it. This involves identifying various user roles and their data access needs.
Drafting Policies: Develop policies that specify how different metadata tags relate to access levels. For example, a document tagged as {“confidentiality”: “high”} might be restricted to senior management only.
Dynamic Access Controls: Incorporate conditions that can change based on context, like project phases or time-based access restrictions, to adapt to varying operational needs.

Utilizing YAML for Policy Documents

Using YAML to define these policies is highly recommended due to its readability and ease of use in configuration management:

YAML Format: Policies can be clearly defined in a structured format that both technical and non-technical staff can understand and manage.
Scalability and Maintenance: YAML files are easy to update and maintain, making them ideal for environments where access needs may change frequently.

Example of a YAML-Based Policy

Here’s an example of how a policy document might be structured in YAML:

Crafting, implementing, and managing policy documents effectively, particularly through user-friendly formats like YAML, is crucial for maintaining secure and efficient operations in AI-driven systems.

Practical Implementation: Case Study

Imagine a company, SpaceTech Inc., which specializes in satellite technology and space exploration. They have recently integrated an LLM into their internal system to manage and disseminate critical information related to their latest project — Satellite X. The goal is to use the assistant to efficiently handle and secure sensitive data across various departments, including mission control, engineering, and executive leadership.

Objective: The primary objective is to demonstrate how to:

Embed Metadata: Incorporate metadata directly into the content of documents to specify access control and policy information.
Create Policy Documents: Develop policy documents that govern access based on the metadata tags embedded in the documents.
Ensure Compliance: Implement a system that references these metadata tags and policy documents to enforce access control dynamically.

Creating the Training Document

To achieve these objectives, I created a training document for SpaceTech Inc.’s Satellite X project. The training document is structured to include various sections such as the mission blueprint, technical specifications, operational protocols, and safety management strategies. Each section is embedded with metadata that dictates the level of access required to view or modify the content.

Sample Word Document with MetaData

In first example, the metadata ensures that only users with the “Executive Review” role can access this sensitive information. This is crucial for maintaining the confidentiality of high-level mission details.

In Technical Specifications, the metadata restricts access to members of the Engineering Team, ensuring that only those involved in the technical aspects of the project can view the detailed specifications of Satellite X.

Creating YAML-Based Policy Documents

Now that we’ve embedded metadata tags within the training document to specify access control (e.g., Permitted Access: Engineering Team and Confidentiality Level: Restricted), the next step is to define the corresponding access policies using YAML.

Why Metadata and YAML Policies Work Together

Metadata Tags: These tags are embedded directly in the document sections to label the content with access control information. They ensure that the document itself carries information about who should access it, making the content self-descriptive and portable within the RAG context.

YAML Policies: These policies are used by the system to enforce access controls dynamically. The YAML file defines the rules for who can access different sections based on the metadata. This centralized control allows for flexibility and scalability, as you can update access rules without altering the documents.

How the YAML Policy Dictates LLM Behavior

Access Control: The LLM checks the user’s role against the YAML policy.
Metadata Matching: It matches the user’s role with the metadata in the document.
Enforcement: If the metadata and policy align, access is granted; if not, it’s denied.

Access Validator Tool: A 2FA for AI

The YAML policy also plays a crucial role in our Access Validator Tool, which acts like a two-factor authentication (2FA) system for AI. Here’s how it works:

Validation Process: Before the LLM returns a response, the Access Validator Tool checks the YAML policy to ensure that the user’s role is authorized to access the requested information. It’s a secondary check to confirm that the LLM’s initial decision matches the strict access controls defined in the YAML policy.

Security Enhancement: Just like 2FA adds an extra layer of security beyond a simple password, the Access Validator Tool ensures that even if the LLM processes the metadata correctly, there’s a final validation step. This minimizes the risk of unauthorized access and adds robustness to the system, particularly in environments where data sensitivity is critical.

Query Verification and Role-Based Access Control

To verify that the embedded metadata tags were functioning as intended, I ran several test queries using different user roles. The system was designed to check the user’s role against the metadata tags before returning any information.

The system provided the correct information, including metadata tags, indicating that the user had the appropriate access level.

Image by Author — Chat with OpenAI Assistant

The system correctly denied access, stating that the mission purpose is tagged for “Executive Review” only.

The system correctly provide the access to the engineering team.

The system should grant access, as the user is in the “Mission Control Team” role, which has access to the “Operational Protocols” section with a confidentiality level of “Restricted.”

By combining metadata tags, YAML policies, and the Access Validator Tool, We ensure that access control is not only descriptive but also enforceable, providing a robust, scalable system to protect sensitive information — much like a 2FA system does for traditional security.

Security Enhancements and Compliance

As we implement access control mechanisms using metadata tags, YAML policies, and the Access Validator Tool, it’s crucial to ensure that these elements are secured and comply with data protection laws. Here’s how you can safeguard your metadata and policy documents, maintain compliance, and keep your system resilient against evolving threats.

Securing Metadata and Policy Documents

Encryption: Ensure that both metadata within documents and YAML policy files are encrypted. This prevents unauthorized access to sensitive information, even if someone gains access to the files.

2. Access Controls: Apply strict access controls to the systems where metadata and policy documents are stored. Only authorized personnel should be able to view or modify these critical components.

3. Version Control: Implement version control for policy documents. This allows you to track changes, revert to previous versions if needed, and ensure that unauthorized modifications are easily detectable.

4. Audit Trails: Enable logging and monitoring to create audit trails of all access and modifications to metadata and policy documents. This not only enhances security but also helps in compliance with regulatory requirements.

Conclusion

In this article, we’ve explored the essential steps to integrating secure access control into AI systems, particularly with tools like OpenAI.

Embedding Metadata: We began by embedding metadata tags within documents to label access control information.

Creating YAML Policies: Next, we created YAML-based policy documents to enforce these controls systematically.

Using the Access Validator Tool: Finally, we introduced the Access Validator Tool, which acts as a 2FA for AI, ensuring that all responses from the LLM are validated against the established policies before being delivered to the user.

Together, these strategies form a robust framework for managing and securing sensitive data within AI-driven environments.

Future Trends in AI Security

As AI continues to evolve, so too will the security measures needed to protect sensitive data. Future advancements may include more sophisticated AI-driven security tools that can detect and respond to threats in real-time, as well as more granular and adaptive access control mechanisms. Additionally, the integration of decentralized identity management systems may provide even greater flexibility and security in managing access to AI systems.

Now is the time to review your AI systems and their security measures. Are your access controls strong enough to protect sensitive data? By updating your security measures now, you can reduce risks, ensure data integrity, and prepare your organization for future AI challenges.

Apply these strategies to your AI systems to create a secure and reliable infrastructure that protects your data and boosts your organization’s credibility in a world increasingly driven by AI.