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Privacy hardening, remote-peer IP masking and review fixes

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- Add privacy projection (core/privacy.py), private report storage, bounded
  process capture; omit identities, free text and unapproved paths
- Mask remote peer IPs: only loopback/host/wildcard stay numeric, others are
  reduced to remote_scope private/public (schema + scoring updated)
- Map ss TCP states to canonical names; ASCII-only numeric fields; fix
  "Microsoft Visual C++" product matching (Python and PowerShell)
- Merge 7fed3b9: -Diagnostic switch, serialization exit code 3, PS7 module
  path, UI error detail; keep hardened task/certificate collectors
- Add audit docs, privacy docs and regression tests

Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
This commit is contained in:
Not Dave
2026-11-04 18:50:24 -08:00
parent 7fed3b9ab9
commit 610a3bb447
51 changed files with 2719 additions and 604 deletions
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8
docs/SCORING.md
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@@ -4,7 +4,7 @@
The previous engine added points for every running service, installed role, listener, connection and administrative share. A routine Windows snapshot could accumulate thousands of raw points and saturate at Critical. Model v2 removes that inventory-counting behavior. Collection schema remains v1; assessment metadata is versioned separately as `model_version: 2.0`.
Re-analyze a previous JSON report through **Analyze → Saved inventory file**. Its raw inventory and collection time are retained; the old assessment is ignored and a v2 assessment is calculated with the selected policy. No new Windows access is needed to re-score existing evidence. Imported data remain explicitly unverified. Old HTML scores do not change; generate a new report. Scores from v1 and v2 are not directly comparable.
Re-analyze a previous JSON report through **Analyze → Saved inventory file**. Its inventory is minimized under the privacy policy and its collection time retained; the old assessment is ignored and a v2 assessment is calculated with the selected policy. No new Windows access is needed to re-score existing evidence. Imported data remain explicitly unverified. Old HTML scores do not change; generate a new report. Scores from v1 and v2 are not directly comparable.
## Evidence classifications
@@ -58,8 +58,4 @@ Historical monitoring over a representative business cycle, infrequent workloads
## Reporting and validation
HTML leads with actionable workload groups, scoring rationale, confidence and missing checks. Routine inventory is summarized in zero-point context groups rather than repeated as medium-risk dependencies. A content-addressed `.inventory-<hash>.json` sidecar preserves every collected inventory row; its SHA-256 and link are in the report. Keep it with the HTML. JSON assessment export also retains full inventory, model/policy and detailed observations. Existing different sidecars are never overwritten.
Regression fixtures cover a clean Windows Server, an inactive former game server, active SQL, active IIS, a domain controller, business file-share use and incomplete collection. An additional regression reconstructs the reported 1,565-point pattern (86 services, 19 connections, 14 features, 23 listeners, three admin shares and two failed checks): v2 gives zero workload risk and reduced confidence, while retaining all observations. This is a synthetic reproduction of the supplied counts, not a replay of an unavailable original inventory file.
These tests do not certify business dependency discovery. Re-score the real test server's saved JSON, review results with its owner, and validate any new SMB activity fields on Windows. Native PowerShell tests remain skipped in the Linux development environment.
HTML leads with actionable workload groups, scoring rationale, confidence and missing checks. Routine inventory is summarized in zero-point context groups rather than repeated as medium-risk dependencies. HTML no longer creates raw inventory sidecars. JSON contains only privacy-minimized evidence, model/policy and observations recomputed from that evidence. This changes the earlier raw-preservation behavior deliberately. Existing raw artifacts are not deleted or scrubbed; handle them under organizational retention/privacy policy. See [privacy hardening](PRIVACY_HARDENING.md).