Jira progress: loading…

TG-OBSERVE

TrustGate Runtime Observability Engine

0. Identity

Loading identity…

Depends on module:

Decision-grade verification and assurance domain that orchestrates verifier workflows, evidence handling, trust scoring, audit trails, and assurance packaging across CSRD/ESRS and other frameworks. Enables third-party sign-off, dispute handling, and assurance-ready disclosure outputs with replayable provenance.

Domain:

assurance-verification

Category:

trust-assurance

Classification:

module

Lifecycle status:

active

Semver:

1.0.0

Introduced in:

v0.3

Governance

AI risk level:

high

Trust threshold:

0.97

Human review required:

true

Verifier involved:

true

Audit required:

true

Ownership

Primary owner:

Platform

Architecture board:

true

White-label allowed:

true

Entrypoints

Docs:

/verification-assurance

UI:

/app/assurance

API:

/api/assurance

Dependencies

Modules

sis
input-hub
reports-insights-hub
zara
zaam

Unresolved tokens

ALTD
DAL
EvidenceVault
StripeConnect
TruliooKYB
TrustGate
VTE
VerifierWorkflowEngine

Engines (declared)

VerifierWorkflowEngine
DaVE
VTE
DICE
EvidenceVault
ALTD
DAL
TrustGate
StripeConnect
TruliooKYB
AISIM

Micro-engines (from registry)

None

Micro-engines (declared)

None

Signals

USO

ASSURANCE.VERIFICATION
ASSURANCE.EVIDENCE
ASSURANCE.SIGNOFF
AUDIT.LINEAGE
GOVERNANCE.TRUST
IDENTITY.VERIFIER
PAYMENT.ESCROW

CSI

CSI_VERIFICATION_ASSURANCE

SSSR tags

assurance
verification
verifiers
evidence
signoff
trust-score
audit
tamper-detection
blockchain
rfp
disputes
escrow
stripe-connect

Workflows & Outputs

Workflows

VerifierOnboardingAndQualification
AssuranceRFPAndProposalFlow
EvidenceRequestAndCollection
EvidenceValidationAndScoring
TrustScoreComputationAndPropagation
VerifierReviewAndCommenting
IssueFlaggingAndDisputeResolution
VerifierSignOffAndStamping
AssurancePackagingForReports
PaymentEscrowAndPayoutOrchestration

Outputs

verifier_profiles
assurance_requests
evidence_packages
validation_findings
trust_score_updates
signoff_stamps
assurance_ready_disclosure_packages
payment_events

Audit

Ledger:

ALTD

Replay supported:

true

PII policy:

controlled_pii_outside_omr

1. Purpose

The TrustGate Runtime Observability Engine is the operational intelligence layer for the TrustGate runtime.

It collects, correlates, normalizes, analyzes, and exports runtime telemetry from every TrustGate micro-engine. It provides the visibility required to operate TrustGate as a high-assurance, production-grade, replayable, federated, and AI-aware trust system.

The engine answers operational and assurance-critical questions such as:

Are TrustGate engines healthy?
Which engine is introducing latency?
Which validation rules fail most frequently?
Are replay operations deterministic?
Are DAL anchors delayed?
Are federation exchanges failing?
Are trust scores drifting?
Are queues backing up?
Are SLOs being breached?
Is an ECO node producing abnormal runtime patterns?
Are AI recommendations correlated with later assurance failures?
Can auditors reconstruct runtime behavior for a given signal, period, or ECO entity?

This engine is not a business-rule engine and does not alter TrustGate decisions. It observes the runtime and emits governed operational intelligence.

2. Architectural Role

TrustGate already contains engines that parse, validate, enrich, score, decide, replay, attest, anchor, federate, and audit.

The Observability Engine does not sit in the critical path for every signal. Instead, it operates as a runtime-sidecar and telemetry aggregator.

TrustGate Runtime Engines
      │
      ├── Metrics
      ├── Traces
      ├── Logs
      ├── Events
      ├── Health Signals
      ├── Queue Signals
      └── SLO Signals
              │
              ▼
──────────────────────────────────────────
 Runtime Observability Engine
──────────────────────────────────────────
              │
              ├── VIZZ Dashboards
              ├── AII Telemetry
              ├── DSAIL Feedback
              ├── Alerts
              ├── Audit Evidence
              ├── DAL Anchor Candidates
              └── Operations Reports

The Observability Engine provides the operational evidence layer required by internal platform operations, assurance staff, auditors, developers, and governance reviewers.

3. Canonical Identity

Property	Value
MEID	`MEID_TRUST_MONITOR_RUNTIME`
CMI	`vera.TG-OBSERVE.ENGINE.RUNTIME.1_0_0`
KIND	`ENGINE`
Owner Module	Verification & Assurance
Runtime Tier	Tier-0 Runtime Operations
Engine Category	`MONITOR`
Engine Domain	`trust`
ZAR Registration	Required
Replay Support	Native for telemetry events
DAL Anchoring	Policy-based
Federation Aware	Yes
Critical Path	No, except health gating policies

The MEID identifies the stable logical observability engine.

The CMI identifies this specific versioned executable artifact.

4. Scope

The engine observes all TrustGate micro-engines.

