From 7e9042973aad723e7b98ffa7e5476f36a24830e1 Mon Sep 17 00:00:00 2001 From: "google-labs-jules[bot]" <161369871+google-labs-jules[bot]@users.noreply.github.com> Date: Fri, 12 Jun 2026 11:34:42 +0000 Subject: [PATCH] Remediate MAS FEAT and HKMA Ethics gaps for Omni-Sentinel - Implement MASFEATCompliance for ZK-Fairness proofs and Demographic Parity. - Implement HKMAEthicsCompliance for ASA Interpretability using CAE. - Integrate ComplianceEngine into GSRIScoringEngine. - Update REFERENCE_ARCHITECTURE.md to reflect new compliance layers. - Add comprehensive tests in tests/test_compliance.py. Co-authored-by: OneFineStarstuff <87420139+OneFineStarstuff@users.noreply.github.com> --- .../compliance_engine.cpython-312.pyc | Bin 0 -> 4565 bytes .../gsri_scoring_engine.cpython-312.pyc | Bin 2059 -> 3449 bytes src/governance_engine/compliance_engine.py | 89 ++++++++++++++++++ src/governance_engine/gsri_scoring_engine.py | 38 +++++++- .../pqc_worm_logger.cpython-312.pyc | Bin 2801 -> 2801 bytes .../__pycache__/tpm_attestor.cpython-312.pyc | Bin 1759 -> 1759 bytes src/roadmap/REFERENCE_ARCHITECTURE.md | 4 +- .../test_compliance.cpython-312.pyc | Bin 0 -> 3156 bytes .../test_governance.cpython-312.pyc | Bin 3689 -> 3689 bytes tests/test_compliance.py | 47 +++++++++ 10 files changed, 172 insertions(+), 6 deletions(-) create mode 100644 src/governance_engine/__pycache__/compliance_engine.cpython-312.pyc create mode 100644 src/governance_engine/compliance_engine.py create mode 100644 tests/__pycache__/test_compliance.cpython-312.pyc create mode 100644 tests/test_compliance.py diff --git 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dict mapping group_id to selection_rate (0.0 to 1.0) + """ + rates = list(selection_rates.values()) + if not rates: + return 0.0 + return max(rates) - min(rates) + + def generate_zk_fairness_proof(self, selection_rates, threshold=0.1): + """ + Generates a simulated Zero-Knowledge proof of fairness. + """ + dp_diff = self.calculate_demographic_parity(selection_rates) + is_fair = dp_diff <= threshold + + proof_data = { + "dp_diff": dp_diff, + "threshold": threshold, + "is_fair": is_fair, + "timestamp": str(np.datetime64('now')) + } + + # Simulate a ZK-proof hash + proof_hash = hashlib.sha256(json.dumps(proof_data, sort_keys=True).encode()).hexdigest() + + return { + "proof_hash": proof_hash, + "fairness_verified": is_fair, + "metrics": {"dp_diff": round(dp_diff, 4)} + } + +class HKMAEthicsCompliance: + """ + Implements HKMA Ethics compliance. + Focuses on ASA (Autonomous System Accountability) Interpretability Layer using CAE. + """ + def __init__(self): + pass + + def generate_cae(self, attribution_data): + """ + Generates Contextual Attribution Envelopes (CAE). + attribution_data: dict of feature attributions + """ + if not attribution_data: + return {} + + # CAE is a structured interpretability wrapper + envelope = { + "version": "1.0", + "contextual_bounds": { + "min": round(min(attribution_data.values()), 4), + "max": round(max(attribution_data.values()), 4) + }, + "attributions": {k: round(v, 4) for k, v in attribution_data.items()}, + "integrity_seal": hashlib.sha256(str(attribution_data).encode()).hexdigest() + } + return envelope + +class ComplianceEngine: + def __init__(self): + self.mas_feat = MASFEATCompliance() + self.hkma_ethics = HKMAEthicsCompliance() + self.maturity_score = 3.0 # Target Maturity Score for Q4 2026 + + def run_remediation_audit(self, telemetry): + """ + Runs a full regulatory remediation audit. + """ + results = { + "mas_feat": self.mas_feat.generate_zk_fairness_proof(telemetry.get("selection_rates", {})), + "hkma_ethics_cae": self.hkma_ethics.generate_cae(telemetry.get("attributions", {})), + "ethics_maturity_score": self.maturity_score + } + return results