Implemented cleanup/triage direction. ui, and baselinbe days

README.md

@@ -244,12 +244,18 @@ Tune these in the dashboard or in `state/fgai-config.json`:
 
 ```json
 {
+  "baseline_training_days": 7,
   "baseline_retention_days": 14,
   "baseline_value_retention_days": 7,
   "baseline_max_values_per_field": 2000
 }
 ```
 
+`baseline_training_days` is the minimum baseline age before profile deviations
+are promoted into live triage. For production data, set this to the amount of
+history you trust, commonly `7` to `14` days. The dashboard shows fields as
+`learning` until both bucket count and baseline age are sufficient.
+
 If an existing baseline database has already grown large, stop the monitor and
 run a manual prune plus SQLite compaction:
 
@@ -302,7 +308,12 @@ export VIRUSTOTAL_API_KEY='...'
 fgai recommend --logs logs/fg_syslog.jsonl --min-score 35 --threat-intel
 ```
 
-Threat intelligence responses are cached locally in `state/threat-intel-cache.json`. Successful results are reused for seven days by default, failures for one hour, and SignalScope permits at most 100 new provider lookups per UTC day. Cached responses are returned even after that budget is reached. Tune these safeguards with `FGAI_THREAT_INTEL_TTL_SECONDS`, `FGAI_THREAT_INTEL_ERROR_TTL_SECONDS`, and `FGAI_THREAT_INTEL_DAILY_LIMIT`.
+Threat intelligence can also be configured in the dashboard settings. Choose
+`auto`, `abuseipdb`, or `virustotal`, paste the provider API key, and set the
+daily lookup budget and cache TTLs. API keys are stored only in the local config
+file and are not returned back to the browser after saving.
+
+Threat intelligence responses are cached locally in `state/threat-intel-cache.json`. Successful results are reused for seven days by default, failures for one hour, and SignalScope permits at most 100 new provider lookups per UTC day. Cached responses are returned even after that budget is reached. Tune these safeguards in the dashboard or with `FGAI_THREAT_INTEL_TTL_SECONDS`, `FGAI_THREAT_INTEL_ERROR_TTL_SECONDS`, and `FGAI_THREAT_INTEL_DAILY_LIMIT`.
 
 Listen for FortiGate syslog locally:
 

src/fgai/baseline.py

@@ -18,6 +18,7 @@ from .query_details import event_query_details
 DEFAULT_RETENTION_DAYS = 14
 DEFAULT_VALUE_RETENTION_DAYS = 7
 DEFAULT_MAX_VALUES_PER_FIELD = 2000
+DEFAULT_TRAINING_DAYS = 7
 MIN_REPORTED_DEVIATION_SCORE = 15
 MAX_RARE_VALUES_PER_ENTITY = 5
 
@@ -50,6 +51,12 @@ def _baseline_confidence(samples: int, *, temporal: bool) -> str:
     return "low"
 
 
+def _age_days(oldest_bucket: int | None, newest_timestamp: int) -> float:
+    if not oldest_bucket:
+        return 0.0
+    return max(0.0, (newest_timestamp - int(oldest_bucket)) / 86400)
+
+
 def _weighted_score(base: int, profile: object | None, field: str, detector: str) -> tuple[int, float]:
     weights = getattr(profile, "field_weights", {}) if profile else {}
     if not isinstance(weights, dict):
@@ -254,7 +261,7 @@ class BaselineStore:
                   on conflict(stream_id, entity, detector, weekday, hour, bucket_start) do update set events=events+excluded.events""", (stream_id, entity, detector, weekday, hour, bucket, count))
         return len(pending)
 
-    def profile_deviations(self, events: list[LogEvent], profiles: dict[str, object]) -> dict[str, list[dict[str, object]]]:
+    def profile_deviations(self, events: list[LogEvent], profiles: dict[str, object], *, min_training_days: int = 0) -> dict[str, list[dict[str, object]]]:
         current: dict[tuple[str, str, str], list[float]] = defaultdict(lambda: [0, 0.0])
         entity_events: dict[tuple[str, str], list[LogEvent]] = defaultdict(list)
         detector_current: Counter[tuple[str, str, str]] = Counter()
@@ -291,6 +298,10 @@ class BaselineStore:
                     baseline_scope = "all observed periods"
                 if len(rows) < 12:
                     continue
+                oldest = connection.execute("select min(bucket_start) from profile_buckets where stream_id=? and entity=? and field=?", (stream, entity, field)).fetchone()[0]
+                baseline_age_days = _age_days(oldest, current_timestamp)
+                if baseline_age_days < min_training_days:
+                    continue
                 if values[1] == 0:
                     continue
                 history = [row[1] / row[0] if row[0] else 0 for row in rows]
@@ -304,7 +315,7 @@ class BaselineStore:
                     base_score = 18 if confidence == "high" else 15 if confidence == "medium" else 10
                     score, weight = _weighted_score(base_score, profile, field, "numeric_baseline")
                     if score >= MIN_REPORTED_DEVIATION_SCORE:
-                        output[entity].append({"detector": "numeric_baseline", "field": field, "stream_id": stream, "score": score, "base_score": base_score, "weight": weight, "confidence": confidence, "baseline_samples": len(rows), "baseline_scope": baseline_scope, "reason": reason, "current": round(current_value, 2), "baseline": round(mean(history), 2), "sample_values": sample_values, "sample_events": evidence_events})
+                        output[entity].append({"detector": "numeric_baseline", "field": field, "stream_id": stream, "score": score, "base_score": base_score, "weight": weight, "confidence": confidence, "baseline_samples": len(rows), "baseline_age_days": round(baseline_age_days, 2), "baseline_scope": baseline_scope, "reason": reason, "current": round(current_value, 2), "baseline": round(mean(history), 2), "sample_values": sample_values, "sample_events": evidence_events})
 
