AI Agents in Action: How BigID Secures Data Governance with Intelligent Autom...

Secure Agentic AI for Data Governance: From LLMs to Agents

As we continue to push the boundaries of what's possible with Artificial Intelligence (AI), one area that has garnered significant attention is the integration of Large Language Models (LLMs) into various applications. In this article, we'll delve into the world of LLMs, explore their limitations in enterprise contexts, and examine how BigID is enabling secure agentic AI for data governance.

Understanding Large Language Models (LLMs)

Large Language Models 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.

• Characteristics:
  • Predictive engines
  • Trained on massive datasets
  • Can generate human-like text
  • Can complete prompts and answer questions

Limitations of Pure LLMs

While LLMs excel at mimicking natural language and surfacing patterns seen in their training data, 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.

• Limitations:
  • Static once trained
  • Limited to training data accuracy and timeliness

Introducing Agentic AI

To overcome the limitations of pure LLMs, BigID has developed a new paradigm for secure agentic AI. This approach combines the strengths of LLMs with the dynamic capabilities of agents.

• Agentic AI Characteristics:
  • Dynamic and adaptive
  • Can learn from real-time data sources
  • Can answer questions about newer developments

Implementation Details

To implement agentic AI, BigID uses a combination of natural language processing (NLP) techniques and machine learning algorithms. The process involves the following steps:

1. Data Collection: Gather relevant data from various sources.
2. Model Training: Train an LLM on the collected data.
3. Agent Development: Develop an agent that can learn from real-time data sources and adapt to changing requirements.

Code Snippet:

import pandas as pd

# Load training data
data = pd.read_csv('training_data.csv')

# Split data into training and validation sets
train_data, val_data = data.split(test_size=0.2, random_state=42)

# Train LLM model
model = train_model(train_data)

# Develop agent
agent = develop_agent(model)

Best Practices

When implementing agentic AI for data governance, keep the following best practices in mind:

• Data Quality: Ensure high-quality data is available for training and validation.
• Model Selection: Choose an LLM model suitable for your specific use case.
• Agent Development: Develop agents that can learn from real-time data sources and adapt to changing requirements.

Conclusion

In conclusion, BigID's secure agentic AI approach combines the strengths of LLMs with the dynamic capabilities of agents. By understanding the limitations of pure LLMs and implementing agentic AI, organizations can achieve more accurate and timely results in their data governance efforts. Remember to follow best practices when developing your own agentic AI solutions.

By combining the power of LLMs with the flexibility of agents, we can unlock new possibilities for secure agentic AI and data governance. The future is exciting, and the possibilities are endless!

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By Malik Abualzait