Hybrid AI Agent for Secrets Detection

Hard-coded secrets are one of the most critical security vulnerabilities in software development. A single exposed API key, database password, or authentication token can lead to devastating breaches, unauthorized access, and compliance violations. Yet traditional static analysis tools have struggled with a persistent problem: too many false positives that bury real threats in noise. This has been true for DeepSource's Secrets Analyzer as well — just like every other secrets detection engine on the market.

Today, we're excited to announce that DeepSource's secrets analyzer now runs on a hybrid AI agent architecture powered by Narada, our open-source secrets classification model. This new engine is available now for all customers on DeepSource Cloud.

Why we built this

Traditional regex-based secrets detection is fast but fundamentally limited. It flags potential secrets based on patterns alone, without understanding context. This leads to two problems:

1. Alert fatigue: Developers are overwhelmed by false positives—test credentials, example code, and configuration templates get flagged alongside real secrets
2. Missed threats: Without semantic understanding, sophisticated or obfuscated secrets can slip through

The result? Teams either ignore alerts or spend precious engineering time manually triaging hundreds of false alarms. Neither is acceptable when one exposed secret can compromise your entire infrastructure.

A smarter approach

Our hybrid AI agent combines the speed of regex-based detection with the contextual intelligence of AI. Here's how it works:

• Fast pattern matching identifies potential secrets in your codebase
• AI-powered classification analyzes each match with semantic understanding, distinguishing real secrets from false positives

The results speak for themselves. In our benchmarks, Narada achieved:

• 93% reduction in false positives (down from 222 to just 16 false positives)
• 97% precision in detecting genuine secrets and credentials
• 96.3% recall ensuring real threats don't slip through

This means your team sees fewer irrelevant alerts while catching more actual security risks. It's the accuracy you need without the noise you don't.

Why this matters for your security posture

Every false positive erodes trust in your security tooling. When developers see too many false alarms, they start ignoring all alerts—including the critical ones. This "alert fatigue" is a well-documented problem in security operations.

With the hybrid AI agent, you get:

• Faster remediation: Engineers can trust the alerts they see and act immediately
• Better coverage: Catch secrets that pattern matching alone would miss
• Reduced risk: Real threats surface faster without being buried in false positives
• Developer productivity: Less time triaging alerts means more time building features

Whether you're working with AWS credentials, API tokens, database passwords, or private keys, the hybrid engine provides the accuracy needed to maintain a strong security posture without slowing down your development workflow.

Available now on DeepSource Cloud

The hybrid AI agent is live and available to all customers on DeepSource Cloud. Team administrators can enable it by navigating to Settings → General → Preferences in their team settings and selecting the Hybrid AI Agent engine.

The new engine is now the default for all newly created teams, and we'll be deprecating the legacy static-only engine in the coming months. If you're currently using the legacy engine, we strongly recommend switching to the hybrid architecture to benefit from improved accuracy and reduced false positives.

Want to learn more about the technology behind Narada? Check out our detailed technical deep-dive on the model architecture and benchmarks.

Contact our sales team to learn how DeepSource can strengthen your organization's security posture, or sign up and take it for a spin right away!