             # Event-rate burst is calculated once per stream/entity, rather than once per selected field.
             for (stream, entity), matching in entity_events.items():
@@ -324,6 +335,10 @@ class BaselineStore:
                     baseline_scope = "all observed periods"
                 if len(rows) < 12:
                     continue
+                oldest = connection.execute("select min(bucket_start) from profile_buckets where stream_id=? and entity=? and field=?", (stream, entity, reference_field)).fetchone()[0]
+                baseline_age_days = _age_days(oldest, current_timestamp)
+                if baseline_age_days < min_training_days:
+                    continue
                 history = [row[0] for row in rows]
                 current_value = len(matching)
                 z_score = (current_value - mean(history)) / (pstdev(history) or 1.0)
@@ -334,7 +349,7 @@ class BaselineStore:
                 score, weight = _weighted_score(base_score, profile, "event_rate", "event_rate_burst")
                 samples = [_sample_event(event) for event in matching[:5]]
                 if score >= MIN_REPORTED_DEVIATION_SCORE:
-                    output[entity].append({"detector": "event_rate_burst", "field": "event_rate", "stream_id": stream, "score": score, "base_score": base_score, "weight": weight, "confidence": confidence, "baseline_samples": len(rows), "baseline_scope": baseline_scope, "reason": f"event rate burst above its {baseline_scope} baseline (z={z_score:.1f})", "current": current_value, "baseline": round(mean(history), 2), "sample_values": [], "sample_events": samples})
+                    output[entity].append({"detector": "event_rate_burst", "field": "event_rate", "stream_id": stream, "score": score, "base_score": base_score, "weight": weight, "confidence": confidence, "baseline_samples": len(rows), "baseline_age_days": round(baseline_age_days, 2), "baseline_scope": baseline_scope, "reason": f"event rate burst above its {baseline_scope} baseline (z={z_score:.1f})", "current": current_value, "baseline": round(mean(history), 2), "sample_values": [], "sample_events": samples})
 
             for (stream, entity, detector), current_value in detector_current.items():
                 profile = profiles.get(stream)
@@ -357,6 +372,10 @@ class BaselineStore:
                     baseline_scope = "all observed periods"
                 if len(rows) < 12:
                     continue
+                oldest = connection.execute("select min(bucket_start) from profile_detector_buckets where stream_id=? and entity=? and detector=?", (stream, entity, detector)).fetchone()[0]
+                baseline_age_days = _age_days(oldest, current_timestamp)
+                if baseline_age_days < min_training_days:
+                    continue
                 history = [row[0] for row in rows]
                 z_score = (current_value - mean(history)) / (pstdev(history) or 1.0)
                 if z_score < z_threshold:
@@ -366,7 +385,7 @@ class BaselineStore:
                 score, weight = _weighted_score(base_score, profile, detector, f"{detector}_burst")
                 samples = [_sample_event(event, detector) for event in matching[:5]]
                 if score >= MIN_REPORTED_DEVIATION_SCORE:
-                    output[entity].append({"detector": f"{detector}_burst", "field": detector, "stream_id": stream, "score": score, "base_score": base_score, "weight": weight, "confidence": confidence, "baseline_samples": len(rows), "baseline_scope": baseline_scope, "reason": f"{detector.replace('_', ' ')} burst above its {baseline_scope} baseline (z={z_score:.1f}, minimum={minimum})", "current": current_value, "baseline": round(mean(history), 2), "sample_values": [detector], "sample_events": samples})
+                    output[entity].append({"detector": f"{detector}_burst", "field": detector, "stream_id": stream, "score": score, "base_score": base_score, "weight": weight, "confidence": confidence, "baseline_samples": len(rows), "baseline_age_days": round(baseline_age_days, 2), "baseline_scope": baseline_scope, "reason": f"{detector.replace('_', ' ')} burst above its {baseline_scope} baseline (z={z_score:.1f}, minimum={minimum})", "current": current_value, "baseline": round(mean(history), 2), "sample_values": [detector], "sample_events": samples})
             # Detect selected categorical values that have not appeared for this entity in prior data.
             rare_counts: Counter[tuple[str, str]] = Counter()
             for event in events:
@@ -386,6 +405,10 @@ class BaselineStore:
                         known = connection.execute("select seen_count from profile_values where stream_id=? and entity=? and field=? and value=?", (stream, entity, field, value)).fetchone()
                         known_total = connection.execute("select coalesce(sum(seen_count), 0) from profile_values where stream_id=? and entity=? and field=?", (stream, entity, field)).fetchone()[0]
                         if known is None and known_total >= 30:
+                            oldest = connection.execute("select min(bucket_start) from profile_buckets where stream_id=? and entity=? and field=?", (stream, entity, field)).fetchone()[0]
+                            baseline_age_days = _age_days(oldest, _event_epoch(event, int(time.time())))
+                            if baseline_age_days < min_training_days:
+                                continue
                             if rare_counts[(stream, entity)] >= MAX_RARE_VALUES_PER_ENTITY:
                                 continue
                             samples = [item for item in events if item.fields.get("fgai_stream_id") == stream and entity in profile_entities(item, profile) and item.fields.get(field) == value]
@@ -393,7 +416,7 @@ class BaselineStore:
                             score, weight = _weighted_score(base_score, profile, field, "rare_value")
                             if score < MIN_REPORTED_DEVIATION_SCORE:
                                 continue
-                            evidence = {"detector": "rare_value", "field": field, "stream_id": stream, "score": score, "base_score": base_score, "weight": weight, "confidence": "medium", "baseline_samples": int(known_total), "baseline_scope": "known field values", "reason": f"new {field} value for this entity", "value": value, "sample_values": [value], "sample_events": [_sample_event(item, value) for item in samples[:5]]}
+                            evidence = {"detector": "rare_value", "field": field, "stream_id": stream, "score": score, "base_score": base_score, "weight": weight, "confidence": "medium", "baseline_samples": int(known_total), "baseline_age_days": round(baseline_age_days, 2), "baseline_scope": "known field values", "reason": f"new {field} value for this entity", "value": value, "sample_values": [value], "sample_events": [_sample_event(item, value) for item in samples[:5]]}