Engine	Observability Scope
Signal Parser	Parse latency, parse failures, payload type distribution.
Structure Validation	Schema failures, datatype failures, validation latency.
Signal Normalizer	Unit conversion failures, normalization drift, latency.
Context Enrichment	Resolver latency, context completeness, identity resolution.
Validation Rule Engine	Rule execution count, failure frequency, severity distribution.
Rule Evaluation Engine	Rule outcomes, policy interactions, evaluation latency.
Policy Resolution	Policy lookup latency, cache hit rate, policy drift.
Trust Scoring	Score distribution, confidence distribution, dimension variance.
Decision Engine	Decision outcome rates, review/quarantine/reject rates.
Quarantine Manager	Quarantine aging, SLA breaches, release/reject ratios.
Replay Engine	Replay success, replay drift, replay latency.
Attestation Engine	Attestation creation rate, signature queue, federation readiness.
DAL Anchor Engine	Anchor candidate rate, anchor latency, verification rate.
Federation Exchange	Export/import success, conflict rate, redaction failures.
AI Feedback	Feedback publication success, DSAIL queue depth, suppression rate.
Audit Log Engine	Audit write latency, chain conflicts, audit durability.

The engine also observes shared services used by TrustGate, including ZAR, USO, SSSR, DAL, EGFS, AFLE, FAGF, OARM, AAE, DSAIL, AII, VIZZ, and ZSSR.

5. Runtime Responsibilities

Responsibility	Description
Metrics Collection	Collect counters, gauges, histograms, and summaries from TrustGate engines.
Distributed Tracing	Correlate signal-level execution across micro-engines using trace IDs and span IDs.
Structured Log Ingestion	Ingest structured operational logs and convert them into searchable assurance telemetry.
Health Monitoring	Track liveness, readiness, dependency status, and engine runtime health.
SLO Monitoring	Evaluate SLOs for latency, throughput, replay success, DAL anchoring, federation, and availability.
Queue Monitoring	Track queue depth, processing lag, retry count, and dead-letter rates.
Runtime Anomaly Detection	Detect unexpected runtime patterns, failure bursts, trust drift, and dependency degradation.
Dashboard Feed Production	Publish VIZZ dashboard feeds for platform operations and assurance teams.
Alert Generation	Emit operational alerts and governance alerts.
AII Telemetry	Publish runtime integrity, resilience, transparency, and efficiency metrics to AII.
DSAIL Feedback	Send operational drift and anomaly telemetry to DSAIL.
Audit Binding	Link runtime telemetry to TrustGate Audit Log events and USO lineage.
DAL Candidate Creation	Produce DAL anchor candidates for critical observability events.
Federation Observability	Track cross-ECO exchange health and federation telemetry.

The engine is an observer and telemetry publisher. It must not modify source events.

6. Observability Data Classes

The engine processes six canonical observability data classes.

Class	Description
`METRIC`	Numeric time-series measurements.
`TRACE`	Distributed execution trace spans.
`LOG`	Structured runtime logs.
`EVENT`	Domain-specific TrustGate runtime event.
`HEALTH`	Engine and dependency health status.
`SLO`	Service-level objective evaluation result.

Each class must preserve source engine, MEID, CMI, timestamp, ECO scope, tenant scope, trace context, and policy context where available.

7. Observability Architecture

TrustGate Engine
    │
    ├── OpenTelemetry SDK
    ├── Runtime Event Publisher
    ├── Structured Logger
    └── Health Endpoint
            │
            ▼
Telemetry Collector Layer
            │
            ├── Metrics Pipeline
            ├── Traces Pipeline
            ├── Logs Pipeline
            └── Event Pipeline
                    │
                    ▼
Runtime Observability Engine
                    │
                    ├── Normalizer
                    ├── Correlator
                    ├── SLO Evaluator
                    ├── Anomaly Detector
                    ├── Alert Publisher
                    ├── VIZZ Feed Publisher
                    ├── AII Publisher
                    └── DSAIL Publisher

The engine should use OpenTelemetry-compatible structures wherever possible, but ZAYAZ-specific assurance metadata must be preserved.

8. Runtime Processing Pipeline

Receive Telemetry
      │
      ▼
Validate Telemetry Schema
      │
      ▼
Attach Engine Identity
      │
      ▼
Attach Signal / ECO / Tenant Context
      │
      ▼
Normalize Metric / Trace / Log / Event
      │
      ▼
Correlate by Trace and Signal
      │
      ▼
Evaluate SLOs
      │
      ▼
Detect Anomalies
      │
      ▼
Publish Metrics and Dashboards
      │
      ▼
Emit Alerts if Required
      │
      ▼
Publish AII / DSAIL Feedback

The pipeline may run asynchronously but must preserve event ordering guarantees within a trace or signal chain where policy requires audit-grade correlation.

9. Processing Algorithm

function observe(telemetry_event):

    validate_schema(telemetry_event)

    engine_identity = resolve_engine_identity(
        telemetry_event.meid,
        telemetry_event.cmi
    )

    runtime_context = resolve_runtime_context(
        telemetry_event.signal_id,
        telemetry_event.eco_number,
        telemetry_event.trace_id
    )

    normalized = normalize_telemetry(
        telemetry_event,
        engine_identity,
        runtime_context
    )

    correlated = correlate_trace_and_signal(normalized)

    slo_results = evaluate_slos(correlated)

    anomaly_results = detect_anomalies(correlated, slo_results)

    publish_to_timeseries(normalized)

    publish_to_vizz(correlated, slo_results, anomaly_results)

    if alert_required(slo_results, anomaly_results):
        emit_alert(correlated, slo_results, anomaly_results)

    if aii_relevant(normalized):
        publish_aii_telemetry(normalized)

    if dsail_relevant(anomaly_results):
        publish_dsail_feedback(anomaly_results)

    if dal_anchor_required(slo_results, anomaly_results):
        queue_dal_anchor_candidate(correlated)

    return correlated

The engine must be idempotent for duplicate telemetry events.

