Securing AI Applications: A Comprehensive 2025 Checklist

AI security evolved significantly in 2025 as more organizations deployed production AI systems. This comprehensive checklist covers the security measures every AI application should implement.

1. Input Validation and Sanitization

Prompt injection remains the top AI security risk:

import re
from typing import Optional

class PromptSanitizer:
    DANGEROUS_PATTERNS = [
        r"ignore previous instructions",
        r"disregard all prior",
        r"you are now",
        r"pretend you are",
        r"act as if",
        r"system:\s*",
    ]

    def __init__(self, max_length: int = 4000):
        self.max_length = max_length
        self.patterns = [re.compile(p, re.IGNORECASE) for p in self.DANGEROUS_PATTERNS]

    def sanitize(self, user_input: str) -> tuple[str, list[str]]:
        warnings = []

        # Length check
        if len(user_input) > self.max_length:
            user_input = user_input[:self.max_length]
            warnings.append("Input truncated to maximum length")

        # Pattern detection
        for pattern in self.patterns:
            if pattern.search(user_input):
                warnings.append(f"Potentially dangerous pattern detected")
                user_input = pattern.sub("[FILTERED]", user_input)

        return user_input, warnings

sanitizer = PromptSanitizer()
clean_input, warnings = sanitizer.sanitize(user_input)

2. Output Filtering

Prevent sensitive data leakage:

class OutputFilter:
    def __init__(self):
        self.pii_patterns = {
            "ssn": r"\b\d{3}-\d{2}-\d{4}\b",
            "credit_card": r"\b\d{4}[- ]?\d{4}[- ]?\d{4}[- ]?\d{4}\b",
            "email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
        }

    def filter_response(self, response: str) -> str:
        for pii_type, pattern in self.pii_patterns.items():
            response = re.sub(pattern, f"[{pii_type.upper()}_REDACTED]", response)
        return response

3. Rate Limiting and Quotas

from azure.appconfiguration import AzureAppConfigurationClient
import redis

class AIRateLimiter:
    def __init__(self, redis_client: redis.Redis):
        self.redis = redis_client
        self.limits = {
            "free": {"requests_per_minute": 10, "tokens_per_day": 10000},
            "pro": {"requests_per_minute": 60, "tokens_per_day": 100000},
        }

    async def check_limit(self, user_id: str, tier: str) -> bool:
        key = f"rate_limit:{user_id}"
        current = self.redis.incr(key)

        if current == 1:
            self.redis.expire(key, 60)

        return current <= self.limits[tier]["requests_per_minute"]

4. Audit Logging

import structlog
from datetime import datetime

logger = structlog.get_logger()

async def log_ai_interaction(
    user_id: str,
    prompt: str,
    response: str,
    model: str,
    tokens_used: int
):
    await logger.ainfo(
        "ai_interaction",
        user_id=user_id,
        prompt_hash=hashlib.sha256(prompt.encode()).hexdigest(),
        response_length=len(response),
        model=model,
        tokens_used=tokens_used,
        timestamp=datetime.utcnow().isoformat()
    )

Security Checklist

• Input sanitization for prompt injection
• Output filtering for PII
• Rate limiting per user/tier
• Comprehensive audit logging
• Model access controls (Azure RBAC)
• Data encryption at rest and in transit
• Regular security assessments
• Incident response procedures
• Content moderation filters
• Human review for high-risk outputs

AI security is not optional. Implement these measures before going to production, and review them quarterly as threats evolve.