AI Governance Best Practices

Overview

Organizational frameworks for responsible and ethical AI deployment. This guide covers practical implementation strategies for production AI systems.

Why It Matters

Core Implementation

python
from typing import Optional, Tuple
import logging
logger = logging.getLogger("ai_safety")
class SafetyCheck:
    """AI Governance Best Practices implementation."""
    
    def __init__(self, config: dict = None):
        self.config = config or {}
        self.enabled = self.config.get("enabled", True)
    
    def validate(self, text: str, context: dict = None) -> Tuple[bool, Optional[str]]:
        """
        Validate text for safety issues.
        Returns: (is_safe, reason_if_unsafe)
        """
        if not self.enabled:
            return True, None
        
        # Implement specific checks here
        issues = self._run_checks(text, context or {})
        
        if issues:
            logger.warning(f"Safety issue detected: {issues}")
            return False, "; ".join(issues)
        
        return True, None
    
    def _run_checks(self, text: str, context: dict) -> list[str]:
        """Run all safety checks. Override in subclasses."""
        return []
class SafetyPipeline:
    """Chain multiple safety checks together."""
    
    def __init__(self, checks: list[SafetyCheck]):
        self.checks = checks
    
    def run(self, text: str, stage: str = "input") -> Tuple[bool, list[str]]:
        all_issues = []
        for check in self.checks:
            is_safe, reason = check.validate(text)
            if not is_safe:
                all_issues.append(reason)
                # Block on first critical failure
                return False, all_issues
        return True, all_issues
Usage in LLM application
def safe_completion(user_input: str, llm_fn, pipeline: SafetyPipeline) -> str:
    # Check input
    is_safe, issues = pipeline.run(user_input, stage="input")
    if not is_safe:
        return f"Request cannot be processed: safety policy violation."
    
    # Get LLM response
    response = llm_fn(user_input)
    
    # Check output
    is_safe, issues = pipeline.run(response, stage="output")
    if not is_safe:
        return "Response filtered: content policy."
    
    return response

Monitoring

python
from datetime import datetime
class SafetyMonitor:
    def __init__(self):
        self.incidents = []
    
    def record(self, incident_type: str, details: dict):
        self.incidents.append({
            "type": incident_type,
            "timestamp": datetime.utcnow().isoformat(),
            "details": details
        })
        if incident_type == "BLOCKED":
            logger.warning(f"Safety block: {details}")
    
    def summary(self) -> dict:
        total = len(self.incidents)
        blocked = sum(1 for i in self.incidents if i["type"] == "BLOCKED")
        return {"total": total, "blocked": blocked, "rate": blocked/max(total,1)}

Testing Safety Measures

python
Always test with adversarial examples
test_cases = [
    ("Normal question", "What is machine learning?", should_pass := True),
    ("Boundary test", "How do I pick a lock?", should_pass := True),  
    # Add domain-specific tests
]
pipeline = SafetyPipeline([SafetyCheck()])
for name, text, expected in test_cases:
    is_safe, _ = pipeline.run(text)
    result = "PASS" if is_safe == expected else "FAIL"
    print(f"{result}: {name}")

Best Practices

Resources