Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

With 100x more non-human identities than human identities expected in 2025, the way we manage machine credentials is fundamentally broken. 83% of attacks involve compromised secrets, yet many organizations still rely on hardcoded keys, sprawling secrets, and scattered vault deployments.

GitGuardian is excited to introduce Machine Learning Powered Similar Incident Grouping, which cuts through the noise by identifying incident-specific patterns across your inventory and clustering incidents that belong together, so you can handle repetitive cases efficiently and reduce incident response toil.

The GitGuardian Platform now automatically ranks every secrets incident with a risk score from 0–100, turning alert floods into a prioritized, trustworthy work queue. Scores are computed from incident context (like validity, exposure, where it was found, and exploitability) and build on existing ML capabilities like Secret Enricher and our False-Positive Remover, which cuts false positives by 80%+.

AI isn’t creating new security problems, it’s exposing existing ones at scale. GitGuardian saw 24M secrets leaked on public GitHub last year (+25%), and private repos are far more likely to contain secrets because people get careless when they feel safe. AI also enables more non-developers to ship apps without security training and generates oversized PRs that can’t be realistically reviewed, increasing leak risk. Attackers increasingly don’t “hack”, they use leaked credentials to log in and blend in like normal users, making traditional incident response less effective.