                             if evidence not in output[entity]:
                                 output[entity].append(evidence)
                                 rare_counts[(stream, entity)] += 1
@@ -449,14 +472,16 @@ class BaselineStore:
                 connection.execute("vacuum")
         return {"retention_days": retention_days, "value_retention_days": value_retention_days, "max_values_per_field": max_values_per_field, "vacuum": vacuum, "deleted": deleted, "size_bytes": self.path.stat().st_size if self.path.exists() else 0}
 
-    def profile_readiness(self, profiles: dict[str, object]) -> list[dict[str, object]]:
+    def profile_readiness(self, profiles: dict[str, object], *, min_training_days: int = 0) -> list[dict[str, object]]:
         rows: list[dict[str, object]] = []
         with self._connect() as connection:
             for stream_id, profile in profiles.items():
                 fields = [*getattr(profile, "categorical_fields", ()), *getattr(profile, "numeric_fields", ())]
                 for field in fields:
                     count = connection.execute("select count(distinct bucket_start) from profile_buckets where stream_id=? and field=?", (stream_id, str(field).lower())).fetchone()[0]
-                    rows.append({"stream_id": stream_id, "field": str(field), "buckets": count, "ready": count >= 12})
+                    oldest = connection.execute("select min(bucket_start) from profile_buckets where stream_id=? and field=?", (stream_id, str(field).lower())).fetchone()[0]
+                    age_days = _age_days(oldest, int(time.time()))
+                    rows.append({"stream_id": stream_id, "field": str(field), "buckets": count, "age_days": round(age_days, 2), "training_days": min_training_days, "ready": count >= 12 and age_days >= min_training_days})
         return rows
 
     def profiles(self, source_ips: set[str]) -> dict[str, dict[str, object]]:

src/fgai/config.py

@@ -16,10 +16,18 @@ DEFAULT_CONFIG: dict[str, object] = {
     "baseline_retention_days": 14,
     "baseline_value_retention_days": 7,
     "baseline_max_values_per_field": 2000,
+    "baseline_training_days": 7,
     "graylog_field_mapping": "",
     "llm_enabled": False,
     "llm_model": "",
     "threat_intel_enabled": False,
+    "threat_intel_provider": "auto",
+    "abuseipdb_api_key": "",
+    "virustotal_api_key": "",
+    "threat_intel_daily_limit": 100,
+    "threat_intel_ttl_seconds": 604800,
+    "threat_intel_error_ttl_seconds": 3600,
+    "abuseipdb_max_age_days": 90,
 }
 
 EDITABLE_FIELDS = set(DEFAULT_CONFIG) | {"graylog_mcp_token"}
@@ -39,6 +47,8 @@ class ConfigStore:
     def public(self) -> dict[str, object]:
         config = self.read()
         config["graylog_mcp_token_configured"] = bool(config.pop("graylog_mcp_token", ""))
+        config["abuseipdb_api_key_configured"] = bool(config.pop("abuseipdb_api_key", ""))
+        config["virustotal_api_key_configured"] = bool(config.pop("virustotal_api_key", ""))
         return config
 
     def update(self, values: dict[str, object]) -> dict[str, object]:
@@ -46,18 +56,20 @@ class ConfigStore:
         for key, value in values.items():
             if key not in EDITABLE_FIELDS:
                 continue
-            if key == "graylog_mcp_token" and value == "":
+            if key in {"graylog_mcp_token", "abuseipdb_api_key", "virustotal_api_key"} and value == "":
                 continue
             if key in {"llm_enabled", "threat_intel_enabled"}:
                 current[key] = bool(value)
             elif key == "log_source" and value in {"local_syslog", "graylog_mcp"}:
                 current[key] = value
-            elif key in {"graylog_range_seconds", "baseline_retention_days", "baseline_value_retention_days", "baseline_max_values_per_field"}:
+            elif key in {"graylog_range_seconds", "baseline_retention_days", "baseline_value_retention_days", "baseline_max_values_per_field", "baseline_training_days", "threat_intel_daily_limit", "threat_intel_ttl_seconds", "threat_intel_error_ttl_seconds", "abuseipdb_max_age_days"}:
                 try:
                     minimum = 60 if key == "graylog_range_seconds" else 1
                     current[key] = max(minimum, int(value))
                 except (TypeError, ValueError):
                     continue
+            elif key == "threat_intel_provider" and value in {"auto", "abuseipdb", "virustotal"}:
+                current[key] = value
             elif key == "graylog_streams" and isinstance(value, list):
                 current[key] = [
                     {"id": str(item.get("id", "")), "title": str(item.get("title", "")), "enabled": bool(item.get("enabled"))}

src/fgai/dashboard.py

@@ -60,6 +60,10 @@ HTML = """<!doctype html>
     .field-controls label { white-space: nowrap; }
     .stream-row { display: flex; align-items: center; justify-content: space-between; gap: 10px; padding: 4px 0; }
     .stream-row button { border: 1px solid #39709a; background: #0b2944; color: #d9e8f7; padding: 4px 8px; cursor: pointer; }
+    .toolbar { display: flex; flex-wrap: wrap; gap: 10px; align-items: center; margin: 8px 0 12px; color: #91abc4; }
+    .toolbar label { display: inline-flex; align-items: center; gap: 6px; }
+    .summary-card { border: 1px solid #163b59; background: #061a2e; padding: 10px; margin-bottom: 8px; border-radius: 6px; }
+    .summary-card h3 { margin: 0 0 6px; font-size: 16px; }
     .review-actions { display: flex; flex-wrap: wrap; gap: 6px; min-width: 250px; }
     .review-actions button { border: 1px solid #39709a; background: #0b2944; color: #d9e8f7; padding: 6px 8px; cursor: pointer; }
     .review-actions button[data-status="false_positive"] { border-color: #b7823a; color: #ffd36e; }
@@ -83,9 +87,9 @@ HTML = """<!doctype html>
     </section>
     <nav class="tabs" aria-label="Dashboard views"><button class="tab active" data-tab="overview">Overview</button><button class="tab" data-tab="findings">Findings</button><button class="tab" data-tab="diagnostics">Diagnostics</button><button class="tab" data-tab="settings">Settings</button></nav>
     <div data-view="overview" class="active"><section class="split"><div class="panel"><h2>Events and Anomalies</h2><canvas id="trendChart" class="chart"></canvas></div><div class="panel"><h2>Baseline and Stream Health</h2><div id="health"></div></div></section><section class="split"><div class="panel"><h2>Correlation Map</h2><canvas id="correlationGraph" class="graph"></canvas><div id="correlationGraphInfo" class="muted"></div></div><div class="panel"><h2>AI Assessment</h2><div id="llmAssessment" class="muted">LLM assessment disabled.</div></div></section><section class="panel"><h2>Investigation Incidents</h2><div id="incidents"></div></section><section class="split"><div class="panel"><h2>Anomalies</h2><div id="anomalies"></div></div><div class="panel"><h2>Recommendations</h2><div id="recommendations"></div></div></section></div>
