Exorcising the False Positives in Secrets Detection with AI Agents

Most tools are designed to catch anything that looks like a secret—whether it’s a real credential or just a UUID, test key, or random file name. The result? Security teams get buried in alerts. Developers lose trust. And critical exposures slip through the cracks.

At Ghost Security, we set out to fix this—not by tightening regex or building a slightly better scanner. We built an entirely new architecture for secrets detection that separates detection from validation—and adds AI into the loop to cut false positives by an order of magnitude.

The Real Problem with Secrets Detection

Traditional scanners try to match known secret formats with regex. Some use entropy scores to flag random-looking strings. A few hybrid approaches combine both. These engines are optimized for recall—catching anything that might be a secret—but they don’t understand context.

They can’t tell if:

• The “password” variable is a placeholder

• The token is actually used in authentication

• A key is a dummy value in a test suite

Security teams are forced to choose:
Do you accept the noise and spend hours triaging?
Or tighten the rules and risk missing something real?

Neither scales.

What Makes Ghost Different

We built Poltergeist as a fast, high-recall engine that flags potential secrets. But what happens after detection is where the real innovation begins.

Each match is passed to a specialized AI Secret Agent—a lightweight model that evaluates code context, usage, and intent.

These agents review:

• How the value is used downstream

• Whether it's actually part of an authentication or encryption flow

• The surrounding code to determine if exposure would cause harm

• The likelihood that it's dummy data, a test artifact, or something benign

It doesn’t just ask: “Does this look like a secret?”
It asks: “Is this a real secret, in a real place, that matters?”

That distinction changes everything.

From Noise to Signal

In real-world testing, we’re seeing massive reductions in false positives—often down to less than 5%, compared to the 30–70% false positive rates seen in traditional tools. That means developers get alerts they trust. Remediation happens faster. And security teams aren’t stuck babysitting scanners.

Instead of “possible secrets,” Ghost only sends findings that are high-confidence and validated by AI.

Why This Works Now

Context-aware secrets detection wasn’t possible a few years ago. It’s working now because:

• AI models can reason over code—not just syntax, but semantics

• Our candidate extraction is fast enough that the AI only sees what’s worth checking

• The agents are trainable on real-world feedback, so accuracy improves continuously

This system doesn’t just scan and alert—it thinks. And that changes how secrets detection fits into modern security workflows.

Built for the Way You Work

Ghost integrates directly with your engineering stack—so once a valid secret is found, it can automatically trigger a webhook, alert your SIEM or SOAR platform, or open a Jira ticket. No more wasted cycles. No more guessing.

Just signal. Delivered fast, with context, and at scale.

This isn’t a faster scanner. It’s a smarter system.