From LLMs to Agents: How BigID is Enabling Secure Agentic AI for Data Governance

Understanding Large Language Models (LLMs)

Large Language Models (LLMs) form the foundation of most generative AI innovations. These models are predictive engines trained on massive datasets, often spanning hundreds of billions of tokens. For example, ChatGPT was trained on nearly 56 terabytes of data, enabling it to predict the next word or token in a sequence with remarkable accuracy. The result is an AI system capable of generating human-like text, completing prompts, answering questions, and even reasoning through structured tasks.

At their core, LLMs are not databases of facts but statistical predictors. They excel at mimicking natural language and surfacing patterns seen in their training data. However, they are static once trained. If a model is trained on data that is five or ten years old, it cannot natively answer questions about newer developments unless it is updated or augmented with real-time sources. This limitation makes pure LLMs insufficient in enterprise contexts where accuracy, compliance, and timeliness are critical.

From LLMs to Agents

The next evolution is the concept of agents. Unlike a simple LLM, an agent has autonomy: it can decide when to call external tools, how to sequence those calls, and how to take real-world actions instead of merely generating text responses. This agentic behavior transforms AI from a passive assistant into an active participant in workflows.

For example, while an LLM might explain how to find sensitive data in a file system, an agent integrated with tools like BigID can actually run a search, classify the data, and present the results directly. This ability to connect intent with execution is what makes agents so powerful in enterprise settings.

LLM vs. Agent: Key Differences

MCP Server: The Backbone of Agentic AI

To enable agents to act, there must be a standard way to connect them with enterprise tools. This is where the Model Context Protocol (MCP) comes in. MCP acts as middleware between the LLM (or agent) and enterprise systems. It defines how tools are exposed, how they can be invoked, and how results are returned.

However, MCP is still evolving. There is no universal packaging method, and implementations vary across vendors. Some use lightweight specifications, while others bundle tools differently, which can cause interoperability challenges. Frequent updates to MCP standards also make it difficult for enterprises to keep up.

BigID’s Agentic AI Solution

BigID addresses these challenges with its Agentic Automation App, which packages an MCP server together with a Gemini LLM and delivers it as a deployable BigID application. This eliminates the need for enterprises to manually manage MCP packaging or integration complexity.

The app allows agents to call tools directly within BigID. For instance, an agent can be asked to “find all clear-text passwords across connected data sources,” and instead of generating a generic answer, the agent uses BigID’s data discovery engine to run the scan and return actionable results. Similarly, it can generate risk reports, identify PII across catalogs, or connect findings to governance workflows.

Business Value of BigID Agentic AI

BigID’s integration of Agentic AI creates tangible business outcomes across three dimensions:

Reduced Operational Costs

Repetitive tasks such as classifying data, running discovery scans, or preparing compliance reports often consume significant staff time. With agentic automation, these tasks are delegated to AI agents, drastically reducing manual intervention. The result is lower operational costs and freed-up resources for higher-value activities.

Increased Scalability

Unlike human analysts, agents can operate across multiple data sources, tools, and environments simultaneously. They scale without requiring retraining or manual coordination, making them suitable for enterprises managing hundreds of systems and millions of records.

Accelerated Decision-Making

By connecting to enterprise systems in real time — whether BigID data stores, Slack channels, or document repositories — agents surface insights faster. Decision-makers can ask natural-language questions such as “Which vendors host customer PII in the cloud?” and receive structured, data-backed responses in seconds.

Key Use Cases for BigID Agentic AI

1. Data Discovery and Classification

Agents can proactively search across connected BigID data sources to locate sensitive elements such as clear-text passwords, credit card numbers, or personal identifiers. The Gemini LLM interprets the natural-language query and calls the appropriate BigID tools, providing not just answers but evidence-backed results.

2. Governance and DSPM Integration

Agents respect governance rules by integrating with Data Security Posture Management (DSPM). This ensures they access only labeled and governed datasets. Sensitive datasets can be tagged, and access can be restricted, enforcing compliance while still enabling discovery.

3. Retrieval-Augmented Generation (RAG)

By combining LLM capabilities with enterprise search (e.g., Microsoft Graph), agents retrieve the most relevant documents before generating responses. This guarantees context-rich and accurate answers tailored to organizational data rather than generic output.

4. Tool Calling for Dynamic Actions

Instead of static answers, agents actively call external APIs or enterprise tools. They can query financial systems, run searches in collaboration platforms like Slack, or pull from BigID catalogs. This transforms queries into real-time, context-aware responses.

5. Agentic Automation App Deployment

The solution is installed like any other BigID app via a documented URL. It uses Google’s Gemini via Vertex AI by default, ensuring no user data is stored or used for retraining. Enterprises can also plug in their own Gemini API key for full control over governance and compliance.

Governance and Security Considerations

A critical insight is that blocking external AI tools like ChatGPT is not enough. Agents can still access open data sources unless governance controls are in place. Firewalls and DLP tools such as Zscaler or Netskope help, but true control begins with understanding and labeling enterprise data.

BigID ensures security by restricting agents to read-only tools. They cannot delete or modify data, reducing the risk of unintended actions. Furthermore, proper DSPM labeling ensures agents operate responsibly, accessing only data appropriate for their role.

API Access Limitations

At present, interactions with BigID’s Agentic AI are limited to the BigID interface. Questions cannot yet be sent directly via API. However, roadmap developments are expected to expand integration points in the future, further embedding agentic automation into enterprise workflows.

The Strategic Advantage

Agentic AI represents the next evolution beyond traditional LLMs — moving from static prediction engines to autonomous systems capable of acting across enterprise environments. By combining BigID’s Agentic Automation App, the MCP framework, and the Gemini LLM, organizations can unify data discovery, governance, and compliance into a scalable, automated ecosystem.

This approach not only reduces operational overhead but also accelerates decision-making with real-time, context-aware insights. As enterprises adopt AI responsibly, solutions like BigID’s Agentic AI offer a secure path forward — balancing innovation with governance and empowering teams to turn data into trusted, actionable intelligence.