10. Telemetry Identity Model

Every telemetry record must include identity metadata.

Field	Required	Description
`telemetry_id`	Yes	Unique telemetry record ID.
`trace_id`	Conditional	Distributed trace ID.
`span_id`	Conditional	Span ID for trace events.
`signal_id`	Conditional	Signal being processed.
`eco_number`	Conditional	ECO entity associated with the event.
`tenant_id`	Conditional	Tenant scope.
`engine_meid`	Yes	Source engine MEID.
`engine_cmi`	Yes	Source engine CMI.
`source_component`	Yes	Runtime component name.
`policy_bundle`	Conditional	Active policy bundle.
`timestamp`	Yes	Event timestamp.
`environment`	Yes	dev, test, staging, production.
`region`	Conditional	Runtime region.

Telemetry without MEID and CMI is not assurance-grade.

11. Trace Correlation Model

TrustGate uses distributed tracing to reconstruct end-to-end runtime paths.

trace_id: TRACE-2026-00001472

Span 1: Signal Parser
Span 2: Structure Validation
Span 3: Signal Normalizer
Span 4: Context Enrichment
Span 5: Validation Rule Engine
Span 6: Policy Resolution
Span 7: Rule Evaluation
Span 8: Trust Scoring
Span 9: Decision Engine
Span 10: Replay Engine
Span 11: Attestation Engine
Span 12: DAL Anchor Engine

Each span should include span name, duration, engine identity, signal identity, status, error code, parent span ID, policy bundle, and runtime region.

12. Canonical Trace Span

{
  "trace_id": "TRACE-2026-00001472",
  "span_id": "SPAN-0007",
  "parent_span_id": "SPAN-0006",
  "span_name": "trustgate.trust_scoring",
  "signal_id": "SIG-2026-00001472",
  "eco_number": "ECO-A123",
  "engine_meid": "MEID_TRUST_SCORE_CALCULATOR",
  "engine_cmi": "vera.TG-SCORE.ENGINE.CALCULATOR.1_0_0",
  "status": "ok",
  "duration_ms": 28,
  "policy_bundle": "FAGF-2026.2",
  "environment": "production",
  "region": "eu-north-1",
  "started_at": "2026-06-25T15:00:01.100Z",
  "ended_at": "2026-06-25T15:00:01.128Z"
}

Trace spans must be exportable to OpenTelemetry-compatible backends.

13. Metrics Model

The engine supports standard metric types.

Metric Type	Description
Counter	Monotonic count, such as total decisions.
Gauge	Current value, such as queue depth.
Histogram	Distribution, such as latency.
Summary	Aggregated quantiles.
Ratio	Derived value, such as replay success rate.
Composite KPI	Multi-source metric, such as TrustGate health score.

Metrics must include source metadata and dimensions.

14. Core Runtime Metrics

Metric	Type	Description
`tg_runtime_signals_total`	Counter	Total signals received.
`tg_runtime_signals_processed_total`	Counter	Signals completed through decisioning.
`tg_runtime_latency_ms`	Histogram	End-to-end TrustGate processing latency.
`tg_runtime_engine_latency_ms`	Histogram	Per-engine latency.
`tg_runtime_error_total`	Counter	Runtime errors by engine and severity.
`tg_runtime_queue_depth`	Gauge	Queue depth by queue and engine.
`tg_runtime_retry_total`	Counter	Retry attempts.
`tg_runtime_deadletter_total`	Counter	Dead-lettered events.
`tg_runtime_health_status`	Gauge	Engine health status.
`tg_runtime_slo_breach_total`	Counter	SLO breach count.

15. Assurance Metrics

Metric	Type	Description
`tg_validation_failure_rate`	Ratio	Failed validation rules divided by executed rules.
`tg_trust_score_avg`	Gauge	Rolling average TrustGate score.
`tg_trust_score_variance`	Gauge	Trust score variance.
`tg_confidence_avg`	Gauge	Rolling average confidence.
`tg_decision_accept_ratio`	Ratio	Accepted decisions divided by total decisions.
`tg_decision_review_ratio`	Ratio	Review decisions divided by total decisions.
`tg_decision_quarantine_ratio`	Ratio	Quarantine decisions divided by total decisions.
`tg_replay_success_rate`	Ratio	Successful replays divided by replay attempts.
`tg_replay_drift_rate`	Ratio	Replay drift events divided by replay attempts.
`tg_dal_verification_rate`	Ratio	Verified anchors divided by submitted anchors.
`tg_federation_conflict_rate`	Ratio	Federation conflicts divided by exchanges.
`tg_attestation_success_rate`	Ratio	Successful attestations divided by attempts.

These metrics feed AII and VIZZ.

16. SLO Model

The Observability Engine evaluates TrustGate SLOs.

SLO	Default Target
TrustGate Runtime Availability	`99.99%`
End-to-End P95 Latency	`< 500 ms`
Parser P95 Latency	`< 50 ms`
Validation P95 Latency	`< 120 ms`
Context Enrichment P95 Latency	`< 150 ms`
Trust Scoring P95 Latency	`< 80 ms`
Decision P95 Latency	`< 50 ms`
Replay Manifest Creation P95	`< 250 ms`
DAL Anchor Verification Rate	`> 99.95%`
Replay Determinism	`100%` where prerequisites exist
Audit Critical Event Durability	`100%`
Federation Verification Rate	`> 99.5%`

SLOs may vary by tenant, region, assurance tier, and policy bundle.

