Industry

SOC 2PCI DSSSOXFINRAGDPR

AI Agent Security for Financial Services

Financial services organizations face unique AI agent security challenges driven by the extreme sensitivity of financial data, strict regulatory requirements, and the direct monetary impact of agent manipulation. AI agents in financial services process transactions, provide advisory recommendations, analyze market data, generate compliance reports, and interact with customers about their accounts. A compromised agent in this sector does not just leak data — it can move money, alter financial records, provide fraudulent advice, and create regulatory violations that carry severe penalties. The combination of high-value data, regulated operations, and direct financial system access makes financial AI deployments the highest-stakes environment for agent security. Rune provides the runtime protection and audit infrastructure that financial institutions require to deploy AI agents while maintaining regulatory compliance.

Start Free — 10K Events/MonthNo credit card required

$4.88M average cost of a financial data breach

Financial services consistently has among the highest breach costs across all industries, driven by regulatory fines, customer notification requirements, and the high value of financial data on secondary markets.

100% audit completeness for regulated operations

Rune's immutable audit logging captures every agent action with full context, providing the complete audit trail that financial regulators require — eliminating gaps that occur with application-level logging.

4.2ms average authorization latency

Transaction validation and authorization checks add minimal latency to financial operations, well within the performance requirements of real-time trading, payment processing, and advisory applications.

Key Security Risks

criticalUnauthorized Transaction Execution

Financial agents with transaction capabilities can be manipulated into executing unauthorized transfers, trades, or payment modifications. Even read-only agents can be tricked into generating and signing transaction requests that are then processed by downstream systems.

Real-world scenario: A customer-facing banking agent was designed to help users check balances and review transactions. An attacker discovered that by embedding a specific instruction in their account nickname (a user-editable field), they could manipulate the agent into initiating a wire transfer request when it read the account details. The request was formatted correctly and passed to the transaction queue before automated fraud detection flagged the anomaly.

criticalFinancial Data Exfiltration

Financial agents access account balances, transaction histories, portfolio holdings, credit scores, and payment information. Injection attacks can extract this data through manipulated responses, embedded in seemingly legitimate financial summaries, or encoded in transaction descriptions.

Real-world scenario: An investment advisory agent was asked to prepare a portfolio review. A prompt injection embedded in a market data feed caused the agent to include the client's full account numbers, holdings, and recent transaction details in a 'market context' section of the review document. The document was emailed to the client and stored in a shared folder accessible to multiple advisory team members.

highRegulatory Compliance Violations

Financial agents providing advice, processing transactions, or generating reports must comply with regulations including SEC rules, FINRA guidelines, PCI DSS, SOX, and anti-money laundering requirements. A manipulated agent can generate non-compliant advice, skip required disclosures, or process transactions that violate regulatory thresholds.

Real-world scenario: A robo-advisory agent was manipulated through a crafted user question to provide specific stock recommendations without the required risk disclosures and suitability disclaimers. The agent generated personalized buy recommendations for high-risk securities to a conservative-profile investor, violating FINRA suitability rules. The compliance violation was discovered during a quarterly audit, exposing the firm to regulatory action.

highAudit Trail Manipulation

Financial regulations require complete audit trails for all agent actions involving customer data or financial transactions. Sophisticated attacks can manipulate agents into logging misleading context, omitting critical details from audit records, or formatting actions in ways that obscure their true nature from compliance review.

Real-world scenario: An injection caused a financial reporting agent to categorize a series of transactions as 'routine account maintenance' in its audit log, when the actual operations were balance transfers that should have triggered enhanced due diligence review. The mislabeled transactions bypassed the compliance team's automated monitoring rules for two months.

How Rune Helps

Transaction Authorization Layer

Rune enforces multi-level authorization for all financial operations. Transactions above configurable thresholds require explicit human approval. Transfer recipients are validated against allowlists. All authorization decisions are immutably logged with full context for regulatory audit.

