For AppSec teams
Several scanners report issues in the same project.
Bring code, dependencies, secrets and infrastructure together, then examine reachability and available evidence.
You can justify a priority and hand a qualified finding to the developer.
A 30-minute demo on a project close to yours.
From noise to decision
Four screens, from the priorities board to the dependency remediation plan.
Demo screenshots. Results illustrate this project, not every project.
Tamis interface · scanned WebGoat project
Under the hood
In Tamis
Tamis analyzes data flows with a graph engine and Opengrep.
Your direct and transitive dependencies are enriched with EPSS, KEV and VEX to inform triage.
The scan also covers Terraform, Kubernetes and Dockerfile.
It looks for secrets in code and Git history.
AI provides an initial assessment, locally by default.
Using a cloud model is optional and only happens when confidence or evidence is lacking.
Every suggestion remains a proposal until you decide.
You receive an explained fix, checked by a fresh scan on a copy.
For supported cases, Tamis rescans the fix before offering you a draft pull request.
Network exposure, business criticality and the data handled can help refine priorities.
Tamis uses the available context for the findings it applies to.
You can distinguish detected, reachable and confirmed findings.
When a trace is available, inspect the files and lines it connects.
For each confirmation, you see its origin: manual entry, runtime observation or DAST.
When rules share a source but reach different sinks, their findings are grouped into a compound vulnerability.
You see that grouping mapped to MITRE ATT&CK techniques.
On the same evidence
Good to know Coverage depends on your languages, enabled scanners and deployment mode.
AI features remain optional and run locally by default.
On the blog
The mechanics explained simply, then in depth. All articles