-    <div data-view="findings"><section class="panel"><h2>Field Baseline Deviations</h2><div id="feedbackNotice" class="muted" role="status"></div><div id="fieldDeviations"></div></section><section class="panel"><h2>Related Activity Across Sources</h2><div id="relatedActivity"></div></section><section class="split"><div class="panel"><h2>Block Candidates</h2><div id="blocks"></div></div><div class="panel"><h2>Threat Intelligence</h2><div id="reputation"></div></div></section><section class="panel"><h2>Policy Findings</h2><div id="policies"></div></section></div>
+    <div data-view="findings"><section class="panel"><h2>Triage Queue</h2><div id="triageQueue"></div></section><section class="panel"><h2>Field Baseline Deviations</h2><div class="toolbar"><label><input id="showReviewedFindings" type="checkbox"> show reviewed</label><label><input id="showLowFindings" type="checkbox"> show low score</label><span id="findingSummary"></span></div><div id="feedbackNotice" class="muted" role="status"></div><div id="fieldDeviations"></div></section><section class="panel"><h2>Related Activity Across Sources</h2><div id="relatedActivity"></div></section><section class="split"><div class="panel"><h2>Block Candidates</h2><div id="blocks"></div></div><div class="panel"><h2>Threat Intelligence</h2><div id="reputation"></div></div></section><section class="panel"><h2>Policy Findings</h2><div id="policies"></div></section></div>
     <div data-view="diagnostics"><section class="panel"><h2>Diagnostics</h2><div id="diagnostics"></div></section></div>
-    <div data-view="settings"><section class="panel"><h2>Runtime Configuration</h2><form id="settingsForm"><div class="grid"><label>Log source<br><select name="log_source"><option value="local_syslog">Local syslog</option><option value="graylog_mcp">Graylog MCP</option></select></label><label>Graylog MCP URL<br><input name="graylog_mcp_url" type="url" placeholder="https://graylog.example/api/mcp"></label><label>Enabled streams<br><button type="button" id="loadStreams">Load streams</button><div id="streamPicker" class="muted">Load streams after URL and token are saved.</div></label><label>Profile editor<br><button type="button" id="loadFields">Edit first checked stream profile</button><div id="profileEditorStatus" class="muted">No profile selected for editing.</div><div id="fieldPicker" class="muted">Click Edit profile on one stream.</div></label><label>Profile name<br><input name="profile_name" placeholder="Example: Windows login behavior"></label><label>Detector thresholds (JSON)<br><textarea name="profile_detectors" placeholder='{"auth_failure":{"enabled":true,"minimum":5,"z_threshold":3}}'></textarea></label><label>Field weights (JSON)<br><textarea name="profile_field_weights" placeholder='{"url":1.5,"auth_failure_burst":2,"query_domain":{"rare_value":1.8}}'></textarea></label><label>Graylog query<br><input name="graylog_query" placeholder="*"></label><label>Graylog analysis window seconds<br><input name="graylog_range_seconds" type="number" min="60" step="60" placeholder="3600"></label><label>Baseline bucket retention days<br><input name="baseline_retention_days" type="number" min="1" step="1" placeholder="14"></label><label>Baseline value retention days<br><input name="baseline_value_retention_days" type="number" min="1" step="1" placeholder="7"></label><label>Max values per entity field<br><input name="baseline_max_values_per_field" type="number" min="1" step="100" placeholder="2000"></label><label>Graylog field mapping (JSON)<br><textarea name="graylog_field_mapping" placeholder='{"srcip":"client_ip","dstip":"server_ip","action":"event_action"}'></textarea></label><label>Graylog MCP token<br><input name="graylog_mcp_token" type="password" placeholder="Leave blank to keep current token"></label><label>Ollama model<br><input name="llm_model" placeholder="llama3.1"></label><label><input name="llm_enabled" type="checkbox"> Enable Ollama analysis</label><label><input name="threat_intel_enabled" type="checkbox"> Enable threat intelligence</label></div><p><button type="submit">Save configuration</button> <span id="settingsResult" class="muted"></span></p></form></section></div>
+    <div data-view="settings"><section class="panel"><h2>Runtime Configuration</h2><form id="settingsForm"><div class="grid"><label>Log source<br><select name="log_source"><option value="local_syslog">Local syslog</option><option value="graylog_mcp">Graylog MCP</option></select></label><label>Graylog MCP URL<br><input name="graylog_mcp_url" type="url" placeholder="https://graylog.example/api/mcp"></label><label>Enabled streams<br><button type="button" id="loadStreams">Load streams</button><div id="streamPicker" class="muted">Load streams after URL and token are saved.</div></label><label>Profile editor<br><button type="button" id="loadFields">Edit first checked stream profile</button><div id="profileEditorStatus" class="muted">No profile selected for editing.</div><div id="fieldPicker" class="muted">Click Edit profile on one stream.</div></label><label>Profile name<br><input name="profile_name" placeholder="Example: Windows login behavior"></label><label>Detector thresholds (JSON)<br><textarea name="profile_detectors" placeholder='{"auth_failure":{"enabled":true,"minimum":5,"z_threshold":3}}'></textarea></label><label>Field weights (JSON)<br><textarea name="profile_field_weights" placeholder='{"url":1.5,"auth_failure_burst":2,"query_domain":{"rare_value":1.8}}'></textarea></label><label>Graylog query<br><input name="graylog_query" placeholder="*"></label><label>Graylog analysis window seconds<br><input name="graylog_range_seconds" type="number" min="60" step="60" placeholder="3600"></label><label>Baseline training days<br><input name="baseline_training_days" type="number" min="1" step="1" placeholder="7"></label><label>Baseline bucket retention days<br><input name="baseline_retention_days" type="number" min="1" step="1" placeholder="14"></label><label>Baseline value retention days<br><input name="baseline_value_retention_days" type="number" min="1" step="1" placeholder="7"></label><label>Max values per entity field<br><input name="baseline_max_values_per_field" type="number" min="1" step="100" placeholder="2000"></label><label>Threat intel provider<br><select name="threat_intel_provider"><option value="auto">Auto</option><option value="abuseipdb">AbuseIPDB</option><option value="virustotal">VirusTotal</option></select></label><label>AbuseIPDB API key<br><input name="abuseipdb_api_key" type="password" placeholder="Leave blank to keep current key"></label><label>VirusTotal API key<br><input name="virustotal_api_key" type="password" placeholder="Leave blank to keep current key"></label><label>Threat intel daily limit<br><input name="threat_intel_daily_limit" type="number" min="1" step="1" placeholder="100"></label><label>Threat intel cache TTL seconds<br><input name="threat_intel_ttl_seconds" type="number" min="60" step="60" placeholder="604800"></label><label>Threat intel error TTL seconds<br><input name="threat_intel_error_ttl_seconds" type="number" min="60" step="60" placeholder="3600"></label><label>AbuseIPDB max age days<br><input name="abuseipdb_max_age_days" type="number" min="1" step="1" placeholder="90"></label><label>Graylog field mapping (JSON)<br><textarea name="graylog_field_mapping" placeholder='{"srcip":"client_ip","dstip":"server_ip","action":"event_action"}'></textarea></label><label>Graylog MCP token<br><input name="graylog_mcp_token" type="password" placeholder="Leave blank to keep current token"></label><label>Ollama model<br><input name="llm_model" placeholder="llama3.1"></label><label><input name="llm_enabled" type="checkbox"> Enable Ollama analysis</label><label><input name="threat_intel_enabled" type="checkbox"> Enable threat intelligence</label></div><p><button type="submit">Save configuration</button> <span id="settingsResult" class="muted"></span></p></form></section></div>
