AI Safety Guardrails

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Human-in-the-LoopAdvanced Agent VerifierAI Safety GuardrailsTesting

Updated: 02-19-2026

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AI Voice+

Overview

AIVoice+ implements a layered guardrails system across all AI-powered features. These guardrails protect the superadmin, the company, and paying users without sacrificing speed or UX.

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Layered Guardrails Stack

1. Policy Layer (System Prompt)

• SAFETY_PREAMBLE injected into every AI conversation

• Non-negotiable rules that the model must follow

• Covers: role adherence, content restrictions, data handling

2. Capability Layer (Input Validation)

• Message length limits (10,000 characters)

• UUID format validation for workspace/run IDs

• Input type checking (string, required fields)

3. Intent Layer (Injection Scanning)

• 10 regex patterns detecting prompt injection attempts

• Safe-wrapping of detected injections (not blocking — preserves UX)

• Audit logging of all injection detections

4. Runtime Layer (Rate Limiting & Quotas)

• IP-based rate limiting: 30 messages / 15 minutes per endpoint

• Per-org daily quotas: 100 messages/day (configurable per plan)

• Fail-closed rate limiter: if the DB is down, requests are blocked (not allowed)

5. Tool Layer (MCP & Agent Sandboxing)

• Per-request context isolation in MCP server (no global state)

• Org-scoped queries in all tool handlers

• API key hashing (SHA-256) for MCP authentication

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9 Guardrail Categories

#	Category	Implementation
1	Safety	Content moderation via AI gateway (fail-closed)
2	Professional Boundaries	No medical/legal/financial advice in system prompt
3	Hallucination	Uncertainty disclosure instructions in preamble
4	Bias	Equitable treatment instructions (see BiasProtections.md)
5	Privacy	PII redaction (5 patterns), data minimization
6	Manipulation	Blocked voice phrases, injection detection
7	Refusal	Graceful decline for unsafe/out-of-scope requests
8	Autonomy	Self-restricting agent instructions ("ask for clarification")
9	Evaluation	Audit logging to ai_usage_logs for all safety events

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Agent Safety Modes

All AI agents operate in a single safety mode: Standard. This mode applies all guardrails uniformly. There is no "relaxed" or "unrestricted" mode.

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Voice-Specific Guardrails

Blocked Voice Phrases

The following phrases are flagged in voice output scanning:

• "guaranteed results"

• "trust me"

• "no risk"

• "100% safe"

• "you must"

• "i promise"

These are scanned client-side via scanBlockedVoicePhrases() in agent-one-safety.ts.

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Error Masking Policy

All edge functions return generic error messages to clients:

• Client sees: "An unexpected error occurred"

• Server logs: Full error details via console.error()

This prevents information leakage about internal architecture, database structure, or API configurations.

Affected functions: delete-organization, store-checkout, send-email, create-vapi-assistant, snapshot-org-stats, admin-ai-insights, ai-contact-reply, chat-with-data

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Logging & Audit Trail

Event	Feature Tag	Table
Content moderation block (input)	agent_one_moderation_block / convo_moderation_block / chat_data_moderation_block	ai_usage_logs
Content moderation block (output)	convo_output_moderation_block	ai_usage_logs
Injection detection	injection_detected	ai_usage_logs
Normal AI usage	agent_one_chat / convo_chat / chat_with_data	ai_usage_logs
Rate limit hits	IP + endpoint logged	auth_rate_limits

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Framework Alignment

This guardrails implementation aligns with:

• NIST AI RMF: Map → Measure → Manage → Govern cycle. Our guardrails cover the Manage function (risk mitigation controls).

• EU AI Act: Our AI systems would be classified as "limited risk" (chatbots). We implement transparency (safety notices) and human oversight (content moderation).

• ISO/IEC 42001: Our safety pipeline documents align with the AI management system requirements.

These are reference alignments, not certifications. External auditing is recommended for formal compliance.

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What We Intentionally Do NOT Implement

Item	Reason
CORS restriction	Would break MCP clients and external integrations
Differential privacy	Academic technique, not applicable to chat-based SaaS
Model retraining	We use third-party models; we mitigate via prompts
Real-time bias scoring	Would add latency; we use post-hoc audit logging instead

Content Moderation (how it's done on our platform)

Content Moderation