TG-AI-FEEDBACK
TrustGate AI Feedback Engine
0. Identity
Depends on module:
1. Purpose
The TrustGate AI Feedback Engine publishes assurance learning signals from TrustGate into DSAIL, AIATI, AII, VIZZ, and related assurance intelligence systems.
It converts operational TrustGate outcomes into structured, governed, replay-aware, and model-traceable feedback events.
The engine ensures that TrustGate does not only score and decide on incoming signals, but continuously learns from:
- replay outcomes;
- trust drift;
- human review decisions;
- quarantine resolutions;
- DAL verification failures;
- federation conflicts;
- attestation outcomes;
- auditor findings;
- policy overrides;
- AI calibration errors.
The engine is the primary feedback bridge between deterministic TrustGate assurance logic and adaptive ZAYAZ trust intelligence.
AI outputs remain advisory. This engine does not allow AI to rewrite historical facts, alter trust decisions, or bypass governance controls.
2. Position within TrustGate Runtime
Trust Scoring
│
▼
Decision Engine
│
▼
Replay / Quarantine / Attestation / DAL
│
▼
──────────────────────────────────────────
AI Feedback Engine
──────────────────────────────────────────
│
├── DSAIL Trust Learner
├── DSAIL Assurance Advisor
├── AII Computation
├── AIATI Dashboards
├── VIZZ Assurance Views
└── FAGF Governance Review
The AI Feedback Engine usually executes after downstream assurance events have occurred, not during the critical trust decision path.
It may operate asynchronously to avoid delaying TrustGate runtime decisions.
3. Canonical Identity
| Property | Value |
|---|---|
| MEID | MEID_TRUST_ENRICH_FEEDBACK |
| CMI | vera.TG-FEEDBACK.ENGINE.LEARNER.1_0_0 |
| KIND | ENGINE |
| Owner Module | Verification & Assurance |
| Runtime Tier | Tier-0 Assurance Runtime |
| Engine Category | ENRICH |
| Engine Domain | trust |
| ZAR Registration | Required |
| Replay Support | Native |
| DAL Anchoring | Policy-based |
| Federation Aware | Yes |
The MEID identifies the stable logical feedback engine.
The CMI identifies this specific versioned implementation.
4. Runtime Responsibilities
| Responsibility | Description |
|---|---|
| Feedback Event Creation | Convert TrustGate outcomes into AI learning events. |
| Trust Drift Reporting | Publish difference between predicted and verified trust. |
| Replay Outcome Reporting | Publish replay pass/fail, drift type, and affected engine. |
| Quarantine Outcome Reporting | Publish quarantine reason, resolution, and review result. |
| Attestation Outcome Reporting | Publish attestation success, failure, federation, and verification state. |
| DAL Outcome Reporting | Publish anchor success, verification failure, and ledger latency. |
| Human Review Feedback | Publish reviewer decisions and override reasons. |
| DSAIL Publication | Send governed feedback payloads to DSAIL learning interfaces. |
| AII Telemetry Publication | Send aggregate-ready assurance telemetry to AII. |
| Governance Guardrails | Prevent unsafe, biased, or unauthorized learning signals. |
The engine must preserve strict separation between factual telemetry and AI-derived interpretation.