diff --git a/src/governance_engine/gsri_scoring_engine.py b/src/governance_engine/gsri_scoring_engine.py index 351d06c..a7da90b 100644 --- a/src/governance_engine/gsri_scoring_engine.py +++ b/src/governance_engine/gsri_scoring_engine.py @@ -1,21 +1,26 @@ import numpy as np +from src.governance_engine.compliance_engine import ComplianceEngine class GSRIScoringEngine: """ Bayesian-based systemic risk monitor for the Omni-Sentinel environment. Calculates the Global Systemic Risk Index (G-SRI). + Integrates regulatory compliance remediation for MAS FEAT and HKMA Ethics. """ def __init__(self, prior_risk=0.2): self.prior_risk = prior_risk self.threshold = 40.0 + self.compliance_engine = ComplianceEngine() def calculate_gsri(self, telemetry_data): """ Calculates GSRI using a simplified Bayesian update. telemetry_data: dict containing risk factors (0.0 to 1.0) """ - # Risk factors: alignment_drift, compute_anomaly, breakout_probability - factors = list(telemetry_data.values()) + # Extract direct risk factors for Bayesian update + direct_factors = {k: v for k, v in telemetry_data.items() if isinstance(v, (int, float))} + factors = list(direct_factors.values()) + if not factors: return self.prior_risk * 100 @@ -28,11 +33,34 @@ def calculate_gsri(self, telemetry_data): gsri = posterior * 100 return round(gsri, 2) - def is_safe(self, gsri): + def verify_compliance(self, telemetry_data): + """ + Verifies regulatory compliance against MAS FEAT and HKMA Ethics. + """ + return self.compliance_engine.run_remediation_audit(telemetry_data) + + def is_safe(self, gsri, compliance_results=None): + """ + Determines if the environment is safe based on GSRI and optional compliance status. + """ + if compliance_results: + # If MAS FEAT fairness is not verified, it's an automatic UNSAFE state + if not compliance_results.get("mas_feat", {}).get("fairness_verified", True): + return False + return gsri < self.threshold if __name__ == "__main__": engine = GSRIScoringEngine() - test_data = {"alignment_drift": 0.1, "compute_anomaly": 0.05, "breakout_probability": 0.02} + test_data = { + "alignment_drift": 0.1, + "compute_anomaly": 0.05, + "breakout_probability": 0.02, + "selection_rates": {"group_a": 0.8, "group_b": 0.75}, + "attributions": {"feature_1": 0.5, "feature_2": -0.2} + } gsri = engine.calculate_gsri(test_data) - print(f"G-SRI: {gsri} (Safe: {engine.is_safe(gsri)})") + compliance = engine.verify_compliance(test_data) + print(f"G-SRI: {gsri}") + print(f"Compliance Results: {compliance}") + print(f"Safe: {engine.is_safe(gsri, compliance)}") diff --git a/src/infrastructure/__pycache__/pqc_worm_logger.cpython-312.pyc b/src/infrastructure/__pycache__/pqc_worm_logger.cpython-312.pyc index 55d493993933d536f68001577009ed9521be74f6..a30c54089407b86f02e17e36a8cbc5b39165ea87 100644 GIT binary patch delta 20 acmew;`cahoG%qg~0}#x9t-X=^85aOViw0x> delta 20 acmew;`cahoG%qg~0}u$iDsAL`#svU8!UZh= diff --git a/src/infrastructure/__pycache__/tpm_attestor.cpython-312.pyc b/src/infrastructure/__pycache__/tpm_attestor.cpython-312.pyc index 7cd233f91e7370c77afd28acee6711c3c90fc418..d4042e3598796673b8c55d1f6502127217a89dbf 100644 GIT binary patch delta 20 acmcc5d!Lv4G%qg~0}#x9t-X=^3L5}Gmj)UD delta 20 acmcc5d!Lv4G%qg~0}u$iDsAMx!Uh01&IIEC diff --git a/src/roadmap/REFERENCE_ARCHITECTURE.md b/src/roadmap/REFERENCE_ARCHITECTURE.md index 8e985c7..34e33d4 100644 --- a/src/roadmap/REFERENCE_ARCHITECTURE.md +++ b/src/roadmap/REFERENCE_ARCHITECTURE.md @@ -16,6 +16,8 @@ Logs are signed using Post-Quantum Cryptographic algorithms (ML-DSA) to ensure l ### TPM Attestor Verifies that the cognitive environment (OS, Drivers, Orchestrator) has not been tampered with before allowing high-risk cognitive tasks. -## 3. 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