17. SLO Evaluation Formula

Availability may be calculated as:

Availability = 1 - \\frac{UnavailableTime}{TotalObservationWindow}

Latency compliance may be calculated as:

LatencyCompliance = \\frac{RequestsWithinThreshold}{TotalRequests}

Replay integrity may be calculated as:

ReplayIntegrity = \\frac{SuccessfulReplays}{TotalReplayAttempts}

DAL verification rate may be calculated as:

DALVerificationRate = \\frac{VerifiedAnchors}{SubmittedAnchors}

18. TrustGate Runtime Health Score

The engine may compute an internal runtime health score.

H = w_aA + w_lL + w_eE + w_qQ + w_rR + w_dD + w_fF

Where:

Variable	Meaning
$A$	Availability score
$L$	Latency compliance
$E$	Error-rate score
$Q$	Queue health
$R$	Replay integrity
$D$	DAL verification health
$F$	Federation health

Weights must satisfy:

\\sum w = 1

The runtime health score is operational and must not be confused with a TrustGate trust score or AII score.

19. Score-Gaming Telemetry

TG-OBSERVE shall publish score-distribution telemetry to AII for governed detection of strategic threshold optimization.

Required monitoring includes:

bunching immediately above decision thresholds;
discontinuities in score density near cut-points;
sudden modifier adoption without corresponding evidence-quality improvement;
repeated near-threshold submissions;
divergence between modifier-driven score improvement and replay stability;
tenant, supplier, industry, and evidence-type cohorts.

The detector shall use the private threshold configuration internally but shall not disclose tenant-specific numeric cut-points to submitters.

Detected anomalies shall emit:

comp.TG.EVENT.BUNCHING-ALERT.v1_0; or
comp.TG.EVENT.MODIFIER-ANOMALY.v1_0.

An alert is evidence of potential gaming, not proof of misconduct. Governance review is required before adverse action.

20. Queue Observability

The engine observes asynchronous queues used by TrustGate.

Queue	Observability Fields
Validation Queue	depth, lag, dead-letter count, retry count.
Replay Queue	replay backlog, priority, age, failure rate.
DAL Anchor Queue	anchor backlog, verification delay, retry count.
Federation Queue	export/import backlog, conflict count, retry count.
AI Feedback Queue	DSAIL publication lag, suppression count, retry count.
Audit Queue	audit write lag, critical event backlog.
VIZZ Feed Queue	dashboard feed lag.

Queue backlog may trigger alerts or scheduler actions.

21. Dependency Health Model

The engine monitors TrustGate dependencies.

Dependency	Health Signals
ZAR	registry latency, availability, stale read rate.
SSSR	schema lookup latency, schema mismatch count.
USO	lineage lookup latency, missing lineage rate.
DAL	anchor submission latency, verification rate.
EGFS	DID resolution, node health, federation sync.
AFLE	attestation exchange success, federation conflicts.
FAGF	policy bundle lookup latency, policy drift.
OARM	replay workflow availability, replay latency.
AAE	replay execution success, autonomous verification status.
DSAIL	feedback publication success, model advisory latency.
AII	telemetry publication success.
VIZZ	dashboard feed availability.

Dependency health is used for root-cause analysis and operational routing.

22. Runtime Anomaly Detection

The Observability Engine detects anomalies using deterministic rules and advisory DSAIL models.

Anomaly categories include:

Category	Example
Latency Spike	Trust Scoring P95 latency suddenly doubles.
Validation Burst	`TG-VAL-0010` failures rise above baseline.
Replay Drift Burst	Replay drift rate exceeds threshold.
DAL Delay	Anchor verification latency exceeds SLO.
Federation Conflict	Cross-ECO conflicts exceed policy threshold.
Score Distribution Shift	Trust score distribution changes unexpectedly.
Quarantine Spike	Quarantine rate exceeds normal range.
AI Feedback Suppression	Feedback events are suppressed unexpectedly.
Audit Chain Conflict	Audit evidence chain mismatch detected.
Dependency Degradation	ZAR, SSSR, DAL, or EGFS latency spikes.

AI anomaly detection is advisory. Deterministic alert rules remain authoritative.

23. Anomaly Score

A generic anomaly score may be computed as:

AnomalyScore = \\frac{|x_t - \\mu_{baseline}|}{\\sigma_{baseline}}

Where:

$x_t$ is the observed value;
$\\mu_{baseline}$ is the baseline mean;
$\\sigma_{baseline}$ is the baseline standard deviation.

Policy may define anomaly thresholds such as:

Score	Classification
`< 2`	Normal
`2 - 3`	Watch
`3 - 5`	Warning
`> 5`	Critical

24. Alert Model

Alerts are generated when SLOs, anomaly thresholds, or governance conditions are breached.

Alert Type	Description
`runtime_health_degraded`	Runtime health score below threshold.
`slo_breach`	SLO target breached.
`dependency_degraded`	Critical dependency health degraded.
`replay_drift_spike`	Replay drift above threshold.
`dal_anchor_delay`	DAL anchoring delayed.
`federation_conflict_spike`	Federation conflicts rising.
`validation_failure_spike`	Validation failures rising.
`audit_chain_conflict`	Audit integrity conflict detected.
`queue_backlog`	Queue depth or age exceeds threshold.
`ai_feedback_lag`	AI feedback publication lag exceeds threshold.

