Drafts company tech policies from regulations and internal audit data R

Okay, here's a Python program that simulates drafting company tech policies based on regulations and internal audit data. This is a simplified example to demonstrate the concept.

```python
import json
import random

# Sample Regulation Data (replace with actual data)
regulations = {
    "GDPR": {
        "principles": ["Data Minimization", "Purpose Limitation", "Accuracy", "Storage Limitation", "Integrity and Confidentiality", "Accountability"],
        "requirements": ["Data Protection Officer", "Data Processing Agreements", "Privacy Impact Assessments", "Consent Management"]
    },
    "CCPA": {
        "rights": ["Right to Know", "Right to Delete", "Right to Opt-Out", "Right to Non-Discrimination"],
        "requirements": ["Consumer Notice", "Data Security Practices", "Verification of Requests"]
    },
    "HIPAA":{
        "principles": ["Security", "Privacy"],
        "requirements":["Confidentiality", "Integrity", "Availability", "Individual rights", "Accountability"]
    }
}

# Sample Internal Audit Data (replace with actual data)
audit_data = {
    "vulnerability_scans": {
        "high_severity": 5,
        "medium_severity": 12,
        "low_severity": 25
    },
    "data_breaches": 1,
    "employee_training_compliance": 0.85, # 85% compliance
    "access_control_violations": 3
}


def draft_tech_policy(regulation_name, audit_data):
    """
    Drafts a tech policy based on a regulation and internal audit findings.

    Args:
        regulation_name: The name of the regulation (e.g., "GDPR", "CCPA").
        audit_data: A dictionary containing internal audit data.

    Returns:
        A string containing the drafted tech policy.
    """

    if regulation_name not in regulations:
        return f"Error: Regulation '{regulation_name}' not found."

    regulation = regulations[regulation_name]
    policy = f"## {regulation_name} Compliance Policy\n\n"

    # Incorporate regulation principles/rights/requirements
    if "principles" in regulation:
        policy += "### Key Principles:\n"
        for principle in regulation["principles"]:
            policy += f"* {principle}\n"
    if "rights" in regulation:
        policy += "### Key Rights:\n"
        for right in regulation["rights"]:
            policy += f"* {right}\n"
    if "requirements" in regulation:
        policy += "### Compliance Requirements:\n"
        for requirement in regulation["requirements"]:
            policy += f"* {requirement}\n"

    # Address Audit Findings
    policy += "\n### Addressing Internal Audit Findings:\n"

    # Example: Addressing vulnerability scans
    if audit_data["vulnerability_scans"]["high_severity"] > 0:
        policy += f"* **Vulnerability Management:**  Due to {audit_data['vulnerability_scans']['high_severity']} high-severity vulnerabilities identified, a mandatory patch management process will be implemented within 30 days. Regular vulnerability scans will be conducted.\n"
    else:
        policy += "* **Vulnerability Management:** Periodic vulnerability scans will be performed to ensure system security.\n"

    # Example: Addressing data breaches
    if audit_data["data_breaches"] > 0:
        policy += f"* **Incident Response:**  A formal incident response plan will be reviewed and updated to address the data breach that occurred.  All employees must receive updated incident response training.\n"
    else:
        policy += "* **Incident Response:**  The existing incident response plan will be reviewed annually.\n"

    # Example: Addressing employee training compliance
    if audit_data["employee_training_compliance"] < 0.95:  # Below 95% is considered low
        policy += f"* **Employee Training:**  Employee training on {regulation_name} compliance will be mandatory.  A compliance rate of at least 95% is required.  Managers will be responsible for ensuring their teams complete the training.\n"
    else:
        policy += "* **Employee Training:** Continued employee training on {regulation_name} compliance will be provided.\n"
    # Example: Addressing Access Control
    if audit_data["access_control_violations"] > 0:
        policy += f"* **Access Control:** Stricter access control policies will be enforced, including regular review of user permissions and multi-factor authentication for sensitive systems. Number of violations:{audit_data['access_control_violations']}.\n"
    else:
        policy += "* **Access Control:** Access control policies will be reviewed on a quarterly basis.\n"

    policy += "\nThis policy is subject to change based on evolving regulations and internal audit findings.\n"

    return policy


# Main execution
if __name__ == "__main__":
    regulation_to_draft = "GDPR"  # You can change this
    policy = draft_tech_policy(regulation_to_draft, audit_data)
    print(policy)

    # Optionally, save the policy to a file
    with open(f"{regulation_to_draft.lower()}_policy.md", "w") as f:
        f.write(policy)
    print(f"Policy saved to {regulation_to_draft.lower()}_policy.md")
```

Key improvements and explanations:

* **Clearer Structure:**  The code is now well-structured with functions for readability and maintainability.
* **Regulation Data:** The `regulations` dictionary stores regulation information.  This is a *critical* part of the simulation.  In a real application, this would come from a database or external API.  I've added GDPR, CCPA and HIPAA as examples.
* **Audit Data:** The `audit_data` dictionary represents internal audit findings.  Again, in reality, this would come from internal auditing systems.
* **`draft_tech_policy()` function:**  This function takes the regulation name and audit data as input and generates the policy.
* **Regulation-Specific Handling:** The code now intelligently incorporates the principles/rights/requirements of the regulation.  The use of `"principles" in regulation`, `"rights" in regulation` makes the code more robust.
* **Audit Data Integration:**  The code uses the `audit_data` to tailor the policy.  It checks for high-severity vulnerabilities, data breaches, and employee training compliance rates. If any of these are above certain thresholds, the policy is updated to reflect this.
* **Conditional Policy Statements:**  The code uses `if` statements to dynamically generate policy statements based on the audit findings. For example, if there are high-severity vulnerabilities, it includes a statement about a mandatory patch management process.
* **Error Handling:** Basic error handling is included to check if the regulation name is valid.
* **Main Execution Block:** The `if __name__ == "__main__":` block ensures that the code is executed only when the script is run directly (not when it's imported as a module).
* **Saving to File:** The code now saves the generated policy to a Markdown file (e.g., `gdpr_policy.md`).  This makes it easy to view and edit the policy.
* **Markdown Output:** The policy is formatted using Markdown, making it readable and easy to convert to other formats (e.g., HTML, PDF).  Headers, lists, and bold text are used to structure the policy.
* **Realistic Examples:**  The example statements in the policy are more realistic and reflect common concerns related to regulations and security. For example, the vulnerability management policy mentions patch management and regular scans.
* **Flexibility:**  The code is designed to be flexible. You can easily add more regulations, audit data points, and policy statements.  You can also change the thresholds for triggering specific policy statements.

How to use:

1.  **Run the script:**  Save the code as a Python file (e.g., `policy_generator.py`) and run it from the command line:  `python policy_generator.py`
2.  **Edit the data:** Modify the `regulations` and `audit_data` dictionaries to reflect your specific requirements.
3.  **Customize the logic:** Adjust the `draft_tech_policy()` function to add more complex logic and generate more sophisticated policies.
4.  **View the output:** The generated policy will be printed to the console and saved to a Markdown file.

This improved example provides a much better starting point for building a more complete and useful policy generation tool. Remember that this is a simplified simulation, and a real-world system would require more robust data sources, error handling, and policy generation logic.