"""
AWS Bedrock Model Fine-Tuning Security Checks Module

This module implements security validation for the model fine-tuning
pipeline, focusing on training data security and preventing data leakage
from fine-tuned models.

Priority: HIGH
Effort: 2 weeks
OWASP Coverage: LLM03 (Training Data Poisoning), LLM06 (Sensitive Info Disclosure)
MITRE ATLAS: AML.T0020 (Poison Training Data), AML.T0024 (Backdoor ML Model)

See ROADMAP.md Section 1.4 for complete implementation details.
"""

from typing import Dict, List, Optional
from ..enums import RiskLevel


class FineTuningSecurityChecks:
    """Security checks for AWS Bedrock model fine-tuning pipeline."""

    def __init__(self, checker):
        """
        Initialize fine-tuning security checks.

        Args:
            checker: Reference to main BedrockSecurityChecker instance
        """
        self.checker = checker
        self.bedrock = checker.bedrock
        self.s3 = checker.session.client('s3')
        self.macie = checker.session.client('macie2')
        self.findings = []

    def check_training_data_bucket_security(self) -> List[Dict]:
        """
        Validate S3 buckets containing training data have proper security.

        CRITICAL: Training data often contains sensitive/proprietary info
        that should be tightly controlled.

        Returns:
            List of security findings

        TODO: Implement check for:
        - List all model customization jobs via bedrock:ListModelCustomizationJobs
        - Extract training data S3 URIs
        - Check bucket encryption (must be customer-managed KMS)
        - Verify bucket policies are restrictive
        - Check Block Public Access settings
        - Verify bucket versioning enabled
        - Risk Score: 10/10 for public buckets, 8/10 for weak encryption
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.1")

    def check_training_data_pii(self) -> List[Dict]:
        """
        Scan training data for PII using Amazon Macie.

        Returns:
            List of security findings

        TODO: Implement check for:
        - Integrate with Amazon Macie
        - Create Macie classification jobs for training data buckets
        - Check for sensitive data types (PII, PHI, financial data)
        - Flag buckets with PII that lack proper controls
        - Risk Score: 9/10 for PII in training data
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.2")

    def check_model_data_replay_risk(self) -> List[Dict]:
        """
        Assess risk of fine-tuned models leaking training data.

        Returns:
            List of security findings

        TODO: Implement check for:
        - List custom fine-tuned models
        - Check training data sensitivity classification
        - Assess model size vs training data size (small models = higher risk)
        - Flag high-risk combinations
        - Recommend differential privacy if available
        - Risk Score: 8/10 for high replay risk
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.3")

    def check_vpc_isolation_for_training(self) -> List[Dict]:
        """
        Verify fine-tuning jobs run in VPC with proper isolation.

        Returns:
            List of security findings

        TODO: Implement check for:
        - List model customization jobs
        - Check VPC configuration settings
        - Verify private subnets are used
        - Check security group configurations
        - Flag jobs without VPC isolation
        - Risk Score: 7/10 for no VPC isolation
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.4")

    def check_training_job_logging(self) -> List[Dict]:
        """
        Verify training job activity is logged to CloudWatch.

        Returns:
            List of security findings

        TODO: Implement check for:
        - Check if CloudWatch logging is enabled for training jobs
        - Verify log retention policies
        - Check log encryption settings
        - Risk Score: 7/10 for missing logging
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.5")

    def check_output_model_encryption(self) -> List[Dict]:
        """
        Validate fine-tuned models use customer-managed KMS keys.

        Returns:
            List of security findings

        TODO: Implement check for:
        - List custom models via bedrock:ListCustomModels
        - Check model encryption configuration
        - Flag models using AWS-managed keys
        - Verify key policies are restrictive
        - Risk Score: 7/10 for AWS-managed keys
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.6")

    def check_training_data_access_logging(self) -> List[Dict]:
        """
        Verify S3 access logging is enabled for training data buckets.

        Returns:
            List of security findings

        TODO: Implement check for:
        - List training data buckets
        - Check S3 server access logging configuration
        - Verify log destination buckets are secure
        - Risk Score: 6/10 for missing access logging
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.7")

    def check_training_job_iam_roles(self) -> List[Dict]:
        """
        Validate IAM roles used for training jobs follow least privilege.

        Returns:
            List of security findings

        TODO: Implement check for:
        - List model customization jobs and their IAM roles
        - Analyze role policies for overly permissive actions
        - Flag wildcard permissions
        - Check for unnecessary cross-account access
        - Risk Score: 8/10 for overly permissive roles
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.8")

    def check_custom_model_tags(self) -> List[Dict]:
        """
        Validate custom models have proper tagging for governance.

        Returns:
            List of security findings

        TODO: Implement check for:
        - List all custom models and their tags
        - Check for required tags (DataClassification, TrainingSource, etc.)
        - Flag untagged models
        - Risk Score: 5/10 for missing tags
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.9")

    def check_training_data_source_validation(self) -> List[Dict]:
        """
        Verify training data sources are from trusted locations.

        Returns:
            List of security findings

        TODO: Implement check for:
        - List training data sources
        - Check for unexpected S3 bucket owners
        - Verify data sources are within organization accounts
        - Flag external/untrusted data sources
        - Risk Score: 9/10 for untrusted data sources
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.10")

    def check_model_card_documentation(self) -> List[Dict]:
        """
        Verify custom models have proper documentation (model cards).

        Returns:
            List of security findings

        TODO: Implement check for:
        - List custom models
        - Check for model description and documentation
        - Verify intended use cases are documented
        - Flag undocumented models
        - Risk Score: 4/10 for missing documentation
        """
        raise NotImplementedError("See ROADMAP.md Section 1.4.11")

    def run_all_checks(self) -> List[Dict]:
        """
        Run all fine-tuning security checks.

        Returns:
            List of all security findings
        """
        print("[CHECK] Running AWS Bedrock Fine-Tuning security checks...")

        # TODO: Uncomment as each check is implemented
        # self.findings.extend(self.check_training_data_bucket_security())
        # self.findings.extend(self.check_training_data_pii())
        # self.findings.extend(self.check_model_data_replay_risk())
        # self.findings.extend(self.check_vpc_isolation_for_training())
        # self.findings.extend(self.check_training_job_logging())
        # self.findings.extend(self.check_output_model_encryption())
        # self.findings.extend(self.check_training_data_access_logging())
        # self.findings.extend(self.check_training_job_iam_roles())
        # self.findings.extend(self.check_custom_model_tags())
        # self.findings.extend(self.check_training_data_source_validation())
        # self.findings.extend(self.check_model_card_documentation())

        print(f"[INFO] Fine-Tuning security checks: {len(self.findings)} findings")
        return self.findings
