Real-Time Guardrails¶

Enforce safety boundaries and operational constraints to keep your AI applications within desired behavior in production. Guardrails evaluate every AI input and output against configurable thresholds — blocking, flagging, or passing content before it reaches users.

Why This Matters¶

Production failures are visible and costly. When a model generates harmful content, leaks PII, or returns irrelevant answers in front of customers, the impact is immediate — regulatory exposure, reputational damage, and loss of trust.
Safety can't be solved at design time alone. Users will find ways to misuse AI systems that pre-deployment testing cannot anticipate. Real-time guardrails provide the last line of defense.
Compliance requires ongoing protection. Regulatory frameworks expect organizations to demonstrate continuous safeguards, not just pre-deployment testing.

How It Works¶

graph TB
    REQ["User Input"]
    REQ --> INPUT_CHK
    INPUT_CHK["Input Guardrail<br>Jailbreak, HAP"]
    INPUT_CHK -->|"block"| BLOCKED1["Input rejected"]
    INPUT_CHK -->|"pass"| MODEL["AI Model"]
    MODEL --> OUTPUT_CHK["Output Guardrail<br>PII, Safety, Quality"]
    OUTPUT_CHK --> DEC{"PASS / FLAG / BLOCK"}
    DEC -->|"pass"| RESP["Response delivered"]
    DEC -->|"flag"| FLAG["Delivered + flagged"]
    DEC -->|"block"| BLOCKED2["Fallback response"]

Three-tier response handling:

PASS — Content is within acceptable limits, serve normally
FLAG — Content is borderline, serve but log for human review
BLOCK — Content violates thresholds, serve a fallback response instead

Guardrail Types¶

Built-in Content Safety¶

Detects harmful content using IBM watsonx governance pre-trained models. No additional setup beyond API credentials.

Metric	What It Detects	Threshold Type
HAP	Hate, abuse, and profanity	Upper-limit (block when exceeded)
PII	Names, emails, SSNs, phone numbers	Upper-limit
Jailbreak	Prompt injection and jailbreak attempts	Upper-limit
Social Bias	Stereotyping and discriminatory language	Upper-limit
Violence	Violent content	Upper-limit
Profanity	Profanity	Upper-limit
Harm	General harm	Upper-limit
Sexual Content	Sexual content	Upper-limit
Unethical Behavior	Unethical content	Upper-limit
Evasiveness	Evasive or non-committal responses	Upper-limit

RAG Quality Guardrails¶

Real-time quality checks on RAG pipeline responses. Blocks responses that are hallucinated or off-topic.

Metric	What It Checks	Threshold Type
Answer Relevance	Does the response address the question?	Lower-limit (block when quality drops)
Context Relevance	Are retrieved passages relevant?	Lower-limit
Faithfulness	Is the response grounded in context?	Lower-limit

Custom LLM-as-Judge Guardrails¶

Define your own guardrail criteria using an LLM evaluator. Two approaches:

Prompt template — Full control over the evaluation prompt (e.g., answer completeness)
Criteria + Options — Structured rubric with named options and scores (e.g., conciseness, helpfulness)

Uses LLMAsJudgeMetric with WxAIFoundationModel as the judge.

Available Assets¶

Script	What It Does
Content Safety Guardrails	Screen inputs/outputs for 10 safety metrics with configurable BLOCK/FLAG/PASS thresholds
RAG Quality Guardrails	Real-time faithfulness, relevance, context quality checks with fallback responses
Custom Guardrails	Define custom LLM-as-judge guardrails (completeness, conciseness, helpfulness)
Guardrail Pipeline	End-to-end: validate input → call model → validate output → audit log

All scripts use the ibm_watsonx_gov SDK with MetricsEvaluator and GenAIConfiguration.

GitHub Repository

Real-Time Guardrails Assets