OWASP LLM Top 10: ISO27001

```python
import re
from typing import Any, Dict, List

# Example utilities that might be used for validations
def validate_input(input_text: str) -> bool:
    """Validate input to prevent prompt injection (LLM01)."""
    # Implement specific validation logic here
    # Example: reject if input matches certain patterns
    return not bool(re.match(r"unwanted_pattern", input_text))

def validate_output(output_text: str) -> bool:
    """Ensure output handling is secure (LLM02)."""
    # Example: check for sensitive information leaks
    return "sensitive_info" not in output_text

def check_data_integrity(data: Any) -> bool:
    """Check for signs of training data poisoning (LLM03)."""
    # Implement checks against known bad patterns or anomalies
    return True  # Placeholder for actual logic

def ensure_service_availability(requests: List[Any]) -> bool:
    """Prevent model denial of service by monitoring usage (LLM04)."""
    # Example: limit the rate of requests
    return len(requests) < 1000  # Placeholder limit

def audit_supply_chain(supplier_info: Dict[str, Any]) -> bool:
    """Assess supply-chain vulnerabilities (LLM05)."""
    # Example: verify supplier against a trusted list
    return supplier_info.get("name") in ["Trusted Supplier A", "Trusted Supplier B"]

def protect_sensitive_info(data: Any) -> bool:
    """Apply controls to prevent sensitive information disclosure (LLM06)."""
    # Example: redact or encrypt sensitive data
    return True  # Placeholder for actual implementation

def validate_plugin_security(plugin_info: Dict[str, Any]) -> bool:
    """Ensure plugins or extensions are secure (LLM07)."""
    # Example: check plugin against a list of approved plugins
    return plugin_info.get("name") in ["Approved Plugin A", "Approved Plugin B"]

def manage_agency(agencies: List[Any]) -> bool:
    """Mitigate risks of excessive agency in LLMs (LLM08)."""
    # Implement logic to ensure LLM operates within boundaries
    return True  # Placeholder for actual logic

def ensure_reliance_on_llm_technologies(policy: Dict[str, Any]) -> bool:
    """Address overreliance on LLM technologies (LLM09)."""
    # Example: check for compliance with internal use policies
    return policy.get("compliance") == "Yes"

def secure_model_data(model_info: Dict[str, Any]) -> bool:
    """Protect against model theft (LLM10)."""
    # Example: encrypt model data
    return True  # Placeholder for actual logic

# Placeholder for integrating the above functions in your application flow
# You should adapt and extend these functions based on your specific requirements and environment


### Recommended Automated Validation Tools

For a robust validation strategy, complement the above Python script with automated tools capable of continuous scanning and alerting based on the specific vulnerabilities and controls referenced:

- **SonarQube**: Useful for continuous inspection of code quality to detect bugs, vulnerabilities, and code smells in your Python scripts.
- **ZAP (Zed Attack Proxy)**: For identifying vulnerabilities in your web applications by simulating attacks.
- **Bandit**: A tool designed to find common security issues in Python code.
- **Black Duck**: Helps in identifying and mitigating open-source software vulnerabilities, including those that could affect supply chains (LLM05).

Integrating these tools into your development and deployment pipelines can significantly enhance your security posture by automating the detection of potential vulnerabilities and compliance issues.