# Wilma Security Features - Implementation Status

**Last Updated:** 2025-11-05
**Current Version:** 1.1.0

This document tracks the implementation status of all security features from GITHUB_ISSUES.md.

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## Summary

| Module | Total Checks | Completed | In Progress | TODO | Completion % |
|--------|--------------|-----------|-------------|------|--------------|
| **Knowledge Bases** | 12 | 8 | 1 | 3 | 67% |
| **Guardrails** | 11 | 4 | 0 | 7 | 36% |
| **Agents** | 10 | 0 | 0 | 10 | 0% |
| **Fine-Tuning** | 11 | 0 | 0 | 11 | 0% |
| **TOTAL** | 44 | 12 | 1 | 31 | 27% |

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## Issue #2: AWS Bedrock Knowledge Bases Security Module

**Priority:** CRITICAL
**Overall Status:** 67% Complete (8/12)
**Implementation File:** `src/wilma/checks/knowledge_bases.py`

### Completed Checks ