The Ollama dashboard assessment was built entirely from pre-computed
statistical scores/reasons/counts — a deliberately tested compaction step
stripped all raw message content before it reached the model, presumably to
keep the prompt small for a local, GPU-constrained instance. That raw
content (sample_events/timeline, already truncated to short messages)
already flows through the pipeline; it was just filtered out at the last
step. Now the top-scoring field deviations and incidents each carry a
small, bounded number of real message excerpts, and the prompt explicitly
asks the model to flag suspicious content (injection attempts, unusual
command lines) that pure statistical scoring can't see — purely a
qualitative addition to the narrative note, never feeding back into scoring
or suppression.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Completes the last deferred item from ROADMAP §0: entities recognized as
the same real-world concept across enabled streams (e.g. srcip in both a
firewall and an IIS stream) now share baseline knowledge of entity
presence/age and known relationship-target values, instead of each stream
learning it cold. Implemented additively (synthetic shared_entity:<group>
namespace in the existing baseline.py tables, no schema migration), scoped
to entity presence + relationship known-values only — per-stream
distribution baselines stay untouched. Also fixes a latent alias-map
collision bug found while building this (ALIAS_CANONICAL_BY_FIELD let
"context" silently steal "hostname"/"dns_query" from their own canonical
groups).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Feedback rules now support value/field/entity/stream scope, require
expiry for broad suppressions, and can be revoked individually.
Adds an active-rules panel, detector precision metrics, and diagnostics.
