Can AI triage hide real vulnerabilities?

Oryon is designed to be conservative. A finding is only dropped if both AI passes agree. On conflict or uncertainty, it is kept.

Do shared suppressions replace review?

No. They capture decisions the team has already made so future scans can reuse that context instead of starting from zero.

Can Oryon coexist with existing CI scanners?

Yes. Many teams can keep broader CI or platform scanners while using Oryon to improve signal quality and triage earlier inside the developer workflow.

Signal quality

Reducing SAST False Positives Without Hiding Real Findings

For most teams, the hard part is not running a scanner. It is deciding which findings are trustworthy enough to review, which ones should be suppressed, and how that decision survives the next scan.

Talk to engineering See shared security memory

Local-first code security· Comparison library

Reduce SAST False Positives

Why SAST false positives become an operating problem

A heuristic prefilter removes baseline noise before AI review even starts.

The main blocker is trust in the findings, not scanner coverage alone.
Your developers need lower-noise reviews inside the editor before CI becomes the bottleneck.
You want suppressions and repository history to improve the signal over time.

What breaks trust

Why SAST false positives become an operating problem

What teams usually see

Developers stop trusting the scanner because too many findings are obviously harmless or poorly prioritized.
AppSec becomes the manual bottleneck for deciding what matters and what should be ignored.
The same false positives come back in future scans because the decision is not shared and persistent.

Workflow

How noise is reduced without turning the system into a black box

Drop harmless baseline noise first

What happens

Oryon applies heuristics before AI review so the most obviously low-value findings do not consume the expensive review path.

Why it matters

That keeps the triage layer focused on the findings where context and judgment matter most.

Use strict AI consensus before dropping anything

What happens

The AI triage runs in two passes. A finding is only dropped if both passes independently agree that it should go.

Why it matters

This makes the system more conservative and lowers the risk of hiding real issues under an aggressive noise filter.

Persist the decisions that should survive

What happens

Shared suppressions and dashboard history let future scans inherit the right context instead of forcing the team to relitigate the same harmless findings.

Why it matters

Real signal quality comes from both filtering and memory, not from one noisy scan after another.

Best fit

When Oryon is a better answer to false-positive fatigue

Choose Oryon if

The main blocker is trust in the findings, not scanner coverage alone.
Your developers need lower-noise reviews inside the editor before CI becomes the bottleneck.
You want suppressions and repository history to improve the signal over time.

Choose something else if

Your top priority is centralized policy administration rather than developer signal quality.
The team is willing to accept more review noise in exchange for the widest possible platform scope.
You do not need the security loop to start in the editor.

FAQ

Questions teams ask when false positives are the main blocker

Can AI triage hide real vulnerabilities?: Oryon is designed to be conservative. A finding is only dropped if both AI passes agree. On conflict or uncertainty, it is kept.
Do shared suppressions replace review?: No. They capture decisions the team has already made so future scans can reuse that context instead of starting from zero.
Can Oryon coexist with existing CI scanners?: Yes. Many teams can keep broader CI or platform scanners while using Oryon to improve signal quality and triage earlier inside the developer workflow.

Why SAST false positives become an operating problem

What teams usually see

How noise is reduced without turning the system into a black box

Drop harmless baseline noise first

Use strict AI consensus before dropping anything

Persist the decisions that should survive

When Oryon is a better answer to false-positive fatigue

Choose Oryon if

Choose something else if

Questions teams ask when false positives are the main blocker

Preferences