Okta AI agent for Natural Language Querying

The Problem: The Complexity of Okta Data Retrieval

While Okta’s Admin Console provides a comprehensive interface, retrieving specific information often requires navigating multiple menus and filters. Generating custom reports or answering ad-hoc questions can be a manual and inefficient process. This is where the AI Agent for Okta comes in.

The Solution: Natural Language Interaction with Your Okta Tenant

The AI Agent for Okta (https://github.com/fctr-id/okta-ai-agent) allows you to query your Okta tenant using plain English. Instead of crafting complex API calls or clicking through the console, you can simply ask questions like:

• “Show me the users created in the last 2 days with created timestamp”
• “Which applications are assigned to the ‘Finance’ group and use SAML?”
• “Show me the users who have push enrolled but NOT sms”
• “What is the status of user Jane.Doe@example.com?”
• “Show me the users who are locked out and their last updated timestamp”

This capability significantly reduces the time and effort required to access critical Okta data.

Key Features for the IAM Professional

Beyond the core natural language querying, the AI agent for Okta offers several features that are particularly relevant to IAM professionals:

• Multiple AI Provider Support: This is a critical feature for enterprise environments. The agent supports:

  • Google Vertex AI (Gemini 1.5 Pro)
  • OpenAI (GPT-4)
  • Azure OpenAI (GPT-4)
  • Ollama (for local, self-hosted deployments using 32B+ models – ideal for maximum data privacy)
  • OpenAI-Compatible APIs (Fireworks, Together AI, OpenRouter, etc.)

  This flexibility allows you to choose the AI provider that aligns with your organization’s policies, security requirements, and budget. The Ollama support is particularly noteworthy for organizations with strict data residency requirements.

• Fast Data Sync: The agent utilizes parallel data fetching to quickly synchronize your Okta data into a local SQLite database. This ensures that queries are answered rapidly, without constant calls to the Okta API.

• Data Model Transparency: The agent stores information about key Okta entities, including Users, Groups, Applications, UserFactors, and Policies. The full data model is documented in the README, and you can directly inspect the SQLite database using tools like DB Browser for SQLite. This transparency is essential for understanding how the agent works and ensuring data integrity.

Technical Implementation: A Python-Based Approach

The AI agent for Okta is built using Python 3.12+, a language widely used in the IAM and security communities. This makes the project accessible for customization, extension, and integration with existing workflows. The setup process is straightforward:

Install Instructions

Click here for Instructions

High Level Architecture

The agent’s architecture prioritizes data privacy. All Okta data is stored locally in the SQLite database. Only the natural language query and the database schema (table and column names) are sent to the chosen LLM. This minimizes the risk of sensitive data exposure.

Security Considerations: A Top Priority for IAM

For IAM professionals, security is paramount. The AI agent for Okta is designed with several security best practices in mind:

• Local Data Storage: As repeatedly emphasized, your Okta data never leaves your machine. This is the most crucial security feature.
• Controlled API Access: You control the Okta API token, including its permissions and network restrictions.
• Least Privilege: Create a dedicated Okta service account with the absolute minimum required permissions (read-only access is often sufficient, although Super Admin is needed for the initial data sync).
• Open Source Scrutiny: The project’s open-source nature allows for community review and identification of potential vulnerabilities.
• LLM choice: Choose between enterprise-approved AI providers, or deploy a model locally, having full control.

Data Privacy Breakdown:

• Details Sent to LLMs: User queries, system prompts
• Never Sent to LLMs: Your Okta user data, organizational information, or the contents of the synced database.

Future-Proofing IAM: The Roadmap

The AI agent for Okta is currently in beta, but the roadmap indicates a strong commitment to expanding its capabilities:

• Real-time Data Integration: Moving beyond periodic syncs to interact with real-time Okta data and APIs.
• Event Log Analytics: Enabling natural language queries against Okta System Logs, opening up possibilities for security incident investigation and reporting. This is a highly valuable feature for IAM teams.
• Managed Changes (with Approvals): The potential to move beyond read-only queries to actionable commands, such as modifying group memberships or application assignments, with appropriate approval workflows. This would represent a significant step towards AI-driven IAM automation.

Conclusion: A Powerful Tool for the Modern IAM Professional

The AI agent for Okta offers a compelling vision for the future of Okta administration. By combining the power of natural language processing with a strong focus on security and data privacy, it provides IAM professionals with a valuable tool to enhance their efficiency, gain deeper insights, and streamline their workflows. We encourage you to explore the project on GitHub (https://github.com/fctr-id/okta-ai-agent), contribute to the community query knowledge base, and consider how this innovative approach can transform your Okta management practices. If you have ideas or suggestions, open a feature request. For questions, reach out to support@fctr.io, or for contributing, reach out to dan@fctr.io.