Financial Data Loss Prevention

Every agent response is scanned for financial data patterns — account numbers, routing numbers, SSNs, credit card numbers, and portfolio details. Rune enforces data classification policies, ensuring sensitive financial data is never included in unauthorized outputs or transmitted to unauthorized recipients.

Regulatory Compliance Enforcement

Rune validates that agent outputs include required disclosures, risk warnings, and suitability statements based on the conversation context and the client's profile. Non-compliant advisory responses are blocked before reaching the client, with the specific compliance gap logged for review.

Immutable Audit Infrastructure

Every agent action, tool call, policy decision, and user interaction is logged to Rune's tamper-evident audit system. Logs include full conversation context, the specific policy rules that were evaluated, and the authorization chain for every financial operation — providing the complete audit trail regulators require.

Example Security Policy

version: "1.0"
rules:
  - name: authorize-financial-transactions
    scanner: tool_call
    action: block
    severity: critical
    config:
      tool_names:
        - execute_transfer
        - submit_trade
        - process_payment
      max_amount: 5000
      require_human_approval_above: 5000
      require_recipient_allowlist: true
      description: "Enforce transaction limits and human approval for financial operations"

  - name: block-financial-data-leakage
    scanner: pii
    action: redact
    severity: critical
    scope: output
    config:
      entities:
        - account_number
        - routing_number
        - ssn
        - credit_card
        - portfolio_holdings
      description: "Prevent financial PII from appearing in agent responses"

  - name: require-advisory-disclosures
    scanner: compliance
    action: block
    severity: high
    scope: output
    config:
      require_risk_disclosure: true
      require_suitability_check: true
      require_not_financial_advice_disclaimer: true
      description: "Ensure all advisory outputs include required regulatory disclosures"

  - name: enforce-audit-logging
    scanner: audit
    action: log
    severity: high
    config:
      log_all_tool_calls: true
      log_all_data_access: true
      immutable: true
      retention_days: 2555
      description: "Maintain complete immutable audit trail for 7 years"

Policies are defined in YAML and enforced at the SDK level. Version control them alongside your agent code.

Quick Start

pip install runesec

from rune import Shield

shield = Shield(
    api_key="rune_live_xxx",
    agent_id="financial-advisor",
    policy_path="financial-policy.yaml"
)

def handle_client_request(message: str, client_id: str, client_profile: dict):
    # Scan client message for manipulation
    input_result = shield.scan_input(
        content=message,
        context={
            "client_id": client_id,
            "risk_profile": client_profile["risk_tolerance"],
            "account_type": client_profile["account_type"],
        }
    )
    if input_result.blocked:
        return "I'm unable to process that request. Connecting you with your advisor."

    # Agent generates response
    response = agent.run(message)

    # Validate financial operations
    for tool_call in response.tool_calls:
        tool_result = shield.scan_tool_call(
            tool_name=tool_call.name,
            parameters=tool_call.params,
            context={
                "client_id": client_id,
                "risk_profile": client_profile["risk_tolerance"],
                "requires_suitability": True,
            }
        )
        if tool_result.blocked:
            return f"This action requires advisor approval: {tool_result.reason}"

    # Scan output for data leakage and compliance
    output_result = shield.scan_output(
        content=response.text,
        context={
            "client_id": client_id,
            "output_type": "financial_advisory",
        }
    )
    if output_result.blocked:
        escalate_to_advisor(client_id, output_result.reason)
        return "Let me connect you with your financial advisor for this request."

    return output_result.content  # Redacted if needed

This example demonstrates financial-grade agent protection. Client messages are scanned with full profile context — enabling suitability checks that consider risk tolerance and account type. Every financial tool call is validated against transaction limits, recipient allowlists, and suitability requirements. The agent's output is scanned for financial data leakage and regulatory compliance, with non-compliant responses blocked and escalated to a human advisor. All interactions are immutably logged for regulatory audit.

Secure your financial services today

Add runtime security in under 5 minutes. Free tier includes 10,000 events per month.

Start Free — 10K Events/Month Getting Started