ArthaShield ā AI Compliance Co-Pilot
Detect AI incidents, map to ISO/IEC 42001 controls, and provide audit-ready remediation guidance for BFSI.
ISO 42001 mapped
RBI CMS aware
EU AI Act alerts
Triggers
š Hello ā Iām your AI Compliance Co-Pilot. Choose a trigger to simulate an incident and I'll map it to ISO 42001 controls, recommend actions, and prepare an audit-ready summary.
Quick Recommendation
When AI misclassifications or bias incidents occur, prioritize containment, RCA, reporting, and model remediation with audit trails.
Severity
High
AI Compliance Issues (ISO/IEC 42001) ā Challenges & ArthaShield Responses
| Issue | Practical Challenge | How ArthaShield Helps | Action / KPI for CDO |
|---|---|---|---|
| Model drift impacting complaint routing | Undetected performance decay ā missed regulatory escalations, reputational risk | Continuous monitoring, auto-drift alerts, automated rollback & canary retrain with timestamped audit | KPI: drift_detected_rate, SLA: retrain decision within 48h |
| Explainability gaps for complex ensembles | Regulators demand rationale; black-box models can't produce evidence | On-demand explainers (SHAP/LIME proxies), counterfactuals, human-readable incident summary mapped to ISO clauses | KPI: % incidents with attached explainability artifacts (target 95%) |
| Training data lineage & provenance failures | Inability to prove training sets ā failed audits and fines | Immutable dataset registry, hashed artifacts, versioned datasets and access logs for every model build | Action: enforce dataset sign-off; KPI: dataset provenance coverage 100% |
| Bias discovered post-deployment | Late discovery ā customer harm + regulatory escalation | Bias scanners + synthetic probing, targeted retraining pipelines, consumer-facing remediation templates | SLA: remediation plan within 7 days; KPI: bias incident closure rate |