Banks and FinTechs deploy AI agents for payments, lending, fraud, and KYC. AxonFlow enforces policy at runtime, gates high-risk actions through human review, and produces the audit evidence regulators expect.
Financial regulators are moving faster than most AI governance tooling can keep up with.
Banks and FinTechs building AI agents face a growing list of regulatory mandates. The RBI's Framework for Responsible and Ethical Enablement of AI (FREE-AI) requires explainability, data protection, and human oversight for automated financial decisions. SEBI Circular SEBI/HO/ITD/ITD-SED1/P/CIR/2025/016C imposes governance obligations for AI used in securities markets. The EU AI Act classifies credit-scoring and fraud-detection systems as high-risk, requiring conformity assessments and technical documentation. MAS FEAT principles demand fairness, ethics, accountability, and transparency for AI in financial services. And PCI-DSS v4.0 mandates that payment card data is never logged, transmitted, or stored without protection.
Auditors ask specific questions: Can you show whether an AI agent exposed a credit card number in a log? Can you show who approved a high-value disbursement before it was executed? Can you produce a complete trace from an LLM prompt to a downstream API call, with every policy decision recorded?
Generic API gateways and prompt-management tools do not answer these questions. They lack PII-specific detection for financial identifiers (Aadhaar, PAN, IBAN), they have no concept of human-in-the-loop approval for high-risk actions, and they cannot produce the structured audit evidence that a compliance team can hand to a regulator.
AxonFlow is a runtime AI governance platform purpose-built for this problem. It sits between your AI agents and the actions they take, enforcing policies in real time, detecting and redacting sensitive data before it reaches an LLM, gating high-risk operations through human approval, and recording every decision with the fidelity required for regulatory evidence.
Concrete agent workflows governed at runtime, from payment disbursement to trade compliance.
AI agents that initiate payments, transfers, or refunds pass through AxonFlow's HITL approval gate before execution. Configurable thresholds route high-value transactions to human reviewers while low-risk payments proceed automatically. Idempotency keys and retry context prevent duplicate disbursements during retries or network failures.
Lending agents that process loan applications handle India-specific PII: Aadhaar numbers, PAN cards, and bank account details. AxonFlow detects and redacts these identifiers before they reach an LLM, while MCP governance policies control which tools the agent can invoke and what data flows through each connector.
Fraud analysts use AI copilots to query transaction databases. AxonFlow's Luhn-validated credit card detection ensures card numbers are redacted before they appear in LLM context. SQL injection scanning on every LLM-generated query prevents prompt-injection attacks from reaching your transaction database.
KYC agents that extract and verify identity documents handle some of the most sensitive PII in banking. AxonFlow detects identity numbers, addresses, and dates of birth before they enter LLM context. The audit trail provides exportable evidence of every policy decision for compliance reviews and regulatory filings.
AI agents that monitor trading activity and flag suspicious patterns operate under strict latency and reliability requirements. AxonFlow's circuit breaker prevents cascading failures when downstream services are unavailable, while cost controls enforce per-tenant budgets on LLM usage to prevent runaway inference costs.
How AxonFlow capabilities map to specific regulatory requirements across jurisdictions.
| Requirement | Regulation | AxonFlow Capability |
|---|---|---|
| AI-generated decisions must be explainable and auditable | RBI FREE-AI | Structured audit trail with decision records, evaluated policies, and evidence export for post-incident review |
| Human oversight for automated financial decisions | RBI FREE-AI EU AI Act | HITL approval gates with configurable thresholds, timeout policies, and escalation rules |
| PII protection for Indian financial identifiers | RBI FREE-AI IT Act | PII detection with Aadhaar (12-digit + Verhoeff checksum), PAN (format + entity-type validation), and UPI ID pattern matching |
| Governance framework for AI in securities markets | SEBI 16C | Policy-as-code enforcement with per-tenant governance rules, role-based access, and configurable policy categories |
| Payment card data must not be logged or exposed | PCI-DSS v4.0 | PII detection with Luhn-validated credit card number scanning, automatic redaction before data reaches LLM context |
| Prevent injection attacks on data stores | PCI-DSS v4.0 OWASP | SQL injection scanning on every LLM-generated output before it reaches a downstream database or API |
| High-risk AI system conformity assessment | EU AI Act | Structured audit evidence export with per-execution policy decision records for conformity documentation |
| Technical documentation for AI systems | EU AI Act SEBI 16C | Execution timeline with full provenance: which policies applied, what data was redacted, and what actions were gated |
| Accountability and transparency in AI-driven financial services | MAS FEAT | Tenant-level governance policies with audit trails, policy versioning, and per-decision traceability via OpenTelemetry |
| Data protection across cross-border AI processing | GDPR RBI FREE-AI | In-VPC deployment mode for data sovereignty; PII detection and redaction before data leaves your network boundary |
Centralize policy decisions across every integration point in your AI stack.
Large banks and FinTechs do not have a single AI gateway. They have multiple: an LLM gateway that routes model calls, an agent gateway that orchestrates multi-step workflows, and an MCP gateway that governs tool and connector access. Each of these integration points needs policy enforcement, but duplicating policy logic across gateways creates drift and audit gaps.
AxonFlow's Decision Mode implements the Policy Decision Point / Policy Enforcement Point (PDP/PEP) pattern, the same architectural approach used by established policy engines like OPA, XACML, and Cedar. Your gateways act as enforcement points (PEPs), sending lightweight policy evaluation requests to AxonFlow's decision API. AxonFlow evaluates the request against your configured policies and returns an allow/deny verdict with structured metadata.
The result: one policy definition governs all three gateways. Every decision is traced via OpenTelemetry with a consistent trace ID across the entire execution path. Audit evidence is centralized regardless of which gateway enforced the policy.
Decision Mode Documentation →AxonFlow is not a compliance certification product. It provides runtime controls, audit evidence, deployment choices, and human approval paths that security, legal, and platform teams can review before AI reaches sensitive workflows.
Guides, compliance references, and tutorials for banking and financial services teams.
Start with Community to validate the fit. Move to Evaluation when you need HITL approval gates and evidence export. Talk to us when you need enterprise rollout support.