5. Feedback Event Types
| Event Type | Purpose |
|---|---|
TRUST_SCORE_FEEDBACK | Publishes trust score, confidence, and dimension data. |
TRUST_DRIFT_FEEDBACK | Publishes predicted vs verified trust difference. |
REPLAY_FEEDBACK | Publishes replay pass/fail, drift, and reproducibility data. |
QUARANTINE_FEEDBACK | Publishes quarantine reasons, aging, and resolution. |
REVIEW_FEEDBACK | Publishes human review and override outcomes. |
ATTESTATION_FEEDBACK | Publishes trust attestation outcomes and verification status. |
DAL_FEEDBACK | Publishes ledger anchoring and verification telemetry. |
FEDERATION_FEEDBACK | Publishes cross-ECO validation and conflict outcomes. |
ROUTING_FEEDBACK | Publishes routing effectiveness and false escalation metrics. |
MODEL_GOVERNANCE_FEEDBACK | Publishes AI model behavior, drift, and governance signals. |
6. Feedback Safety Principles
The engine enforces the following rules:
| Principle | Requirement |
|---|---|
| Fact Preservation | Factual TrustGate outcomes must not be modified. |
| AI Separation | AI recommendations must be stored separately from facts. |
| Replayability | Feedback events must reference replayable source artifacts. |
| Model Traceability | Every AI-facing payload must include model and policy context where relevant. |
| Governance Control | Only approved event types may be sent to learning systems. |
| Tenant Isolation | Feedback must not leak cross-tenant information. |
| Federation Redaction | Cross-ECO feedback must apply federation redaction profiles. |
| Auditability | All feedback publications must be logged and traceable. |
7. Runtime Processing Pipeline
Receive Assurance Event
│
▼
Classify Feedback Type
│
▼
Resolve Source Artifacts
│
▼
Apply Governance Policy
│
▼
Apply Redaction / Privacy Controls
│
▼
Create Feedback Payload
│
▼
Validate Model Eligibility
│
▼
Publish to DSAIL / AII
│
▼
Emit Feedback Publication Event
The engine may batch feedback for performance, but each event must remain individually traceable.
8. Processing Algorithm
function publish_feedback(event):
feedback_type = classify_event(event)
policy = resolve_feedback_policy(event.eco_number, feedback_type)
if not policy.feedback_allowed:
emit_feedback_suppressed_event(event, policy)
return SUPPRESSED
source_refs = resolve_source_artifacts(event)
redacted_payload = apply_privacy_controls(event, policy)
payload = build_feedback_payload(
feedback_type,
redacted_payload,
source_refs,
policy
)
validate_feedback_payload(payload)
destinations = resolve_destinations(feedback_type, policy)
publish(payload, destinations)
emit_feedback_published_event(payload)
return payload
The engine must fail closed for unauthorized feedback and fail open only for non-critical telemetry where policy permits.
9. Trust Drift Model
Trust drift is the difference between a predicted trust score and a verified trust score.
Where:
| Variable | Meaning |
|---|---|
$TD$ | Trust drift |
$T_{verified}$ | Replay-verified or auditor-verified trust |
$T_{predicted}$ | Original TrustGate or AI-assisted trust estimate |
Absolute drift may be calculated as:
High trust drift indicates that the scoring model, rules, policies, or source quality assumptions may require recalibration.
10. Replay Feedback Model
Replay feedback captures reproducibility.
Replay success rate may be calculated as:
Where:
$RI$= Replay Integrity.
Replay feedback must include:
- replay result;
- drift type;
- affected engine CMI;
- original hash;
- replay hash;
- policy context;
- auditor action if any.
11. CSI Contracts
11.1. Input CSIs
| CSI | Required | Purpose |
|---|---|---|
comp.TG.INPUT.TRUST-SCORE.v1_0 | Conditional | Trust score and confidence. |
comp.TG.INPUT.TRUST-DECISION.v1_0 | Conditional | Decision outcome. |
comp.TG.INPUT.REPLAY-RESULT.v1_0 | Conditional | Replay pass/fail and drift. |
comp.TG.INPUT.QUARANTINE-RECORD.v1_0 | Conditional | Quarantine state and resolution. |
comp.TG.INPUT.TRUST-ATTESTATION.v1_0 | Conditional | Attestation status. |
comp.DAL.INPUT.VERIFICATION-EVENT.v1_0 | Conditional | DAL anchor and proof status. |
