How AI is Revolutionizing Data Security: Druva's Smart Copilot Cuts Investigation Time from Hours to Minutes

How AI is Revolutionizing Data Security: Druva's Smart Copilot Cuts Investigation Time from Hours to Minutes

Data security teams are drowning in alerts and manual tasks. A global financial services company managing 500+ servers used to spend hours checking logs when backups failed. Now, they can simply ask "Why did my backups fail last night?" and get instant analysis with step-by-step fixes in minutes.

This transformation is happening thanks to Druva's new AI-powered copilot, developed in partnership with Amazon Web Services. The system uses advanced language models and multi-agent architecture to handle 90% of routine data protection tasks through natural conversation.

What Makes This AI Copilot Different

Unlike traditional systems that require technical expertise, this copilot works like having a data security expert on call 24/7:

• Instant Troubleshooting: The system analyzes multiple data sources simultaneously to identify root causes and provide personalized solutions
• Smart Policy Management: Users can create and modify data protection policies through simple conversations, reducing human errors
• Proactive Monitoring: The AI continuously watches for potential issues and alerts users before problems escalate

The Technology Behind the Solution

The copilot uses a sophisticated multi-agent system powered by Amazon Bedrock. At its core, a supervisor agent coordinates specialized sub-agents:

• A data agent retrieves information from backup systems
• A help agent provides guidance from extensive knowledge bases
• An action agent executes critical operations with user approval

The system's dynamic API selection process uses semantic search to identify the most relevant tools for each request, ensuring accuracy and efficiency.

Real-World Performance Results

Testing revealed impressive performance metrics across different AI models. Nova Pro achieved 93% accuracy in API selection with just over one-second response times, while larger models like Sonnet 3.5 showed comparable accuracy but with eight-second delays.

Early evaluation with subject matter experts scored the system 3.3 out of 5 for completeness, accuracy, and relevance—solid performance for an early-stage development.

The results speak for themselves: 70% reduction in time-to-resolution for data security issues and troubleshooting times slashed from hours to minutes.

This innovation showcases how conversational AI can transform complex IT operations, making enterprise data protection more accessible and efficient than ever before.

🔗 Read the full article on AWS Machine Learning Blog