Alerts may be routed to VIZZ, PagerDuty, Slack, email, ZARA, or internal incident systems depending on policy.

25. Alert Payload

{
  "alert_id": "TGALERT-2026-00001472",
  "alert_type": "replay_drift_spike",
  "severity": "critical",
  "eco_number": "ECO-A123",
  "engine_meid": "MEID_TRUST_REPLAY_COORDINATOR",
  "engine_cmi": "vera.TG-REPLAY.ENGINE.COORDINATOR.1_0_0",
  "metric": "tg_replay_drift_rate",
  "observed_value": 0.018,
  "threshold": 0.005,
  "policy_bundle": "FAGF-2026.2",
  "recommended_action": "increase_manual_review_and_trigger_replay_sampling",
  "created_at": "2026-06-25T15:12:00Z"
}

Critical alerts must be audit logged.

26. Canonical Telemetry Input

{
  "telemetry_id": "TGTEL-2026-00001472",
  "telemetry_class": "METRIC",
  "metric_name": "tg_score_latency_ms",
  "metric_type": "histogram",
  "value": 28,
  "signal_id": "SIG-2026-00001472",
  "eco_number": "ECO-A123",
  "trace_id": "TRACE-2026-00001472",
  "span_id": "SPAN-0007",
  "engine_meid": "MEID_TRUST_SCORE_CALCULATOR",
  "engine_cmi": "vera.TG-SCORE.ENGINE.CALCULATOR.1_0_0",
  "policy_bundle": "FAGF-2026.2",
  "environment": "production",
  "region": "eu-north-1",
  "timestamp": "2026-06-25T15:00:01.128Z"
}

27. Canonical Observability Output

{
  "observability_event_id": "TGOBS-2026-00001472",
  "source_telemetry_id": "TGTEL-2026-00001472",
  "status": "processed",
  "correlation": {
    "trace_id": "TRACE-2026-00001472",
    "signal_id": "SIG-2026-00001472",
    "eco_number": "ECO-A123"
  },
  "engine": {
    "meid": "MEID_TRUST_SCORE_CALCULATOR",
    "cmi": "vera.TG-SCORE.ENGINE.CALCULATOR.1_0_0"
  },
  "metric": {
    "name": "tg_score_latency_ms",
    "value": 28,
    "unit": "ms"
  },
  "slo": {
    "evaluated": true,
    "status": "within_target"
  },
  "anomaly": {
    "evaluated": true,
    "status": "normal"
  },
  "created_at": "2026-06-25T15:00:01.140Z"
}

28. Health Status Event

{
  "event_type": "trustgate.runtime.health",
  "health_event_id": "TGHEALTH-2026-00001472",
  "engine_meid": "MEID_TRUST_MONITOR_RUNTIME",
  "engine_cmi": "vera.TG-OBSERVE.ENGINE.RUNTIME.1_0_0",
  "status": "healthy",
  "dependencies": {
    "ZAR": "healthy",
    "SSSR": "healthy",
    "USO": "healthy",
    "DAL": "degraded",
    "EGFS": "healthy",
    "DSAIL": "healthy"
  },
  "runtime_health_score": 0.94,
  "created_at": "2026-06-25T15:05:00Z"
}

29. VIZZ Dashboard Feed

The Observability Engine publishes dashboard-ready feeds to VIZZ.

Core dashboards:

Dashboard	Content
TrustGate Runtime Overview	Health, throughput, latency, queue depth.
Engine Performance	Per-engine latency, error rate, dependency calls.
Validation Intelligence	Rule failures, severity, domains, trends.
Trust Score Distribution	Score histograms, confidence trends, drift.
Replay Operations	Replay queue, success, drift, latency.
DAL Integrity	Anchor candidates, submissions, verification delay.
Federation Health	Exports, imports, conflicts, verification.
Quarantine Operations	Open cases, SLA breaches, release/reject ratio.
AI Feedback Operations	Feedback publication, suppression, DSAIL latency.
Audit Integrity	Audit chain status, critical event durability.

30. VIZZ Feed Example

{
  "dashboard_feed_id": "VIZZ-TG-OBS-2026-00001472",
  "feed_type": "trustgate_runtime_overview",
  "period": "PT5M",
  "summary": {
    "signals_processed": 128440,
    "avg_latency_ms": 184,
    "p95_latency_ms": 412,
    "runtime_health_score": 0.94,
    "slo_breaches": 1,
    "critical_alerts": 0,
    "dal_verification_rate": 0.9996,
    "replay_success_rate": 0.998,
    "federation_verification_rate": 0.997
  },
  "created_at": "2026-06-25T15:05:00Z"
}

31. AII Integration

Runtime observability contributes to AII dimensions.

AII Dimension	Observability Contribution
Integrity	DAL verification, audit chain integrity, replay determinism.
Transparency	Trace coverage, audit completeness, dashboard coverage.
Trust Accuracy	Trust drift, replay drift, verified vs predicted score patterns.
Verification Efficiency	Replay latency, DAL latency, audit processing efficiency.
Assurance Resilience	Runtime health, queue resilience, dependency degradation recovery.

