Finish KG-WP-0003: stream completeness and live qonto observation

Classify evidence as load-bearing or attributive, draft the emission-cadence
declaration for Taxonomy, treat silence as a stream finding, keep completeness
separate from record richness, forbid immune memory as a state plane, and make
containment proposals reconstructable to their origin. Observe real
qonto-assistant audit events; deny-class completeness stays unknown until the
source publishes a heartbeat.

Assistant: grok
Assistant-Session: 01a05ef1-9e5a-70f2-b0ff-0b05d6b38ae9
This commit is contained in:
tegwick 2026-09-02 00:11:57 +02:00
parent c85646dc3c
commit 9daea96c43
35 changed files with 2023 additions and 138 deletions

View file

@ -1,16 +1,26 @@
from __future__ import annotations
from collections.abc import Sequence
from kings_guard.cadence import EmissionCadence
from kings_guard.contracts import (
STREAM_FINDING_PREFIX,
EffectorRequest,
ImmuneObservation,
ImmuneSignal,
PostureAssessment,
PostureEvaluation,
PostureLevel,
ReconciliationView,
RestrictiveDirection,
SecurityGenome,
SecurityPhenotype,
SignalKind,
StreamAssessment,
StreamCompleteness,
StreamHeartbeat,
)
from kings_guard.stream import evaluate_stream as assess_stream
CRITICAL_FINDINGS = frozenset(
{
@ -39,13 +49,70 @@ RISK_WEIGHTS = {
"control_plane_error": 20,
}
UNKNOWN_STREAM_REASON = (
"per-observation evaluation cannot vouch for stream completeness; "
"the record in hand was scored for richness only"
)
class PostureEvaluator:
def evaluate(self, genome: SecurityGenome, observation: ImmuneObservation) -> PostureEvaluation:
def evaluate(
self,
genome: SecurityGenome,
observation: ImmuneObservation,
*,
stream: StreamAssessment | None = None,
) -> PostureEvaluation:
phenotype = self._derive_phenotype(genome, observation)
assessment = self._assess(phenotype, observation)
signals = self._build_signals(observation, assessment)
return PostureEvaluation(phenotype=phenotype, assessment=assessment, signals=signals)
assessment = self._assess(phenotype, observation, stream=stream)
signals = self._build_signals(observation, assessment, stream=stream)
return PostureEvaluation(
phenotype=phenotype,
assessment=assessment,
signals=signals,
stream=stream,
)
def evaluate_stream(
self,
observations: Sequence[ImmuneObservation],
cadence: EmissionCadence,
*,
now: str,
watching_since: str | None = None,
heartbeats: Sequence[StreamHeartbeat] = (),
reconciliation: ReconciliationView | None = None,
) -> StreamAssessment:
return assess_stream(
observations,
cadence,
now=now,
watching_since=watching_since,
heartbeats=heartbeats,
reconciliation=reconciliation,
)
def evaluate_with_stream(
self,
genome: SecurityGenome,
observation: ImmuneObservation,
cadence: EmissionCadence,
*,
now: str,
observations: Sequence[ImmuneObservation] | None = None,
watching_since: str | None = None,
heartbeats: Sequence[StreamHeartbeat] = (),
reconciliation: ReconciliationView | None = None,
) -> PostureEvaluation:
stream = self.evaluate_stream(
observations if observations is not None else (observation,),
cadence,
now=now,
watching_since=watching_since,
heartbeats=heartbeats,
reconciliation=reconciliation,
)
return self.evaluate(genome, observation, stream=stream)
def _derive_phenotype(
self,
@ -55,6 +122,17 @@ class PostureEvaluator:
findings: list[str] = []
tolerated: list[str] = []
if observation.decision is observation.decision.HEARTBEAT:
return SecurityPhenotype(
subject_id=observation.subject_id,
tenant_id=observation.tenant_id,
observed_capability=observation.capability,
protocol=observation.protocol,
decision=observation.decision,
active_findings=(),
tolerated_findings=(),
)
if observation.tenant_id != genome.tenant_id:
findings.append("tenant_mismatch")
if observation.capability not in genome.permitted_capabilities:
@ -95,6 +173,8 @@ class PostureEvaluator:
self,
phenotype: SecurityPhenotype,
observation: ImmuneObservation,
*,
stream: StreamAssessment | None,
) -> PostureAssessment:
findings = set(phenotype.active_findings)
@ -115,6 +195,7 @@ class PostureEvaluator:
risk_score = max(risk_score, 95)
risk_score = min(risk_score, 100)
# Richness of the record received — never of the stream.
confidence_score = 70
if observation.policy_version is not None:
confidence_score += 10
@ -124,15 +205,26 @@ class PostureEvaluator:
confidence_score += 5
confidence_score = min(confidence_score, 95)
if stream is None:
completeness = StreamCompleteness.UNKNOWN
completeness_reason = UNKNOWN_STREAM_REASON
else:
completeness = stream.completeness
completeness_reason = stream.reason
rationale = _build_rationale(
posture=posture,
findings=phenotype.active_findings,
tolerated_findings=phenotype.tolerated_findings,
completeness=completeness,
completeness_reason=completeness_reason,
)
return PostureAssessment(
posture=posture,
risk_score=risk_score,
confidence_score=confidence_score,
stream_completeness=completeness,
completeness_reason=completeness_reason,
findings=phenotype.active_findings,
tolerated_findings=phenotype.tolerated_findings,
