Arthur AI monitors model performance. Zytra governs AI for banking regulators.
Arthur AI's strength is production monitoring — drift detection, explainability, and performance tracking across model deployments. That covers the ML engineering view well. It does not cover the regulatory evidence trail, BFSI-specific runtime guardrails, MRM workflows, or India regulatory frameworks that a regulated bank's AI governance program requires.
Arthur AI
Zytra
Monitoring tells you what happened. Governance tells your regulator what was done.
Arthur AI is built around an observation loop — it watches model outputs and alerts when things go wrong. Zytra is built around a governance loop — it ensures that models were properly reviewed, approved, monitored, and remediated, with tamper-evident evidence of each step.
Monitoring (what Arthur does)
Track model performance in production: accuracy trends, data drift, feature distribution shifts, bias metrics, SHAP-based explanations. These signals tell you when a model is degrading. They are input to governance — they are not governance itself.
A drift alert tells an ML engineer to investigate. It does not produce the evidence an RBI examiner needs to see when reviewing your model risk management program.
Governance (what Zytra adds)
Governance wraps monitoring with the control layer: was the drift investigated? Was the investigation escalated? Was a remediation approved and tested? Was the committee notified? Are those facts documented in a tamper-evident audit trail?
Monitoring without governance produces dashboards. Governance produces the evidence chain an examiner can read, verify, and accept as proof that the bank managed its AI risk responsibly.
What the examiner asks for
When an RBI or OCC examiner reviews your AI program, the question is not "what does your monitoring show?" The question is "show me how you governed this model from deployment approval through the performance issue detected last quarter."
That question requires governance documentation — who approved what, when, on what evidence, and what they decided. Monitoring data is one input. The governance trail is the answer.
Framework coverage
Arthur AI's platform was designed for the general enterprise MLOps market. Banking-specific frameworks — SR 11-7, RBI, SEBI — are not in scope for their product. For an Indian bank, the gap is total.
| Framework | Arthur AI | Zytra |
|---|---|---|
| NIST AI RMF | Partial — MEASURE function only | ✓ All functions, sub-practice citations |
| EU AI Act | — | ✓ Clause-level, automated evidence |
| ISO/IEC 42001 | — | ✓ + simultaneous DORA mapping |
| SR 11-7 · Model Risk Management | — | ✓ MRM workflow engine, OSCAL export |
| RBI 2024 AI Circular | — | ✓ Clause-level, exam-ready |
| RBI FREE-AI Framework (26 recommendations) | — | ✓ All 26 recommendations mapped |
| SEBI Algorithmic AI Oversight | — | ✓ Clause-level |
| DPDP Act 2023 | — | ✓ Inference-pipeline level |
| MAS TRM · Singapore | — | ✓ |
| MITRE ATLAS · adversarial ML | — | ✓ Technique-level with guardrail mapping |
Assessment reflects Arthur AI's publicly available product pages and documentation as of Q3 2026.
Arthur AI monitors after inference. Aval acts before it.
Arthur's platform observes outputs after they are generated — comparing prediction distributions, tracking drift, flagging anomalies. Aval intercepts requests and responses in real time, blocking harmful outputs before they reach customers. For banks deploying GenAI in customer-facing channels, post-hoc monitoring is not a substitute for pre-hoc protection.
| Capability | Arthur AI | Zytra (Aval) |
|---|---|---|
| Operates at inference time (pre-response) | — Post-hoc observation | ✓ Inline, pre-delivery |
| Prompt injection detection | — | ✓ 22 BFSI categories |
| Financial advice violation blocking | — | ✓ Risk ID B-10 |
| PII exfiltration blocking (Aadhaar, PAN) | — | ✓ India-specific entity detection |
| AgentHarm FPR on BFSI benign queries | Not measured | 0.5% · FinProof T4 |
| Agentic threat detection (D-09/D-10/D-11) | — | ✓ MCP, A2A, multi-agent |
| Open, reproducible benchmark | — | FinProof on HuggingFace |
| Inference latency | N/A (post-hoc) | 11.6ms |
Arthur AI has no India coverage. The gap is total.
