Vaikora for Financial Services

When an AI agent inside a bank’s customer service workflow can read account balances, what stops it from sending that balance to the wrong customer? When a trading firm’s agent has tool access to a broker API, what prevents a prompt-injection attack from turning a stop-loss query into a position-opening order? These are not theoretical risks. They are the questions FINRA, the SEC, and the Federal Reserve’s bank examiners are starting to ask during 2026 examinations.

Vaikora is the deterministic policy enforcement layer that sits between every AI agent action and the underlying system (Stripe, Plaid, broker APIs, customer databases, payment rails) and produces audit-grade evidence of every decision. The policy engine is rule-based, not LLM-based, so the same agent call always produces the same allow-or-deny answer. Auditors can trace every action back to the rule that fired.

The financial services AI agent problem

AI agents in financial services have appeared in three waves since 2023: chatbot-style customer service, document analysis (KYC, loan applications, claims), and increasingly autonomous trading and back-office workflows. Each wave brings the same control gap: the agent can do things the customer can’t do directly, and the underlying systems trust the agent.

Specific risks examiners are flagging:

• Privilege escalation across agent chains (the customer service agent invokes a back-office agent that can move money)
• Prompt injection from customer-uploaded documents (the agent reads a PDF, the PDF contains instructions, the agent acts on those instructions)
• Cross-customer data leakage in shared agent infrastructure
• Unauditable model judgment as the basis for material decisions

Compliance frameworks Vaikora addresses

• FINRA Rule 3110 (supervision): per-action audit log of every AI-initiated trade, account change, or customer communication
• SOC 2 Type II (security and availability): complete audit trail with rule version and policy state at time of evaluation
• NY DFS 23 NYCRR 500 (cybersecurity): documented access controls and incident-response evidence
• GLBA Safeguards Rule: enforcement of customer data access restrictions at the agent action layer
• CFPB UDAAP: provable consistency in customer-facing decisions

Sample policy rules

- name: trade_size_limit_per_account
  match: { tool: broker.place_order, arg.notional: "> account.daily_trading_limit" }
  decision: deny

- name: account_transfer_requires_dual_approval_above_threshold
  match: { tool: payments.transfer, arg.amount: "> 25000" }
  decision: require_approval
  approvers: ["compliance_officer", "operations_supervisor"]

- name: customer_data_no_external_share
  match: { tool: ["email.send", "webhook.post"], payload.contains_pii: true, target.is_external: true }
  decision: deny

- name: kyc_documents_only_accessible_during_session
  match: { tool: storage.read, arg.path: "kyc/*", context.session_active: false }
  decision: deny

Resources

• Vaikora flagship product page
• Vaikora vs Zenity comparison
• OWASP LLM Top 10 mapping
• Book a demo

FAQs for Financial Services

• Does Vaikora’s audit log meet FINRA evidence requirements? Yes. Every action is logged with the agent identity, the calling user context, the full tool call, the rule that fired, the policy version, and the timestamp. Logs export to standard SIEMs (Splunk, IBM QRadar, Microsoft Sentinel) in formats FINRA examiners accept.
• Can Vaikora rules reference customer-specific limits? Yes. Rules can reference external data sources at evaluation time, including per-customer trading limits, per-account permission sets, and current regulatory state.
• What’s the latency for a policy decision on a trading agent? Sub-10ms p99 for typical rule sets. The policy engine is co-located with the agent runtime, no remote round-trip.
• Is there a starter ruleset for broker-dealers? Yes. Vaikora ships with a starter ruleset for retail brokerage that covers position limits, customer suitability checks, market-hours enforcement, and short-position approvals.