comp.AFLE.INPUT.FEDERATION-EVENT.v1_0 | Conditional | Federation validation result. |
comp.FAGF.INPUT.FEEDBACK-POLICY.v1_0 | Yes | Governance policy controlling feedback. |
11.2. Output CSIs
| CSI | Purpose |
|---|---|
comp.DSAIL.INPUT.TRUST-FEEDBACK.v1_0 | DSAIL trust learning event. |
comp.DSAIL.INPUT.REPLAY-FEEDBACK.v1_0 | DSAIL replay learning event. |
comp.DSAIL.INPUT.ANOMALY-FEEDBACK.v1_0 | DSAIL anomaly learning event. |
comp.AII.INPUT.ASSURANCE-TELEMETRY.v1_0 | AII telemetry contribution. |
comp.TG.EVENT.FEEDBACK-PUBLISHED.v1_0 | Runtime feedback publication event. |
comp.TG.EVENT.FEEDBACK-SUPPRESSED.v1_0 | Suppressed feedback event. |
comp.TG.EVENT.TRUST-DRIFT-DETECTED.v1_0 | Trust drift event. |
12. Canonical Input Payload
{
"feedback_source_event_id": "TGRR-2026-00001472",
"event_type": "trustgate.replay.result",
"eco_number": "ECO-A123",
"signal_id": "SIG-2026-00001472",
"trust_score_original": 0.914,
"trust_score_verified": 0.902,
"replay": {
"replay_id": "TGR-2026-00001472",
"result": "PASS",
"drift": 0.012
},
"policy_context": {
"policy_bundle": "FAGF-2026.2",
"feedback_profile": "trust_learning_standard_v1"
},
"created_at": "2026-06-25T13:12:10Z"
}
13. Canonical Feedback Output
{
"feedback_id": "TGF-2026-00001472",
"feedback_type": "REPLAY_FEEDBACK",
"eco_number": "ECO-A123",
"signal_id": "SIG-2026-00001472",
"engine_cmi": "vera.TG-FEEDBACK.ENGINE.LEARNER.1_0_0",
"meid": "MEID_TRUST_ENRICH_FEEDBACK",
"trust_drift": -0.012,
"replay_result": "PASS",
"model_training_allowed": true,
"destinations": [
"DSAIL.TrustLearner",
"AII.Telemetry"
],
"source_refs": {
"score_id": "TGS-2026-00001472",
"decision_id": "TGD-2026-00001472",
"replay_id": "TGR-2026-00001472",
"dal_ref": "DAL-2026-001245"
},
"created_at": "2026-06-25T13:12:11Z"
}
14. DSAIL Trust Feedback Event
{
"event_type": "dsail.trust.feedback",
"feedback_id": "TGF-2026-00001472",
"eco_number": "ECO-A123",
"domain": "trust",
"original_trust": 0.914,
"verified_trust": 0.902,
"trust_delta": -0.012,
"replay_result": "PASS",
"policy_bundle": "FAGF-2026.2",
"model_training_allowed": true,
"timestamp": "2026-06-25T13:12:11Z"
}
15. AII Telemetry Event
{
"event_type": "aii.assurance.telemetry",
"eco_number": "ECO-A123",
"period": "2026-Q2",
"avg_trust": 0.914,
"verified_trust": 0.902,
"trust_delta": -0.012,
"replay_success": true,
"dal_verified": true,
"quarantine_required": false,
"attestation_created": true,
"updated_at": "2026-06-25T13:12:11Z"
}
16. Feedback Suppression Event
Feedback may be suppressed by policy.
{
"event_type": "trustgate.feedback.suppressed",
"feedback_source_event_id": "TGRR-2026-00001472",
"eco_number": "ECO-A123",
"reason": "tenant_feedback_disabled",
"policy_bundle": "FAGF-2026.2",
"timestamp": "2026-06-25T13:12:11Z"
}
Suppression must be logged. Silent suppression is not allowed.
17. DAL Integration
Feedback events may be anchored when required by policy.
DAL anchoring is mandatory for:
- critical replay drift;
- model governance feedback;
- auditor override feedback;
- federation conflict feedback;
- major policy override feedback.
Example anchor candidate:
{
"artifact_type": "AITrustFeedbackEvent",
"artifact_id": "TGF-2026-00001472",
"artifact_hash": "sha256:f9aa22...",
"engine_cmi": "vera.TG-FEEDBACK.ENGINE.LEARNER.1_0_0",
"meid": "MEID_TRUST_ENRICH_FEEDBACK",
"created_at": "2026-06-25T13:12:11Z"
}
18. Replay Behaviour
Feedback generation must be replayable.
Replay inputs include:
- source assurance event;
- feedback policy;
- redaction profile;
- destination resolution policy;
- source artifact references;
- engine CMI.
Replay verification succeeds when:
If destination availability changes, replay validates the generated payload, not the original network delivery side effect.
19. Federation Behaviour
Federation feedback may be exchanged only under approved federation profiles.
Federated feedback may include:
- attestation verification outcomes;
- cross-ECO replay success;
- Carbon Passport trust references;
- federation conflict signals;
- node trust decay signals.