India's three active AI regulatory frameworks — RBI's 2024 AI circular and FREE-AI guidance, SEBI's algorithmic AI oversight, and DPDP Act 2023 — are not covered by Arthur AI. For an Indian regulated entity, deploying Arthur AI for governance means doing all India regulatory mapping manually, with no vendor support.
No RBI coverage
Zero mapping to RBI's 2024 AI Circular (6 obligation categories) or the August 2025 FREE-AI Framework (26 recommendations across Fairness, Reliability, Explainability, Ethics). No audit trail in the format an RBI examiner expects. No governance pack for third-party AI outsourcing.
No SEBI coverage
SEBI's algorithmic AI oversight guidance applies to brokers, intermediaries, and exchanges deploying AI in advisory, trading, and surveillance workflows. Arthur AI has no clause-level mapping to this framework.
No DPDP Act coverage
DPDP Act 2023 creates obligations at the AI inference layer — PII detection, consent enforcement, data localisation at inference time. Zytra maps these at pipeline level. Arthur AI has no India data protection coverage.
Arthur launched an agentic governance platform in December 2025. Here's what it covers — and what it doesn't.
Arthur AI's December 2025 agentic governance announcement adds multi-agent monitoring and orchestration visibility to their platform. For BFSI teams evaluating this launch, the critical question is whether it covers the BFSI-specific agentic risk framework — D-09 indirect injection, D-10 authorization boundary, D-11 multi-agent manipulation — or provides generic agentic monitoring without regulated-context depth.
Arthur's agentic platform (December 2025)
Based on public documentation, Arthur's agentic platform provides:
- Multi-agent task flow monitoring and observability
- Agent hallucination detection in agentic pipelines
- Orchestration-level logging for agentic workflows
- Performance monitoring for agent outputs
These are observability capabilities extended to agentic architectures — the same pattern as Arthur's core platform.
What BFSI agentic governance requires
BFSI-specific agentic governance goes beyond observability:
- D-09: Detection of indirect prompt injection into agent pipelines via tool results, documents, and external data — the attack vector that compromises agents in RAG and tool-use workflows
- D-10: Authorization boundary enforcement — preventing agents from acquiring capabilities beyond their declared scope
- D-11: Multi-agent manipulation detection — preventing adversarial agent-to-agent instruction injection
- Regulatory mapping of agentic controls to RBI FREE-AI, eMRM, EU AI Act, and SR 11-7
- Evidence chain for agentic authorization decisions — what a BFSI examiner requires
| Agentic governance capability | Arthur AI (Dec 2025 platform) | Zytra |
|---|---|---|
| Multi-agent observability and monitoring | ✓ | ✓ |
| Agent hallucination detection | ✓ | ✓ |
| D-09 indirect injection detection (MITRE ATLAS) | Not documented | ✓ Technique-level |
| D-10 authorization boundary enforcement | Not documented | ✓ |
| D-11 multi-agent manipulation detection | Not documented | ✓ |
| RBI FREE-AI agentic controls mapping | — | ✓ Clause-level |
| SR 11-7 agentic extension documentation | — | ✓ 6-gap framework |
| Evidence chain for agentic authorization | — | ✓ Hash-chained, exportable |
| India regulatory coverage | Zero | ✓ RBI, SEBI, DPDP, IRDAI |
Assessment reflects Arthur AI's public documentation as of Q4 2026. Arthur's agentic platform was announced December 2025; the coverage above is based on publicly documented capabilities.
Evaluating Arthur AI alternatives for banking?
Bring your regulator, your use case, and your compliance requirements. Zytra will map the governance gap — including the MRM workflows, evidence chains, and India regulatory coverage Arthur AI cannot address.