Example AII telemetry payload:

{
  "eco_number": "ECO-A123",
  "period": "2026-Q2",
  "runtime_health_score": 0.94,
  "trace_coverage": 0.998,
  "audit_coverage": 0.999,
  "dal_verification_rate": 0.9996,
  "replay_success_rate": 0.998,
  "federation_verification_rate": 0.997,
  "slo_breach_rate": 0.0004,
  "avg_runtime_latency_ms": 184,
  "updated_at": "2026-06-25T15:05:00Z"
}

32. DSAIL Integration

DSAIL consumes observability feedback for adaptive trust learning and runtime optimization.

DSAIL may use:

latency trends;
validation failure clusters;
replay drift trends;
DAL verification delays;
federation conflict patterns;
trust score distribution shifts;
queue backlogs;
policy-resolution anomalies;
AI feedback suppression.

DSAIL may recommend increased replay sampling, trust threshold review, routing changes, policy review, capacity scaling, supplier review, or model recalibration.

DSAIL may not suppress alerts, alter observed telemetry, modify runtime health status, override SLO breaches, or change TrustGate decisions directly.

33. DAL Integration

The Observability Engine may produce DAL anchor candidates for critical observability events.

DAL anchoring is required for:

audit chain conflicts;
replay drift spikes;
DAL verification failure alerts;
federation conflict spikes;
critical SLO breaches affecting assurance;
observability tampering attempts;
regulator-requested runtime evidence;
material AI governance incidents.

Example:

{
  "artifact_type": "TrustRuntimeObservabilityEvent",
  "artifact_id": "TGOBS-2026-00001472",
  "artifact_hash": "sha256:ca77aa...",
  "engine_cmi": "vera.TG-OBSERVE.ENGINE.RUNTIME.1_0_0",
  "meid": "MEID_TRUST_MONITOR_RUNTIME",
  "event_type": "slo_breach",
  "created_at": "2026-06-25T15:05:00Z"
}

Final anchoring is performed by the TrustGate DAL Anchor Engine.

34. Audit Log Integration

The Observability Engine sends critical operational events to the TrustGate Audit Log Engine.

Audit-required observability events include:

SLO breach;
dependency critical failure;
runtime health below threshold;
trace integrity failure;
missing audit coverage;
replay drift spike;
DAL verification failure;
federation conflict spike;
telemetry ingestion failure;
observability configuration change.

Routine metrics do not need audit logging unless policy requires it.

35. Replay Behaviour

Observability events must be replayable where they affect assurance.

Replay inputs include:

raw telemetry event;
correlation context;
SLO policy;
anomaly baseline snapshot;
engine CMI;
metric definitions;
dashboard feed rules;
alert policy.

Replay verification succeeds when:

Hash(ObservabilityEvent_{original}) = Hash(ObservabilityEvent_{replay})

For time-series aggregations, replay must specify the observation window, aggregation function, and dataset snapshot.

36. Federation Behaviour

Federation observability supports cross-ECO trust operations.

Federation-safe observability may include:

federation package counts;
verification rate;
conflict rate;
import/export latency;
DAL reference verification rate;
replay reference presence;
node health summary.

Federation observability must not expose raw payloads, tenant-private traces, internal reviewer notes, sensitive error messages, private policy internals, or personal data.

Federation observability exports must use FAGF and EGFS redaction profiles.

37. Privacy and Redaction

Observability can leak sensitive operational information if unmanaged.

The engine must support redaction profiles:

Profile	Use Case
`internal_ops_full`	Internal platform operations.
`assurance_internal`	Internal assurance teams.
`auditor_detailed`	Approved auditor evidence package.
`federation_summary`	Cross-ECO health exchange.
`regulator_readonly`	Regulator runtime evidence.
`public_none`	No public observability exposure.

Sensitive fields must be redacted before export.

38. CSI Contracts

38.1. Input CSIs

CSI	Required	Purpose
`comp.TG.INPUT.RUNTIME-METRIC.v1_0`	Conditional	Runtime metric input.
`comp.TG.INPUT.RUNTIME-TRACE.v1_0`	Conditional	Runtime trace span input.
`comp.TG.INPUT.RUNTIME-LOG.v1_0`	Conditional	Structured log input.
`comp.TG.INPUT.RUNTIME-EVENT.v1_0`	Conditional	TrustGate runtime event.
`comp.TG.INPUT.HEALTH-EVENT.v1_0`	Conditional	Engine health event.
`comp.TG.INPUT.QUEUE-METRIC.v1_0`	Conditional	Queue observability metric.
`comp.FAGF.INPUT.OBSERVABILITY-POLICY.v1_0`	Yes	Observability policy and SLO definitions.
`comp.ZAR.INPUT.ENGINE-REGISTRY.v1_0`	Yes	MEID/CMI engine metadata.

38.2. Output CSIs

CSI	Purpose
`comp.TG.OUTPUT.OBSERVABILITY-EVENT.v1_0`	Canonical processed observability event.
`comp.TG.OUTPUT.RUNTIME-HEALTH.v1_0`	Runtime health summary.
`comp.TG.OUTPUT.SLO-RESULT.v1_0`	SLO evaluation result.
`comp.TG.OUTPUT.RUNTIME-ALERT.v1_0`	Runtime alert.
`comp.VIZZ.INPUT.DASHBOARD-FEED.v1_0`	VIZZ dashboard feed.
`comp.AII.INPUT.RUNTIME-TELEMETRY.v1_0`	AII runtime telemetry.
`comp.DSAIL.INPUT.OBSERVABILITY-FEEDBACK.v1_0`	DSAIL feedback signal.
`comp.DAL.INPUT.ANCHOR-CANDIDATE.v1_0`	DAL anchor candidate.

39. API Specification

39.1. Telemetry Ingest

POST /api/trustgate/MEID_TRUST_MONITOR_RUNTIME

Request:

{
  "telemetry_class": "METRIC",
  "metric_name": "tg_runtime_latency_ms",
  "value": 184,
  "engine_meid": "MEID_TRUST_ROUTE_DECISION",
  "engine_cmi": "vera.TG-DECISION.ENGINE.ROUTER.1_0_0",
  "trace_id": "TRACE-2026-00001472",
  "signal_id": "SIG-2026-00001472",
  "eco_number": "ECO-A123",
  "timestamp": "2026-06-25T15:00:01Z"
}

Response:

{
  "status": "accepted",
  "observability_event_id": "TGOBS-2026-00001472",
  "processed_async": true
}

40. Health Endpoints

The engine exposes health endpoints.

Endpoint	Purpose
`/health`	Liveness.
`/ready`	Readiness.
`/metrics`	Prometheus-compatible metrics.
`/traces`	Trace ingest endpoint where enabled.
`/status`	Dependency status.
`/slo`	SLO evaluation status.
`/version`	Engine version and CMI.

41. Storage Architecture

Recommended storage pattern:

Store	Purpose
Time-series store	Metrics and health values.
Trace store	Distributed trace spans.
Log index	Structured runtime logs.
Event store	Observability events and alerts.
Object store	Large diagnostic artifacts.
Graph store	Trace-to-USO-to-DAL linkage.
Warehouse	Long-term AII and analytics data.

Critical assurance events must be immutable and eligible for DAL anchoring.

42. Retention Model

Data Type	Default Retention
Raw metrics	90 days hot, 7 years aggregated where required.
Trace spans	30–90 days hot, sampled long-term.
Structured logs	180 days hot, policy-defined archive.
Critical alerts	7 years or reporting-cycle policy.
AII telemetry	Reporting period + retention policy.
Audit-linked observability	Same as audit retention.
DAL-anchored observability events	Ledger retention policy.

Retention is governed by FAGF and tenant policy.

43. Failure Handling

Failure	Severity	Action
Invalid telemetry schema	Warning	Reject telemetry and log.
Missing MEID/CMI	Critical for assurance telemetry	Reject or mark non-assurance-grade.
Time-series store unavailable	Warning	Buffer and retry.
Trace backend unavailable	Warning	Buffer or degrade trace capture.
Alert publisher unavailable	Critical if alert critical	Retry and escalate.
VIZZ unavailable	Warning	Queue dashboard feed.
AII unavailable	Warning	Queue telemetry.
DSAIL unavailable	Info	Queue or skip if non-critical.
DAL unavailable	Warning	Queue anchor candidate.
Health score unavailable	Critical	Emit runtime degraded event.
Audit log unavailable	Critical for critical events	Fail closed if policy requires.

The engine must not block normal TrustGate runtime for routine telemetry failures, but may trigger fail-closed behavior for audit-critical observability gaps.

44. Observability of the Observability Engine

The Observability Engine must observe itself.

Self-observed metrics include:

Metric	Description
`tg_observe_ingest_total`	Telemetry events ingested.
`tg_observe_ingest_failed_total`	Failed telemetry ingests.
`tg_observe_processing_latency_ms`	Processing latency.
`tg_observe_queue_depth`	Internal observability queue depth.
`tg_observe_alerts_emitted_total`	Alerts emitted.
`tg_observe_slo_evaluations_total`	SLO evaluations performed.
`tg_observe_aii_publish_failed_total`	AII publication failures.
`tg_observe_dsail_publish_failed_total`	DSAIL publication failures.
`tg_observe_dal_candidate_total`	DAL anchor candidates emitted.

A blind observability engine is not acceptable for Tier-0 assurance operations.

45. Performance Targets

Metric	Target
Telemetry ingest latency	< 10 ms
Metric normalization latency	< 20 ms
Trace correlation latency	< 50 ms
SLO evaluation latency	< 100 ms
Alert creation latency	< 250 ms
VIZZ feed update latency	< 5 seconds
AII telemetry lag	< 60 seconds
DSAIL feedback lag	< 5 minutes
Telemetry throughput	250,000 events/min per worker pool
Availability	99.99%
Critical alert durability	100%

46. Security Requirements

The engine must:

authenticate telemetry sources;
reject unregistered engine CMIs where policy requires;
enforce tenant isolation;
encrypt telemetry in transit and at rest;
redact sensitive fields before export;
prevent telemetry spoofing;
preserve trace integrity;
protect alert channels;
restrict dashboard access by role;
prevent cross-ECO telemetry leakage;
record MEID and CMI in all assurance-grade telemetry;
audit critical observability events;
support signed telemetry batches where required.

47. Compliance Alignment

Framework	Observability Contribution
CSRD	Supports assurance evidence for data controls and runtime reliability.
ESRS	Supports transparency, traceability, data quality, and control monitoring.
ISSB / IFRS S1-S2	Supports investor-grade control environment monitoring.
ISO 14064-1	Supports GHG assurance workflow monitoring and reproducibility.
ISO 27001	Supports logging, monitoring, incident detection, and control evidence.
EU AI Act	Supports AI monitoring, drift observation, and human oversight.
SOC2	Supports availability, processing integrity, and security evidence.