rationale=rationale,
@ -142,23 +234,47 @@ class PostureEvaluator:
self,
observation: ImmuneObservation,
assessment: PostureAssessment,
*,
stream: StreamAssessment | None,
) -> tuple[ImmuneSignal, ...]:
if assessment.posture is PostureLevel.HEALTHY:
return ()
signals: list[ImmuneSignal] = []
if assessment.posture is not PostureLevel.HEALTHY:
if observation.source_system == "qonto-assistant":
signals.append(self._build_qonto_pilot_signal(observation, assessment))
else:
signals.append(
ImmuneSignal(
signal_id=f"sig:{observation.observation_id}",
signal_kind=SignalKind.OBSERVATION_ALERT,
posture=assessment.posture,
summary=assessment.rationale,
target_system=observation.source_system,
findings=assessment.findings,
metadata={"source_system": observation.source_system},
)
)
if stream is not None and stream.findings:
signals.append(self._build_stream_signal(observation, assessment, stream))
return tuple(signals)
if observation.source_system == "qonto-assistant":
return (self._build_qonto_pilot_signal(observation, assessment),)
signal = ImmuneSignal(
signal_id=f"sig:{observation.observation_id}",
signal_kind=SignalKind.OBSERVATION_ALERT,
def _build_stream_signal(
self,
observation: ImmuneObservation,
assessment: PostureAssessment,
stream: StreamAssessment,
) -> ImmuneSignal:
return ImmuneSignal(
signal_id=f"sig:stream:{observation.observation_id}",
signal_kind=SignalKind.STREAM_COMPLETENESS,
posture=assessment.posture,
summary=assessment.rationale,
summary=stream.reason,
target_system=observation.source_system,
findings=assessment.findings,
metadata={"source_system": observation.source_system},
findings=stream.findings,
metadata={
"stream_completeness": stream.completeness.value,
"finding_class": "stream",
},
)
return (signal,)
def _build_qonto_pilot_signal(
self,
@ -167,19 +283,22 @@ class PostureEvaluator:
) -> ImmuneSignal:
if "credential_exfil_probe" in assessment.findings:
action = "lock_actor_temporarily"
direction: RestrictiveDirection = "reduce_authority"
reason = (
"Observed a credential-exfil deny signal; qonto-assistant should activate "
"its fast local loop lockout and preserve metadata-only evidence."
)
else:
action = "tighten_actor_scrutiny"
direction = "require_step_up"
reason = (
"Observed repeated policy-boundary pressure; qonto-assistant should tighten "
"local scrutiny without delegating final authorization to kings-guard."
)
signal_id = f"sig:{observation.observation_id}"
return ImmuneSignal(
signal_id=f"sig:{observation.observation_id}",
signal_id=signal_id,
signal_kind=SignalKind.POSTURE_HINT,
posture=assessment.posture,
summary=reason,
@ -192,6 +311,10 @@ class PostureEvaluator:
authority_boundary="advisory_only",
reason=reason,
requires_human_approval=False,
originating_observation_id=observation.observation_id,
originating_signal_id=signal_id,
stream_completeness=assessment.stream_completeness,
restrictive_direction=direction,
),
EffectorRequest(
target_system="state-hub",
@ -199,11 +322,16 @@ class PostureEvaluator:
authority_boundary="metadata_only",
reason="Preserve posture evidence without copying secret values.",
requires_human_approval=False,
originating_observation_id=observation.observation_id,
originating_signal_id=signal_id,
stream_completeness=assessment.stream_completeness,
restrictive_direction="none",
),
),
metadata={
"pilot_lane": "qonto-assistant",
"resource_scope": observation.resource_scope or "unknown",
"originating_observation_id": observation.observation_id,
},
)
@ -224,19 +352,40 @@ def _build_rationale(
posture: PostureLevel,
findings: tuple[str, ...],
tolerated_findings: tuple[str, ...],
completeness: StreamCompleteness,
completeness_reason: str,
) -> str:
if posture is PostureLevel.HEALTHY:
if tolerated_findings:
return (
content = (
"Healthy posture with tolerated deviations only: "
+ ", ".join(tolerated_findings)
)
return "Healthy posture: observation is compatible with declared intent."
else:
content = "Healthy posture: observation is compatible with declared intent."
else:
detail = ", ".join(findings) if findings else "no active findings"
tolerated = (
f" Tolerated deviations still present: {', '.join(tolerated_findings)}."
if tolerated_findings
else ""
)
content = f"{posture.value.title()} posture driven by {detail}.{tolerated}"
detail = ", ".join(findings) if findings else "no active findings"
tolerated = (
f" Tolerated deviations still present: {', '.join(tolerated_findings)}."
if tolerated_findings
else ""
)
return f"{posture.value.title()} posture driven by {detail}.{tolerated}"
if completeness is StreamCompleteness.COMPLETE:
stream_text = "Stream completeness is complete."
elif completeness is StreamCompleteness.DEGRADED:
stream_text = (
"This judgment rests on a stream I cannot vouch for "
f"({completeness_reason})."
)
else:
stream_text = (
"Stream completeness is unknown; confidence scores the record, "
f"not the stream ({completeness_reason})."
)
return f"{content} {stream_text}"
def is_stream_finding(finding: str) -> bool:
return finding.startswith(STREAM_FINDING_PREFIX)