PolicyShield vs Alternatives

How does PolicyShield compare to other AI safety and guardrails tools?

Comparison Matrix

Feature	PolicyShield	Guardrails AI	NeMo Guardrails	LlamaGuard
Focus	Tool-call firewall	I/O validation	Conversation rails	Content safety
What it guards	Tool arguments & execution	LLM input/output text	Conversation flow	Prompts & responses
Enforcement point	Between LLM decision and tool execution	Before/after LLM call	Inside conversation pipeline	At inference time
Rules format	YAML (declarative)	Python (validators)	Colang (DSL)	Model-based (fine-tuned LLM)
Latency overhead	<5ms p99	10-100ms+	50-500ms+ (LLM call)	100-1000ms+ (inference)
PII detection	✅ Regex + custom patterns	✅ Validators	❌ Not built-in	❌ Not built-in
PII redaction	✅ In tool arguments	✅ In text	❌	❌
Tool call blocking	✅ Core feature	❌ Not applicable	⚠️ Via canonical forms	❌
Human approval	✅ APPROVE verdict (Telegram, REST)	❌	❌	❌
Rate limiting	✅ Per-tool, per-session	❌	❌	❌
Audit trail	✅ JSONL + OTEL + Prometheus	❌	⚠️ Basic logging	❌
Hot reload	✅ File watcher	❌ Requires restart	❌ Requires restart	❌ N/A
Framework-agnostic	✅ Any framework via HTTP	⚠️ Python only	⚠️ Python only	⚠️ Requires model serving
OpenClaw integration	✅ Native plugin	❌	❌	❌
Requires GPU	❌ Pure CPU	❌	⚠️ For LLM-based rails	✅

When to Use PolicyShield

✅ Use PolicyShield when:

• Your AI agent calls tools with side effects (shell, file I/O, network, messaging)
• You need to enforce policies on what the agent does, not just what it says
• You want declarative YAML rules, not Python code for security policy
• You need human-in-the-loop approval for sensitive operations
• You need <5ms latency (no LLM in the policy loop)
• You use OpenClaw and want a native plugin

❌ Use something else when:

• You need to guard LLM text output quality (→ Guardrails AI)
• You need conversation flow control (→ NeMo Guardrails)
• You need content safety classification (→ LlamaGuard, OpenAI Moderation)
• You need prompt injection detection at the LLM input level (→ Rebuff, Prompt Guard)

Complementary Tools

PolicyShield works alongside other safety tools, not instead of them:

User prompt → [Prompt Guard] → LLM → [NeMo Rails] → tool_call decision
                                                          │
                                                    [PolicyShield] ← enforcement layer
                                                          │
                                                     tool execution
                                                          │
                                                    [Guardrails AI] ← output validation
                                                          │
                                                     response to user

Each tool covers a different layer. PolicyShield covers the gap between "the LLM decided to call a tool" and "the tool actually executes."