   </main>
 <script>
 function esc(value) {
@@ -148,6 +152,7 @@ async function refresh() {
     `Critical anomalies: ${esc((a.critical || 0))}`,
     `High anomalies: ${esc((a.high || 0))}`,
     `Baseline sources ready: ${esc((data.baseline || {}).sources_ready || 0)}`,
+    `Baseline training days: ${esc((data.baseline || {}).training_days || 0)}`,
     `Baseline DB size: ${esc(bytes((data.baseline || {}).size_bytes || 0))}`
   ].join('<br>');
   document.getElementById('health').innerHTML = [
@@ -200,12 +205,31 @@ async function refresh() {
   const reputationRows = Object.entries(data.reputation || {}).map(([ip, intel]) => ({ip, ...intel}));
   const relatedRows = correlations.flatMap(correlation => (correlation.samples || []).map(sample => ({entity: correlation.entity || correlation.source_ip, source_ip: correlation.source_ip, ...sample})));
   const streamTitles = Object.fromEntries((configuration.graylog_streams || []).map(item => [item.id, item.title || item.id]));
+  document.getElementById('triageQueue').innerHTML = table(data.triage_queue || [], [
+    {label:'Entity', key:'entity'},
+    {label:'Score', key:'score'},
+    {label:'Severity', render:r => `<span class="sev-${esc(r.severity)}">${esc(r.severity)}</span>`},
+    {label:'State', key:'state'},
+    {label:'Streams', render:r => esc((r.streams || []).join(', ') || '-')},
+    {label:'Detectors', render:r => esc((r.detectors || []).join(', ') || '-')},
+    {label:'Open signals', key:'active_deviation_count'},
+    {label:'Why', render:r => esc((r.evidence || []).join('; ') || '-')},
+    {label:'Next', key:'next_action'}
+  ], 'triage-queue');
   const rawFieldRows = Object.entries(data.field_deviations || {}).flatMap(([entity, deviations]) => (deviations || []).map(item => ({entity, ...item, stream_title: item.stream_name || item.stream_title || streamTitles[item.stream_id] || item.stream_id})));
   const fieldRowsCached = rawFieldRows.length === 0 && uiCache.fieldRows.length > 0;
-  const fieldRows = rawFieldRows.length ? rawFieldRows : uiCache.fieldRows;
+  const sourceFieldRows = rawFieldRows.length ? rawFieldRows : uiCache.fieldRows;
+  const showReviewed = document.getElementById('showReviewedFindings')?.checked;
+  const showLow = document.getElementById('showLowFindings')?.checked;
+  const fieldRows = sourceFieldRows
+    .filter(row => showReviewed || !['expected','false_positive'].includes(row.feedback || ''))
+    .filter(row => showLow || Number(row.score || 0) >= 35 || row.feedback === 'confirmed')
+    .sort((left, right) => Number(right.score || 0) - Number(left.score || 0))
+    .slice(0, 50);
+  document.getElementById('findingSummary').textContent = `${fieldRows.length} shown from ${sourceFieldRows.length} raw deviations. Reviewed and low-score findings are hidden by default.`;
   if (rawFieldRows.length) uiCache.fieldRows = rawFieldRows;
   document.getElementById('fieldDeviations').innerHTML = `${fieldRowsCached ? '<p class="muted">No current field deviations in this poll; showing cached findings from the last non-empty poll.</p>' : ''}` + table(fieldRows, [
-    {label:'Entity', key:'entity'}, {label:'Stream', key:'stream_title'}, {label:'Detector', key:'detector'}, {label:'Field', key:'field'}, {label:'Score', key:'score'}, {label:'Confidence', key:'confidence'}, {label:'Review', render:r => esc(r.feedback || 'unreviewed')}, {label:'Evidence', render:r => { const weighted = r.weight && r.weight !== 1 ? `; weighted ${r.base_score ?? r.score} x ${r.weight}` : ''; const summary=esc(`${r.reason}; current ${r.current ?? '-'} vs baseline ${r.baseline ?? '-'}; samples ${r.baseline_samples ?? '-'}; scope ${r.baseline_scope ?? '-'}${weighted}; values: ${(r.sample_values || []).join(', ') || '-'}`); const events=(r.sample_events || []).map(item => esc(`${item.timestamp} | ${item.source} -> ${item.destination} | ${item.action} ${item.service} | ${item.value} | ${item.message}`)).join('<br>'); const id=`deviation:${r.entity}:${r.stream_id}:${r.field}:${r.value || ''}`; return events ? `<details data-detail-id="${esc(id)}"><summary>${summary}</summary><p>${events}</p></details>` : summary; }}, {label:'Review action', render:r => `<div class="review-actions"><button class="feedback" data-status="expected" data-entity="${esc(r.entity)}" data-stream="${esc(r.stream_id)}" data-field="${esc(r.field)}" data-value="${esc(r.value || '')}">Mark expected</button><button class="feedback" data-status="false_positive" data-entity="${esc(r.entity)}" data-stream="${esc(r.stream_id)}" data-field="${esc(r.field)}" data-value="${esc(r.value || '')}">Mark false positive</button><button class="feedback" data-status="confirmed" data-entity="${esc(r.entity)}" data-stream="${esc(r.stream_id)}" data-field="${esc(r.field)}" data-value="${esc(r.value || '')}">Mark confirmed</button></div>`}
+    {label:'Entity', key:'entity'}, {label:'Stream', key:'stream_title'}, {label:'Detector', key:'detector'}, {label:'Field', key:'field'}, {label:'Score', key:'score'}, {label:'Baseline', render:r => esc(`${r.confidence || '-'}; ${r.baseline_age_days ?? 0}d; ${r.baseline_samples || 0} samples`)}, {label:'Review', render:r => esc(r.feedback || 'unreviewed')}, {label:'Evidence', render:r => { const weighted = r.weight && r.weight !== 1 ? `; weighted ${r.base_score ?? r.score} x ${r.weight}` : ''; const summary=esc(`${r.reason}; current ${r.current ?? '-'} vs baseline ${r.baseline ?? '-'}; scope ${r.baseline_scope ?? '-'}${weighted}; values: ${(r.sample_values || []).join(', ') || '-'}`); const events=(r.sample_events || []).map(item => esc(`${item.timestamp} | ${item.source} -> ${item.destination} | ${item.action} ${item.service} | ${item.value} | ${item.message}`)).join('<br>'); const id=`deviation:${r.entity}:${r.stream_id}:${r.field}:${r.value || ''}`; return events ? `<details data-detail-id="${esc(id)}"><summary>${summary}</summary><p>${events}</p></details>` : summary; }}, {label:'Review action', render:r => `<div class="review-actions"><button class="feedback" data-status="expected" data-entity="${esc(r.entity)}" data-stream="${esc(r.stream_id)}" data-field="${esc(r.field)}" data-value="${esc(r.value || '')}">Expected</button><button class="feedback" data-status="false_positive" data-entity="${esc(r.entity)}" data-stream="${esc(r.stream_id)}" data-field="${esc(r.field)}" data-value="${esc(r.value || '')}">False positive</button><button class="feedback" data-status="confirmed" data-entity="${esc(r.entity)}" data-stream="${esc(r.stream_id)}" data-field="${esc(r.field)}" data-value="${esc(r.value || '')}">Confirm</button></div>`}
