Clear examiner reviews faster with evidence produced as agents run, and roll governed AI out across teams.
So you can hand agents real work, and real money, without losing control.
Let small transfers to approved payees proceed, block unapproved bank-detail changes, and hold unusual payments for a person. Set AI usage budgets alongside action limits. Ten deterministic fraud and AML controls ship in the early-access add-on. Watch the fraud demo.
Governed decisions record who acted, which rule applied and who approved. Reports for RBI and SEBI, MAS FEAT, OJK / UU PDP and the EU AI Act draw from the same evidence.
Reuse mandates, approvals and decision evidence across connected employee AI tools and banking workflows. BukuWarung reached organisation-wide production in 21 days and used spend by team and task to choose premium seats. Read its story.
Financial regulators are moving faster than most AI governance tooling can keep up with.
Banks and FinTechs building AI agents face a growing set of governance expectations. In the United States there is no single AI statute; instead, examiners ask standing questions in routine examinations: where is AI in use, can each decision be reconstructed, can a system be shut down, what data can it reach, who oversees it. The revised interagency model risk guidance (Federal Reserve SR 26-2 and OCC Bulletin 2026-13, superseding SR 11-7) narrows the formal model definition and excludes generative and agentic AI from its scope while the agencies indicate the underlying risk-management principles still apply; the 2023 interagency third-party risk guidance frames how banks govern AI vendors, the GLBA Safeguards Rule requires monitoring and logging of activity on systems holding customer information, and the FFIEC BSA/AML examination manual sets expectations for AI-assisted monitoring. Farm Credit System institutions answer to the Farm Credit Administration, whose examination manual retains SR 11-7 grade model risk discipline and whose cyber rule applies to AI-based technologies.
Elsewhere, the RBI FREE-AI committee report recommends explainability, data protection, and human oversight, while later RBI instruments determine which recommendations become binding. SEBI Regulation 16C makes regulated intermediaries responsible for client-data safeguards, AI outputs, and compliance even when third-party tools are used. The EU AI Act treats certain creditworthiness and other Annex III uses as high-risk. MAS FEAT frames fairness, ethics, accountability, and transparency for financial services, while PCI DSS limits storage and requires protection of cardholder data.
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 (SSN, 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 built for this problem. For every model and tool call that passes through it, AxonFlow checks the rules in real time, detects and redacts financial identifiers, holds high-risk operations for human approval, and records why each decision was made.
Concrete agent workflows governed at runtime, from payment disbursement to trade compliance.
Payment, transfer, or refund actions routed through AxonFlow can use HITL approval gates before execution. Configurable thresholds route selected high-value transactions to human reviewers. WCP preserves idempotency keys and retry context; the downstream payment service must enforce the final idempotent write.
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 the model, and controls which tools and data the agent may use.
Fraud analysts use AI copilots to query transaction databases. AxonFlow finds card numbers with Luhn validation and redacts them before they reach the model. SQL injection scanning catches known attack patterns in queries and connector responses, alongside your existing database permissions and parameterized queries.
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 reach the model. Decision records can be exported alongside your wider compliance and filing evidence.
AI agents that monitor trading activity and flag suspicious patterns operate under strict latency and reliability requirements. AxonFlow's circuit breaker stops further requests once error or policy thresholds trip, and cost controls enforce LLM budgets per tenant.
How AxonFlow capabilities map to specific regulatory requirements across jurisdictions.
| Requirement | Regulation | AxonFlow Capability |
|---|---|---|
| Model risk discipline applied to AI systems: inventory, validation support, ongoing monitoring, board reporting | SR 26-2 OCC 2026-13 FCA EM-31.1 | Per-decision audit records, human touchpoints, and monitoring evidence that a model risk or AI governance team can fold into its own MRM documentation; AxonFlow does not itself perform model validation. SR 26-2's formal model definition excludes generative and agentic AI while its risk principles still apply; Farm Credit institutions are examined under FCA EM-31.1, which retains SR 11-7 grade discipline |
| Examiners ask standing AI questions: where AI is used, per-decision reconstruction, shutdown capability, data boundaries, human oversight | US Federal banking agencies | Structured audit trail with decision records and evaluated policies, HITL approval history with approver identity, circuit-breaker halt, and policy-scoped data access on governed paths |
| Risk management across the life cycle of third-party AI relationships | Interagency TPRM (2023) | LLM provider inventory and routing controls, per-provider usage and decision records, and self-hosted deployment inside the institution's own environment support due diligence and ongoing monitoring of AI vendors |
| Monitor and log the activity of users and systems that hold customer information | GLBA Safeguards FFIEC | Every model and tool call that passes through AxonFlow is recorded with the person who made it; PII detection and redaction reduce exposure of customer information before model or connector access |
| Governance of AI-assisted financial crime monitoring, with supporting records producible for review | BSA/AML FinCEN FFIEC | The Fraud & Risk Add-on's deterministic rules are authored from public FFIEC examination guidance, FinCEN advisories, OFAC sanctions programs, and 31 CFR 1010.311 thresholds, and detections land in the same audit surface compliance teams already review |
| Support for specific adverse-action reasons where AI touches credit decisions | ECOA / Reg B | Per-decision records identify which policies were evaluated and what was gated or redacted, supporting the institution's own adverse-action reason process; AxonFlow does not generate adverse-action notices or perform fair-lending testing |
| Map AI governance evidence to the voluntary frameworks US examiners reference | NIST AI RMF Treasury FS AI RMF | Policy enforcement, HITL approvals, and audit evidence align with the Govern, Map, Measure, and Manage functions and with evidence-of-implementation expectations; final mapping remains with the institution's risk team |
| 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 |
| Cardholder data must be minimized and protected | PCI-DSS v4.0 | Luhn-validated card-number detection and configurable redaction reduce exposure before model or connector access; the complete PCI DSS control environment remains the institution's responsibility |
| Prevent injection attacks on data stores | PCI-DSS v4.0 OWASP | Configurable SQL injection detection on governed input and MCP connector response paths, used alongside database permissions and parameterized queries |
| 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 for governed stages: which policies applied, what supported data categories were redacted, and which 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 | PII detection and redaction before model or connector access; customer-controlled deployment supports the institution's approved network and data-transfer boundaries |
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.
