DeepSigma turns AI decisions into auditable, recoverable infrastructure. It seals each decision episode end-to-end (inputs, actions, outcomes), detects drift, enforces governance gates, and drives patch loops with measurable confidence. Built in Python with MCP, LangChain, and OpenTelemetry.
- capability exposure inferred + 35
- recent drift inferred + 12
- abandonment inferred + 10
inferred
The A–E grade is our heuristic synthesis — a "review this" prompt, not a verdict. Each factor is tagged by what backs it: attested (a verifiable record), reported (a third party's claim), or inferred (our own heuristic, e.g. permissions). See methodology.
graded 5m ago · see ecosystem CVEs →
- D · 65 → C · 57
- D · 60 → D · 65
- C · 52 → D · 60
- A · 0 → C · 52
No known CVEs for this server.
No tool-safety findings — heuristic detectors run on the compute-risk cadence; a finding appears when a tool trips a rule.
- recent drift +12 capability drift →
- abandonment +10 abandonment hub →
[](https://mcpobservatory.com/servers/github:8ryanWh1t3/DeepSigma/security) Heuristic, inferred signals — false positives (legitimately powerful tools, forks, language ports) are expected. Treat each as "review this", not a verdict. See the ecosystem-wide picture on the security hub, or the fleet security of 8ryanWh1t3.