   ], 'field-deviations');
   document.querySelectorAll('.feedback').forEach(button => button.addEventListener('click', async () => {
     const note = prompt('Review note (optional):') || '';
@@ -242,7 +266,7 @@ async function refresh() {
     '<h3>Stream Coverage</h3>' + table(streamCoverage, [{label:'Stream', key:'stream_name'}, {label:'Enabled', key:'enabled', render:r => r.enabled ? 'yes' : 'no'}, {label:'Profile', render:r => esc(r.profile || 'missing')}, {label:'Entity Field', key:'entity_field'}, {label:'Tracked Fields', key:'tracked_fields'}, {label:'Ready Fields', key:'readiness'}, {label:'Events', key:'events_fetched'}, {label:'Latest Event', key:'latest_event_time'}, {label:'Health', key:'health'}], 'stream-coverage') +
     '<h3>Cross-Source Correlations</h3>' + table(correlations, [{label:'Entity', key:'entity', render:r => esc(`${r.entity || r.source_ip} (${r.entity_type || 'ip'})`)}, {label:'Streams', render:r => esc((r.streams || []).join(', '))}, {label:'Events', key:'events'}, {label:'Security Events', key:'security_events'}], 'correlations') +
     '<h3>Entities</h3>' + table(context.source_profiles || [], [{label:'Entity', key:'entity'}, {label:'Events', key:'events'}, {label:'UTM', key:'utm_events'}, {label:'Deny', key:'deny_or_threat_actions'}, {label:'Destinations', key:'distinct_destinations'}, {label:'Actions', render:r => esc((r.top_actions || []).join(', '))}], 'entities') +
-    '<h3>Profile Baseline Readiness</h3>' + table(profileReadiness, [{label:'Profile', key:'profile_name'}, {label:'Stream', key:'stream_title'}, {label:'Field', key:'field'}, {label:'Buckets', key:'buckets'}, {label:'Ready', key:'ready', render:r => r.ready ? 'ready' : 'learning'}], 'profile-readiness') +
+    '<h3>Profile Baseline Readiness</h3>' + table(profileReadiness, [{label:'Profile', key:'profile_name'}, {label:'Stream', key:'stream_title'}, {label:'Field', key:'field'}, {label:'Buckets', key:'buckets'}, {label:'Age days', key:'age_days'}, {label:'Training days', key:'training_days'}, {label:'Ready', key:'ready', render:r => r.ready ? 'ready' : 'learning'}], 'profile-readiness') +
     '<h3>Data Quality</h3>' + table([quality], [{label:'Events', key:'events'}, {label:'Timestamp coverage', render:r => `${r.timestamp_coverage || 0}%`}, {label:'Source coverage', render:r => `${r.source_coverage || 0}%`}, {label:'Truncated streams', render:r => esc((r.truncated_streams || []).join(', ') || 'none')}]) +
     '<h3>Security Event Samples</h3>' + table(context.security_event_samples || [], [{label:'Entity', key:'entity'}, {label:'Type', key:'type'}, {label:'Action', key:'action'}, {label:'Severity', key:'severity'}, {label:'Destination', key:'dst'}, {label:'Service', key:'service'}]) +
     '<h3>Top Sources</h3>' + table(d.top_source_ips || [], [{label:'Value', key:'value'}, {label:'Count', key:'count'}]) +
@@ -265,6 +289,10 @@ async function loadSettings() {
   }
   const token = form.elements.namedItem('graylog_mcp_token');
   token.placeholder = config.graylog_mcp_token_configured ? 'Token configured; leave blank to keep it' : 'Paste a read-only token';
+  const abuseKey = form.elements.namedItem('abuseipdb_api_key');
+  abuseKey.placeholder = config.abuseipdb_api_key_configured ? 'Key configured; leave blank to keep it' : 'Paste AbuseIPDB API key';
+  const vtKey = form.elements.namedItem('virustotal_api_key');
+  vtKey.placeholder = config.virustotal_api_key_configured ? 'Key configured; leave blank to keep it' : 'Paste VirusTotal API key';
   window.streamProfiles = config.graylog_stream_profiles || [];
   if (config.graylog_mcp_token_configured && config.graylog_mcp_url) loadStreams();
 }
@@ -318,6 +346,7 @@ document.querySelectorAll('.tab').forEach(button => button.addEventListener('cli
   document.querySelectorAll('.tab').forEach(item => item.classList.toggle('active', item === button));
   document.querySelectorAll('[data-view]').forEach(view => view.classList.toggle('active', view.dataset.view === button.dataset.tab));
 }));
+['showReviewedFindings','showLowFindings'].forEach(id => document.getElementById(id)?.addEventListener('change', refresh));
 refresh();
 loadSettings();
 setInterval(refresh, 5000);

src/fgai/monitor.py

@@ -22,6 +22,7 @@ from .policies import audit_policies, read_policies
 from .recommendations import build_recommendations
 from .sequences import detect_sequences
 from .threat_intel import ThreatIntelClient, enrich_ips, is_public_ip
+from .triage import build_triage_queue
 from .stream_profiles import parse_profiles
 
 
@@ -147,7 +148,8 @@ def build_status(
                 events = []
     baseline = BaselineStore(baseline_path) if baseline_path else None
     profiles = baseline.profiles({event.src_ip for event in events if event.src_ip}) if baseline else {}
-    field_deviations = baseline.profile_deviations(events, stream_profiles) if baseline else {}
+    baseline_training_days = int(runtime_values.get("baseline_training_days", 7) or 7)
+    field_deviations = baseline.profile_deviations(events, stream_profiles, min_training_days=baseline_training_days) if baseline else {}
     sequence_findings = detect_sequences(events)
     for entity, findings in sequence_findings.items():
         field_deviations.setdefault(entity, []).extend(findings)
@@ -189,7 +191,7 @@ def build_status(
         if baseline
         else {}
     )
-    profile_readiness = baseline.profile_readiness(stream_profiles) if baseline else []
+    profile_readiness = baseline.profile_readiness(stream_profiles, min_training_days=baseline_training_days) if baseline else []
     profile_readiness = [
         {
             **item,
@@ -208,11 +210,21 @@ def build_status(
         }
     )
     threat_enabled = bool(runtime_values.get("threat_intel_enabled")) if runtime_values else None
-    reputation = enrich_ips(intel_ips, limit=25, enabled=threat_enabled)
+    reputation = enrich_ips(intel_ips, limit=25, enabled=threat_enabled, config=runtime_values)
-    threat_intel_status = ThreatIntelClient(enabled=threat_enabled).status()
+    threat_intel_status = ThreatIntelClient(
+        enabled=threat_enabled,
+        provider=str(runtime_values.get("threat_intel_provider", "auto")),
+        abuseipdb_key=str(runtime_values.get("abuseipdb_api_key", "") or "") or None,
+        virustotal_key=str(runtime_values.get("virustotal_api_key", "") or "") or None,
