How to Secure Customer-Facing AI Agents

Customer-facing AI agents — support chatbots, sales assistants, and self-service portals that interact directly with end users — are uniquely vulnerable because they process untrusted input from the public internet while having access to internal systems like CRMs, order databases, and account management tools. SafeClaw by Authensor secures customer-facing agents by gating every backend action with deny-by-default policies, ensuring that even if a customer's message contains a prompt injection, the agent cannot access data or perform actions outside its explicit permissions. Install with npx @authensor/safeclaw.

The Customer-Facing Agent Threat Model

Customer-facing agents operate at the intersection of two threat surfaces: the untrusted public input and the privileged backend:

  UNTRUSTED                    PRIVILEGED
  ┌─────────────┐             ┌──────────────────┐
  │ Customer    │  message    │  Backend Systems  │
  │ (public     │ ──────────▶ │  ├─ CRM          │
  │  internet)  │             │  ├─ Order DB      │
  │             │             │  ├─ Account Mgmt  │
  │ May contain │  SafeClaw   │  ├─ Payment API   │
  │ prompt      │  Gate sits  │  └─ Internal Docs │
  │ injection   │  HERE ──▶   │                   │
  └─────────────┘             └──────────────────┘

Attack scenarios:

1. Data harvesting — Customer asks "Show me all customer records" via injection
2. Account manipulation — Injection causes agent to modify another customer's account
3. Price/refund manipulation — Injection tricks agent into issuing unauthorized refunds
4. Internal information disclosure — Agent reveals system prompts, internal processes, or employee data
5. Reputation damage — Agent makes promises or statements outside company policy

SafeClaw Policy for Customer-Facing Agents

# safeclaw-customer-agent.yaml
version: "1.0"
agent: customer-support
rules:
  # === CRM ACCESS ===
  - action: crm_query
    scope: "current_customer"   # Only the authenticated customer
    fields:
      - "name"
      - "order_history"
      - "ticket_history"
    decision: allow
  - action: crm_query
    scope: "all_customers"
    decision: deny              # Never query all customers
  - action: crm_query
    decision: deny

# === ORDER OPERATIONS ===
  - action: order_lookup
    customer: "current"
    decision: allow
  - action: order_cancel
    customer: "current"
    order_age_hours_lt: 24      # Only cancel recent orders
    decision: allow
  - action: order_cancel
    decision: deny

# === REFUNDS ===
  - action: issue_refund
    amount_lt: 50               # Auto-refund under $50
    customer: "current"
    decision: allow
  - action: issue_refund
    decision: require_approval  # Human approval for larger refunds

# === ACCOUNT MODIFICATION ===
  - action: account_modify
    field: "email"
    decision: require_approval  # Email changes need human review
  - action: account_modify
    field: "password"
    decision: deny              # Never handle password changes
  - action: account_modify
    decision: deny

# === INTERNAL SYSTEMS ===
  - action: internal_doc_read
    decision: deny              # No access to internal docs
  - action: employee_lookup
    decision: deny              # No employee data access

# === NETWORK ===
  - action: network_request
    decision: deny              # No external API calls

# === FILE SYSTEM ===
  - action: file_read
    decision: deny
  - action: file_write
    decision: deny

Scope-Locked Data Access

The most critical control for customer-facing agents is ensuring data access is scoped to the authenticated customer. Without this, a prompt injection could instruct the agent to query another customer's records:

# BAD: No customer scoping

action: crm_query
  decision: allow   # Agent can query ANY customer

GOOD: Scoped to current authenticated customer

action: crm_query
  scope: "current_customer"
  decision: allow   # Agent can ONLY query the current customer's data

SafeClaw enforces this scope at the action gate, not in the prompt. The LLM cannot override the scope because it is evaluated deterministically by the policy engine.

Response Boundary Controls

Beyond backend action gating, customer-facing agents need response boundaries:

response_controls:
  max_response_length: 2000      # Prevent information dumping
  deny_content_patterns:
    - "system prompt"            # Don't reveal internals
    - "you are a"                # Don't reveal agent instructions
    - "internal only"            # Don't share internal info
  required_disclaimer: true      # Add disclaimer to financial advice

Rate Limiting Per Customer

Prevent abuse by rate-limiting actions per customer session:

customer_limits:
  max_queries_per_session: 50
  max_refunds_per_day: 3
  max_order_cancellations_per_day: 2
  on_limit_exceeded: escalate_to_human

Audit Trail for Compliance

Every customer interaction action is logged in SafeClaw's hash-chained audit trail, providing GDPR and CCPA compliance evidence that shows exactly what data the agent accessed and what actions it took for each customer. SafeClaw has 446 tests, is MIT-licensed, and works with both Claude and OpenAI.

Cross-References

• Customer Support Agent Recipe
• GDPR AI Agent Compliance
• Prompt Injection Defense for Agents
• Customer Service Bot Safety