Federated feedback must not include:
- raw sensitive payloads;
- internal reviewer notes;
- tenant-private policy internals;
- model training data unless explicitly authorized.
20. AI Governance
The engine enforces EU AI Act-aligned governance controls.
| Control | Requirement |
|---|---|
| Human Oversight | Human review outcomes are preserved separately from AI recommendations. |
| Explainability | Feedback events must include source references and rationale. |
| Data Minimization | Only required learning fields are sent to DSAIL. |
| Bias Monitoring | Feedback payloads must include segmentation metadata where permitted. |
| Model Traceability | Model CMI must be recorded when feedback relates to model behavior. |
| Replayability | Feedback creation must be reproducible. |
| Suppression Logging | Suppressed feedback must be logged. |
21. Failure Handling
| Failure | Severity | Action |
|---|---|---|
| Missing source event | Critical | Abort |
| Missing policy context | Critical | Abort |
| Feedback not allowed | Info | Suppress and log |
| DSAIL unavailable | Warning | Queue retry |
| AII unavailable | Warning | Queue retry |
| Redaction failure | Critical | Abort |
| Unauthorized feedback destination | Critical | Abort |
| DAL unavailable | Warning | Queue anchor candidate if required |
| Invalid payload schema | Critical | Abort |
| Model training not allowed | Info | Publish telemetry-only payload |
The engine must fail closed for privacy, security, and federation constraints.
22. Observability Metrics
| Metric | Description |
|---|---|
tg_feedback_published_total | Total feedback events published. |
tg_feedback_suppressed_total | Feedback events suppressed by policy. |
tg_feedback_failed_total | Failed feedback publications. |
tg_feedback_latency_ms | Feedback processing latency. |
tg_feedback_queue_depth | Pending feedback events. |
tg_feedback_trust_drift_avg | Average trust drift. |
tg_feedback_replay_drift_total | Replay drift feedback events. |
tg_feedback_dal_required_total | Feedback events requiring DAL anchoring. |
23. Performance Targets
| Metric | Target |
|---|---|
| Feedback payload creation latency | < 50 ms |
| DSAIL publication latency | < 250 ms async |
| AII publication latency | < 250 ms async |
| Throughput | 10,000 feedback events/sec per worker pool |
| Replay determinism | 100% for generated payload |
| Availability | 99.95% |
24. Security Requirements
The engine must:
- enforce feedback policy before publication;
- apply tenant isolation;
- apply federation redaction profiles;
- prevent raw sensitive data leakage;
- sign or authenticate outbound events;
- preserve source artifact references;
- record MEID and CMI in every event;
- log feedback suppression;
- separate AI recommendations from factual outcomes;
- ensure DSAIL training eligibility is explicit.
25. Compliance Alignment
| Framework | AI Feedback Engine Contribution |
|---|---|
| CSRD | Supports continuous improvement of assurance and data quality controls. |
| ESRS | Supports traceability of data quality, uncertainty, and control outcomes. |
| ISSB / IFRS S1-S2 | Supports decision-useful trust intelligence and control evidence. |
| ISO 14064-1 | Supports feedback loops from GHG verification and replay. |
| ISO 27001 | Supports governed telemetry, logging, and access controls. |
| EU AI Act | Supports AI governance, traceability, oversight, and explainability. |
26. Developer Implementation Notes
Developers should implement the AI Feedback Engine as an asynchronous, policy-controlled event publisher.
Recommended design:
- feedback classifier;
- source artifact resolver;
- feedback policy evaluator;
- redaction engine;
- payload builder;
- schema validator;
- destination resolver;
- DSAIL publisher;
- AII telemetry publisher;
- retry queue;
- suppression logger;
- DAL anchor candidate publisher.
The engine must never block critical TrustGate decisions unless policy explicitly requires synchronous feedback publication.
27. Summary
The TrustGate AI Feedback Engine closes the assurance learning loop between deterministic TrustGate operations and adaptive ZAYAZ intelligence.
It transforms runtime outcomes into governed learning signals for DSAIL, AII, AIATI, VIZZ, and FAGF review.
This engine ensures that ZAYAZ becomes progressively more accurate, more auditable, and more adaptive without allowing AI to override deterministic assurance controls or human-verifiable governance.