+        daily_limit=int(runtime_values.get("threat_intel_daily_limit", 100) or 100),
+        ttl_seconds=int(runtime_values.get("threat_intel_ttl_seconds", 604800) or 604800),
+        error_ttl_seconds=int(runtime_values.get("threat_intel_error_ttl_seconds", 3600) or 3600),
+        abuseipdb_max_age_days=int(runtime_values.get("abuseipdb_max_age_days", 90) or 90),
+    ).status()
     recommendations = build_recommendations(events, anomalies, reputation)
     correlations = correlate_source_ips(events)
     incidents = IncidentStore(incident_path or "state/signalscope-incidents.json").apply(build_incidents(anomalies, field_deviations, correlations))
+    triage_queue = build_triage_queue(incidents, field_deviations, correlations, recommendations)
     block_candidates = suggest_block_candidates(
         events,
         min_events=min_block_events,
@@ -234,7 +246,7 @@ def build_status(
         "policy_path": policy_path,
         "summary": summarize_events(events),
         "anomaly_summary": anomaly_summary(anomalies),
-        "baseline": {"enabled": bool(baseline), "sources_ready": len(profiles), "new_events_recorded": baseline_events, "profile_fields_recorded": profile_baseline_fields, "maintenance": baseline_maintenance, "size_bytes": baseline_maintenance.get("size_bytes", 0) if isinstance(baseline_maintenance, dict) else 0},
+        "baseline": {"enabled": bool(baseline), "sources_ready": len(profiles), "training_days": baseline_training_days, "new_events_recorded": baseline_events, "profile_fields_recorded": profile_baseline_fields, "maintenance": baseline_maintenance, "size_bytes": baseline_maintenance.get("size_bytes", 0) if isinstance(baseline_maintenance, dict) else 0},
         "capabilities": {"threat_intel": threat_intel_status, "graylog_mcp": mcp_status},
         "configuration": runtime_config,
         "stream_profiles": [{"stream_id": item.stream_id, "name": _profile_name(item.stream_id, stream_titles, item), "stream_name": _stream_name(item.stream_id, stream_titles, item), "entity_field": item.entity_field, "entity_fields": list(item.entity_fields), "timestamp_field": item.timestamp_field, "categorical_fields": list(item.categorical_fields), "numeric_fields": list(item.numeric_fields), "detectors": item.detectors, "field_weights": item.field_weights} for item in stream_profiles.values()],
@@ -253,6 +265,7 @@ def build_status(
         },
         "event_context": build_event_context(events),
         "field_deviations": field_deviations,
+        "triage_queue": triage_queue,
         "sequence_findings": sequence_findings,
         "feedback": feedback,
         "cross_source_correlations": correlations,

src/fgai/threat_intel.py

@@ -19,16 +19,28 @@ def is_public_ip(value: str | None) -> bool:
 
 
 class ThreatIntelClient:
-    def __init__(self, *, cache_file: str = "state/threat-intel-cache.json", ttl_seconds: int | None = None, enabled: bool | None = None) -> None:
+    def __init__(
+        self,
+        *,
+        cache_file: str = "state/threat-intel-cache.json",
+        ttl_seconds: int | None = None,
+        enabled: bool | None = None,
+        provider: str | None = None,
+        abuseipdb_key: str | None = None,
+        virustotal_key: str | None = None,
+        daily_limit: int | None = None,
+        error_ttl_seconds: int | None = None,
+        abuseipdb_max_age_days: int | None = None,
+    ) -> None:
         self.enabled = os.getenv("FGAI_THREAT_INTEL", "").lower() in {"1", "true", "yes", "on"} if enabled is None else enabled
-        self.abuseipdb_key = os.getenv("ABUSEIPDB_API_KEY")
+        self.abuseipdb_key = abuseipdb_key if abuseipdb_key is not None else os.getenv("ABUSEIPDB_API_KEY")
-        self.virustotal_key = os.getenv("VIRUSTOTAL_API_KEY")
+        self.virustotal_key = virustotal_key if virustotal_key is not None else os.getenv("VIRUSTOTAL_API_KEY")
-        self.provider = os.getenv("FGAI_THREAT_INTEL_PROVIDER", "auto").lower()
+        self.provider = (provider if provider is not None else os.getenv("FGAI_THREAT_INTEL_PROVIDER", "auto")).lower()
-        self.max_age_days = int(os.getenv("ABUSEIPDB_MAX_AGE_DAYS", "90"))
+        self.max_age_days = abuseipdb_max_age_days if abuseipdb_max_age_days is not None else int(os.getenv("ABUSEIPDB_MAX_AGE_DAYS", "90"))
         self.cache_path = Path(cache_file)
         self.ttl_seconds = ttl_seconds if ttl_seconds is not None else int(os.getenv("FGAI_THREAT_INTEL_TTL_SECONDS", "604800"))
-        self.error_ttl_seconds = int(os.getenv("FGAI_THREAT_INTEL_ERROR_TTL_SECONDS", "3600"))
+        self.error_ttl_seconds = error_ttl_seconds if error_ttl_seconds is not None else int(os.getenv("FGAI_THREAT_INTEL_ERROR_TTL_SECONDS", "3600"))
-        self.daily_limit = int(os.getenv("FGAI_THREAT_INTEL_DAILY_LIMIT", "100"))
+        self.daily_limit = daily_limit if daily_limit is not None else int(os.getenv("FGAI_THREAT_INTEL_DAILY_LIMIT", "100"))
         self.cache = self._read_cache()
 
     def status(self) -> dict[str, object]:
@@ -196,9 +208,20 @@ class ThreatIntelClient:
 
 
 def enrich_ips(
-    ips: list[str], *, cache_file: str = "state/threat-intel-cache.json", limit: int = 25, enabled: bool | None = None
+    ips: list[str], *, cache_file: str = "state/threat-intel-cache.json", limit: int = 25, enabled: bool | None = None, config: dict[str, object] | None = None
 ) -> dict[str, dict[str, object]]:
-    client = ThreatIntelClient(cache_file=cache_file, enabled=enabled)
+    config = config or {}
+    client = ThreatIntelClient(
+        cache_file=cache_file,
+        enabled=enabled,
+        provider=str(config.get("threat_intel_provider", "auto")),
+        abuseipdb_key=str(config.get("abuseipdb_api_key", "") or "") or None,
+        virustotal_key=str(config.get("virustotal_api_key", "") or "") or None,
+        daily_limit=int(config.get("threat_intel_daily_limit", 100) or 100),
+        ttl_seconds=int(config.get("threat_intel_ttl_seconds", 604800) or 604800),
+        error_ttl_seconds=int(config.get("threat_intel_error_ttl_seconds", 3600) or 3600),
+        abuseipdb_max_age_days=int(config.get("abuseipdb_max_age_days", 90) or 90),
+    )
     enriched: dict[str, dict[str, object]] = {}
     for ip in ips[:limit]:
         enriched[ip] = client.lookup_ip(ip)

src/fgai/triage.py

@@ -0,0 +1,112 @@
+from __future__ import annotations
+
+from collections import Counter, defaultdict
+
+
+def _severity(score: int) -> str:
+    return "critical" if score >= 85 else "high" if score >= 60 else "medium" if score >= 35 else "low"
+
+
+def build_triage_queue(
+    incidents: list[dict[str, object]],
+    field_deviations: dict[str, list[dict[str, object]]],
+    correlations: list[dict[str, object]],
+    recommendations: list[object],
+    *,
+    limit: int = 25,
+) -> list[dict[str, object]]:
+    """Summarize raw findings into analyst-sized investigation candidates."""