48. Developer Implementation Notes

Developers should implement the Runtime Observability Engine as an event-driven telemetry processing service.

Recommended components:

OpenTelemetry collector adapter;
metrics normalizer;
trace correlator;
structured log ingestor;
health monitor;
SLO evaluator;
anomaly detector;
alert publisher;
VIZZ dashboard feed publisher;
AII telemetry publisher;
DSAIL feedback publisher;
DAL anchor candidate publisher;
redaction engine;
retention manager;
dependency health monitor.

The engine should support both streaming and batch telemetry ingestion.

49. Deployment Model

Recommended deployment:

TrustGate Engine Pods
      │
      ├── OTel SDK
      ├── Sidecar Collector
      └── Runtime Event Publisher
              │
              ▼
Telemetry Collector
              │
              ▼
Runtime Observability Engine
              │
              ├── Metrics Store
              ├── Trace Store
              ├── Log Store
              ├── Alert Manager
              ├── VIZZ
              ├── AII
              └── DSAIL

The engine should be horizontally scalable and region-aware.

50. Testing Requirements

The engine requires:

Test Type	Requirement
Schema Tests	Validate telemetry CSIs.
Load Tests	Verify high-volume telemetry throughput.
Trace Tests	Verify distributed trace correlation.
SLO Tests	Verify SLO breach detection.
Alert Tests	Verify alert routing and deduplication.
Redaction Tests	Verify no sensitive telemetry leaks.
Replay Tests	Verify deterministic observability event replay.
Failure Tests	Verify backend outage buffering and recovery.
Security Tests	Verify telemetry source authentication.
Dashboard Tests	Verify VIZZ feed correctness.

51. Summary

The TrustGate Runtime Observability Engine is the operational intelligence and monitoring layer of the TrustGate runtime.

It transforms metrics, traces, logs, events, health signals, queue signals, and SLO evaluations into actionable runtime intelligence for VIZZ, AII, DSAIL, OARM, FAGF, auditors, and platform operations.

It is essential for operating TrustGate at production scale because it makes the assurance system observable, explainable, measurable, auditable, and resilient.

No Tier-0 TrustGate deployment should be considered production-ready without this engine or an equivalent FAGF-authorized observability mechanism.

GitHub Repo Request for Change (RFC)

0. Identity​

1. Purpose​

2. Architectural Role​

3. Canonical Identity​

4. Scope​

5. Runtime Responsibilities​

6. Observability Data Classes​

7. Observability Architecture​

8. Runtime Processing Pipeline​

9. Processing Algorithm​

10. Telemetry Identity Model​

11. Trace Correlation Model​

12. Canonical Trace Span​

13. Metrics Model​

14. Core Runtime Metrics​

15. Assurance Metrics​

16. SLO Model​

17. SLO Evaluation Formula​

18. TrustGate Runtime Health Score​

19. Score-Gaming Telemetry​

20. Queue Observability​

21. Dependency Health Model​

22. Runtime Anomaly Detection​

23. Anomaly Score​

24. Alert Model​

25. Alert Payload​

26. Canonical Telemetry Input​

27. Canonical Observability Output​

28. Health Status Event​

29. VIZZ Dashboard Feed​

30. VIZZ Feed Example​

31. AII Integration​

32. DSAIL Integration​

33. DAL Integration​

34. Audit Log Integration​

35. Replay Behaviour​

36. Federation Behaviour​

37. Privacy and Redaction​

38. CSI Contracts​

38.1. Input CSIs​

38.2. Output CSIs​

39. API Specification​

39.1. Telemetry Ingest​

40. Health Endpoints​

41. Storage Architecture​

42. Retention Model​

43. Failure Handling​

44. Observability of the Observability Engine​

45. Performance Targets​

46. Security Requirements​

47. Compliance Alignment​

48. Developer Implementation Notes​

49. Deployment Model​

50. Testing Requirements​

51. Summary​

0. Identity

1. Purpose

2. Architectural Role

3. Canonical Identity

4. Scope

5. Runtime Responsibilities

6. Observability Data Classes

7. Observability Architecture

8. Runtime Processing Pipeline

9. Processing Algorithm

10. Telemetry Identity Model

11. Trace Correlation Model

12. Canonical Trace Span

13. Metrics Model

14. Core Runtime Metrics

15. Assurance Metrics

16. SLO Model

17. SLO Evaluation Formula

18. TrustGate Runtime Health Score

19. Score-Gaming Telemetry

20. Queue Observability

21. Dependency Health Model

22. Runtime Anomaly Detection

23. Anomaly Score

24. Alert Model

25. Alert Payload

26. Canonical Telemetry Input

27. Canonical Observability Output

28. Health Status Event

29. VIZZ Dashboard Feed

30. VIZZ Feed Example

31. AII Integration

32. DSAIL Integration

33. DAL Integration

34. Audit Log Integration

35. Replay Behaviour

36. Federation Behaviour

37. Privacy and Redaction

38. CSI Contracts

38.1. Input CSIs

38.2. Output CSIs

39. API Specification

39.1. Telemetry Ingest

40. Health Endpoints

41. Storage Architecture

42. Retention Model

43. Failure Handling

44. Observability of the Observability Engine

45. Performance Targets

46. Security Requirements

47. Compliance Alignment

48. Developer Implementation Notes

49. Deployment Model

50. Testing Requirements

51. Summary