Every AI governance platform covers EU AI Act. None cover your OCC examiner.
Generic platforms speak compliance checklist. General-purpose guardrails speak accuracy numbers. Neither speaks SR 11-7, RBI circular, or model validation committee. This page explains what that gap costs — and what closes it.
High recall is table stakes. False-positive rate is what gets you fired.
A guardrail that blocks 100% of attacks but flags 96.9% of legitimate banking queries is not a guardrail — it's an outage. The FinProof benchmark is the only evaluation that measures both, on BFSI-specific data, with a verified zero-contamination benign set.
| Model | BFSI F1 (FinProof T4) | HackaPrompt Recall | AgentHarm FPR | Latency | Deployable in BFSI? |
|---|---|---|---|---|---|
| Aval v1.5 · Zytra | 0.952 | 0.994 | 0.5% | 11.6ms | ✓ Production-safe |
| Granite Guardian 3.3 IBM | 0.813 | — | — | — | Not BFSI-evaluated |
| ShieldGemma 9B Google | 0.731 | — | — | — | Not BFSI-evaluated |
| LlamaGuard 3 Meta | 0.569 | 0.0% | 0% | ~60ms | Unusable |
| PromptGuard-86M Meta | — | 1.000 | 96.9% | 8ms | 96.9% FPR = production outage |
BFSI F1 scored on withheld Tier 4 test set (B-01–B-07) — 2,608 attack prompts + 1,345 verified BFSI benign queries. FPR without a BFSI benign set is not meaningful. HackaPrompt R and AgentHarm FPR from separate public benchmark suites. Source: finproof.ai leaderboard ↗ · July 2026.
Generic platforms govern AI. Zytra governs AI for banking regulators.
Every platform covers EU AI Act, NIST AI RMF, and ISO 42001 — the three frameworks that appear in every vendor's marketing. What separates a banking-grade platform is the frameworks that don't appear in marketing but do appear in your next examination.
| Regulatory capability | Zytra | Arthur AI / Credo AI / Holistic AI | Lakera / F5 AI Guardrails |
|---|---|---|---|
| EU AI Act (Art. 9–15, GPAI Art. 53–55) | ✓ Clause-level | ✓ Framework-level | — |
| NIST AI RMF · GenAI profile | ✓ Sub-practice citations | ✓ Function-level | — |
| ISO/IEC 42001 · DORA mapped alongside | ✓ Both simultaneously | Partial | — |
| SR 11-7 · Model Risk Management | ✓ MRM workflow engine | — | — |
| RBI 2024 AI Circular · India banking | ✓ Clause-level, exam-ready | — | — |
| SEBI · Algorithmic AI oversight | ✓ | — | — |
| DPDP Act 2023 · India data protection | ✓ Inference pipeline obligations | — | — |
| MAS TRM · Singapore | ✓ | — | — |
| OCC / FFIEC · US banking | ✓ | — | — |
| MITRE ATLAS · adversarial ML | ✓ Technique-level | Partial | Partial |
Generic platform assessment reflects publicly available product pages and documentation as of Q3 2026. Specific product capabilities may vary.
What banking actually requires
The capabilities that appear on most vendor comparison pages — logging, dashboards, policy documents — are not the capabilities your model risk committee or OCC examiner will ask about.
| Capability | Zytra | Arthur AI / Credo AI / Holistic AI | Lakera / F5 AI Guardrails |
|---|---|---|---|
| BFSI-specific threat taxonomy (B-01–B-14, D-09–D-11) | ✓ 13 categories, 36 subtypes | — | Generic harm categories |
| Production-safe FPR on BFSI benign queries | 0.5% · FinProof T4 | Not measured | Up to 96.9% |
| SR 11-7 MRM workflow engine | ✓ Risk tiering, validation tracking, committee exports | — | — |
| OSCAL export for model validation committees | ✓ Professional + Enterprise | — | — |
| Signed evidence ledger (tamper-evident) | ✓ Hash-chained, timestamped | — | — |
| Forensic replay — reconstruct any context state | ✓ KV Cache Governance | — | — |
| AI Bill of Materials (AIBOM) | ✓ AI Asset Registry | Partial | — |
| Three-lines-of-defense evidence base | ✓ All three lines, shared | — | — |
| Residual risk scoring · board heat map | ✓ Inherent × (1 − effectiveness) | Dashboard only | — |
| Open, reproducible benchmark | FinProof (HuggingFace) | Vendor-run only | Vendor-run only |
| Runs in your own tenant / on-prem / air-gap | ✓ All deployment modes | Cloud-only typical | API-only typical |
| Continuous red-team (not point-in-time) | ✓ 30 operators, runs always | Manual / on-request | — |
| Agentic security (D-09 / D-10 / D-11 categories) | ✓ Multi-agent, MCP, A2A | Partial | — |
Assessment reflects publicly available documentation as of Q3 2026. OSCAL export and signed evidence ledger available on Professional and Enterprise tiers.
Arthur AI / Credo AI / Holistic AI / Lakera
Zytra
Evaluating a specific platform?
Zytra vs Credo AI
Policy management and audit logs vs an examination-ready evidence chain. Plus: all the frameworks Credo AI doesn't cover.
Compare →Zytra vs Arthur AI
Monitoring-first vs governance-first. And why SR 11-7, RBI, and DPDP coverage is what your model risk committee actually needs.
Compare →Zytra vs Holistic AI
Risk assessment and impact scoring vs a full governance workflow with tested controls and board-ready residual scoring.
Compare →Zytra vs Solytics Partners
India-native RegTech consulting vs an India-native governance platform. The difference between content on FREE-AI compliance and automated evidence that proves it.
Compare →Zytra vs Lakera (Check Point)
Acquired by Check Point Oct 2025. Runtime security detection vs a full AI governance evidence chain for RBI, SEBI, and EU AI Act. Two different products for two different problems.
Compare →See the difference on your use case
Bring your compliance requirements, your regulator, and your toughest adversarial prompts.