+    groups: dict[str, dict[str, object]] = defaultdict(lambda: {
+        "entity": "",
+        "score": 0,
+        "streams": set(),
+        "detectors": Counter(),
+        "evidence": [],
+        "deviations": 0,
+        "reviewed": Counter(),
+        "correlated_streams": 0,
+        "recommendations": [],
+        "incident_id": "",
+        "state": "open",
+    })
+    for incident in incidents:
+        entity = str(incident.get("entity", ""))
+        if not entity:
+            continue
+        group = groups[entity]
+        group["entity"] = entity
+        group["score"] = max(int(group["score"]), int(incident.get("score", 0) or 0))
+        group["incident_id"] = str(incident.get("id", ""))
+        group["state"] = str(incident.get("lifecycle_status", "open") or "open")
+        group["streams"].update(str(item) for item in incident.get("correlated_streams", []) if item)
+        group["evidence"].extend(str(item) for item in incident.get("evidence", []) if item)
+    for entity, deviations in field_deviations.items():
+        group = groups[str(entity)]
+        group["entity"] = str(entity)
+        active_scores = []
+        for item in deviations:
+            status = str(item.get("feedback", "unreviewed") or "unreviewed")
+            group["reviewed"][status] += 1
+            group["deviations"] = int(group["deviations"]) + 1
+            group["streams"].add(str(item.get("stream_name") or item.get("stream_title") or item.get("stream_id", "")))
+            group["detectors"][str(item.get("detector", "unknown"))] += 1
+            if status in {"expected", "false_positive"}:
+                continue
+            score = int(item.get("score", 0) or 0)
+            active_scores.append(score)
+            group["evidence"].append(str(item.get("reason", "")))
+        if active_scores:
+            group["score"] = max(int(group["score"]), max(active_scores))
+    for correlation in correlations:
+        entity = str(correlation.get("entity") or correlation.get("source_ip") or "")
+        if not entity:
+            continue
+        group = groups[entity]
+        group["entity"] = entity
+        streams = [str(item) for item in correlation.get("streams", []) if item]
+        group["streams"].update(streams)
+        group["correlated_streams"] = max(int(group["correlated_streams"]), len(streams))
+        if len(streams) > 1:
+            group["score"] = max(int(group["score"]), min(100, 25 + len(streams) * 8 + int(correlation.get("security_events", 0) or 0) * 2))
+            group["evidence"].append(f"activity observed across {len(streams)} streams")
+    for recommendation in recommendations:
+        subject = str(getattr(recommendation, "subject", ""))
+        if not subject:
+            continue
+        group = groups[subject]
+        group["entity"] = subject
+        group["score"] = max(int(group["score"]), int(getattr(recommendation, "score", 0) or 0))
+        group["recommendations"].append(str(getattr(recommendation, "title", "")))
+
+    queue = []
+    for group in groups.values():
+        score = int(group["score"])
+        if score <= 0:
+            continue
+        reviewed = group["reviewed"]
+        active = int(group["deviations"]) - int(reviewed.get("expected", 0)) - int(reviewed.get("false_positive", 0))
+        if active <= 0 and score < 60:
+            continue
+        detectors = group["detectors"].most_common(3)
+        evidence = list(dict.fromkeys(str(item) for item in group["evidence"] if item))[:4]
+        next_action = "confirm or suppress expected behavior"
+        if int(group["correlated_streams"]) > 1:
+            next_action = "investigate cross-source activity"
+        if reviewed.get("confirmed"):
+            next_action = "follow confirmed incident response"
+        queue.append({
+            "entity": group["entity"],
+            "score": score,
+            "severity": _severity(score),
+            "state": group["state"],
+            "incident_id": group["incident_id"],
+            "streams": sorted(item for item in group["streams"] if item),
+            "detectors": [name for name, _count in detectors],
+            "deviation_count": int(group["deviations"]),
+            "active_deviation_count": active,
+            "reviewed": dict(reviewed),
+            "evidence": evidence,
+            "recommendations": [item for item in group["recommendations"] if item][:3],
+            "next_action": next_action,
+        })
+    return sorted(queue, key=lambda item: (int(item["score"]), int(item["active_deviation_count"])), reverse=True)[:limit]

tests/test_baseline.py

@@ -130,3 +130,20 @@ class BaselineTests(unittest.TestCase):
 
             self.assertLessEqual(len(rare), 5)
             self.assertTrue(all(item["score"] >= 15 for item in rare))
+
+    def test_training_days_gate_profile_deviations(self):
+        with tempfile.TemporaryDirectory() as directory:
+            store = BaselineStore(str(Path(directory) / "baseline.sqlite3"))
+            profiles = parse_profiles([{"stream_id": "windows", "entity_field": "username", "categorical_fields": ["action"]}])
+            now = int(time.time())
+            for index in range(12):
+                event = parse_log_line(f"fgai_stream_id=windows username=alice action=failed baseline={index}")
+                store.ingest_profile_fields([event], profiles, observed_at=now - 86400 + index * 300)
+
+            burst = [
+                parse_log_line(f"fgai_stream_id=windows username=alice action=failed burst={index}")
+                for index in range(5)
+            ]
+
+            self.assertNotIn("alice", store.profile_deviations(burst, profiles, min_training_days=7))
+            self.assertIn("alice", store.profile_deviations(burst, profiles, min_training_days=0))

tests/test_triage.py

@@ -0,0 +1,27 @@
+import unittest
+
+from fgai.triage import build_triage_queue
+
+
+class TriageTests(unittest.TestCase):
+    def test_builds_compact_queue_from_raw_deviations(self):
+        queue = build_triage_queue(
+            [{"id": "abc", "entity": "alice", "score": 42, "severity": "medium", "correlated_streams": ["Windows"], "evidence": ["login burst"]}],
+            {
+                "alice": [
+                    {"score": 60, "detector": "auth_failure_burst", "stream_name": "Windows", "reason": "auth failure burst"},
+                    {"score": 0, "detector": "rare_value", "stream_name": "DNS", "reason": "known", "feedback": "expected"},
+                ]
+            },
+            [{"entity": "alice", "streams": ["Windows", "DNS"], "security_events": 2}],
+            [],
+        )
+
+        self.assertEqual(queue[0]["entity"], "alice")
+        self.assertEqual(queue[0]["active_deviation_count"], 1)
+        self.assertIn("auth_failure_burst", queue[0]["detectors"])
+        self.assertEqual(queue[0]["next_action"], "investigate cross-source activity")
+
+
+if __name__ == "__main__":
+    unittest.main()