Custom Security Rules

Custom security rules let you go beyond built-in detection. Define policies that match your exact business requirements - block specific topics, protect entity names, enforce natural-language constraints, and more.

Policy Types

PromptGuard supports seven policy types. Each policy has a type, an action (block, redact, flag, allow), and either rules (condition-based) or a system prompt (LLM-judged).

Type	How it works	When to use
`input_filter`	Evaluates rules against incoming prompts	Block injection patterns, forbidden terms
`output_filter`	Evaluates rules against LLM responses	Redact PII in output, block toxic content
`topic_filter`	LLM judge evaluates a natural-language description	Keep conversations on-topic
`llm_guard`	LLM judge evaluates a natural-language description	Custom business logic too complex for regex
`entity_blocklist`	Pattern matching on both input and output	Block specific names, terms, or identifiers
`rate_limit`	Rate-based enforcement	Throttle requests per time window
`custom`	Flexible rule-based evaluation	Anything else

Creating Policies

Policies are created and edited in the dashboard. The public API exposes a read-only listing so you can verify which policies are active for your project.

Create in the Dashboard

Navigate to app.promptguard.co → your project → Policies
Click “Create Policy”
Select the policy type
Configure rules or system prompt description
Click “Create Policy”

For example, an entity_blocklist policy named “Block Competitor Mentions” with the rule:

{
  "condition": "contains_text_any",
  "value": "Acme Corp|Globex|Initech",
  "action": "block"
}

List via API

Verify your project’s policies with the Developer API:

curl https://api.promptguard.co/api/v1/policies \
  -H "X-API-Key: $PROMPTGUARD_API_KEY"

Returns all policies attached to the project associated with your API key, including type, rules, and active status.

Rule Conditions

Rule-based policies (input_filter, output_filter, entity_blocklist, custom) use condition/value/action triples:

Condition	Description	Example Value
`contains_pii`	Matches PII entities (email, SSN, etc.)	`true`
`prompt_injection`	Matches injection patterns	`true`
`contains_text`	Exact substring match	`confidential`
`contains_text_any`	Match any of pipe-separated terms	`password\|secret\|credential`
`natural_language`	LLM-judged condition	`Request asks about pricing`

Actions

Each rule specifies what happens when the condition matches:

Action	Behavior
`block`	Reject the request entirely (HTTP 400)
`redact`	Remove or mask the matched content
`flag`	Allow the request but log the violation
`allow`	Explicitly permit (useful for allowlist rules)

Topic Filter

Topic filters use natural language to define what a conversation should be about. An LLM judge evaluates each request against your description and blocks off-topic queries. Create a topic_filter policy in the dashboard with a description like:

{
  "name": "Support Bot Scope",
  "policy_type": "topic_filter",
  "is_active": true,
  "system_prompt_details": "This bot handles Azure cloud infrastructure support only. Block questions about billing, HR, competitor products, or anything unrelated to Azure services, networking, and deployment."
}

When to use topic_filter vs. input_filter:

Use topic_filter when the boundary is semantic (“stay on topic”)
Use input_filter with contains_text rules when the boundary is lexical (“block this exact word”)

LLM Guard

LLM Guard policies define custom business rules in natural language, evaluated by an LLM judge. Use these for constraints that are too nuanced for pattern matching. Create an llm_guard policy in the dashboard with your business rule as the description:

{
  "name": "No Financial Advice",
  "policy_type": "llm_guard",
  "is_active": true,
  "system_prompt_details": "Block any response that provides specific financial advice, stock recommendations, or investment guidance. General financial education is acceptable."
}

Self-hosting the judge? llm_guard runs on a small open model — by default a non-thinking instruct model, which is the right tool for a structured flag/no-flag verdict. If you point LLM_GUARD_MODEL (or a local LLM_GUARD_BASE_URL server) at a reasoning / “thinking” model, it emits a long chain-of-thought before its answer and can run out of tokens before the verdict — the guard then fails open (stops guarding) silently. If you must use a reasoning model, raise LLM_GUARD_MAX_TOKENS well above its trace length; otherwise stick with an instruct model. Watch the llm_guard_truncated log marker to catch this, and use python -m shared.security.evals.guard_model_bakeoff to compare candidate models on accuracy, latency, and truncation rate.

Entity Blocklist

Entity blocklists protect specific names, terms, or identifiers from appearing in prompts or responses. They evaluate against both input and output. Create an entity_blocklist policy in the dashboard with your protected terms:

{
  "name": "Client Name Protection",
  "policy_type": "entity_blocklist",
  "is_active": true,
  "rules": [
    {
      "condition": "contains_text_any",
      "value": "Acuity Analytics|John Smith|Project Phoenix",
      "action": "redact"
    }
  ]
}

The contains_text_any condition accepts pipe-separated (|) terms and matches any of them. This is more efficient than creating multiple contains_text rules.

Policy Presets

PromptGuard also provides six use-case-specific presets that combine multiple built-in detectors:

Preset	Optimized For
Default	Balanced security for general AI apps
Support Bot	Strict PII and exfiltration protection
Code Assistant	Injection detection, API key/secret scanning
RAG System	Maximum security, enhanced leak prevention
Data Analysis	Strict PII, SSN/DOB detection
Creative Writing	Nuanced content filtering, higher thresholds

See Policy Presets for detailed configuration.

Feature Comparison by Tier

Feature	Free	Pro	Scale
Policy Presets	Default only	All presets	All presets
Custom Policies (rules-based)	5 policies	25 policies	Unlimited
Topic Filter	—	Yes	Yes
LLM Guard	—	Yes	Yes
Entity Blocklist	Yes	Yes	Yes
Regex Detection	70-80%	70-80%	70-80%
ML Detection	~95%	~95%	~95%

Next Steps

Policy Presets

Pre-configured security policies

Threat Detection

Built-in detection capabilities

Dashboard

Trace policy decisions and debug

API Reference

Full policy management API

Get Started

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Resources

Roadmap / Preview

Custom Security Rules

Policy Types

Creating Policies

Create in the Dashboard

List via API

Rule Conditions

Actions

Topic Filter

LLM Guard

Entity Blocklist

Policy Presets

Feature Comparison by Tier

Next Steps

Policy Presets

Threat Detection

Dashboard

API Reference

​Policy Types

​Creating Policies

​Create in the Dashboard

​List via API

​Rule Conditions

​Actions

​Topic Filter

​LLM Guard

​Entity Blocklist

​Policy Presets

​Feature Comparison by Tier

​Next Steps

Policy Presets

Threat Detection

Dashboard

API Reference

Policy Types

Creating Policies

Create in the Dashboard

List via API

Rule Conditions

Actions

Topic Filter

LLM Guard

Entity Blocklist

Policy Presets

Feature Comparison by Tier

Next Steps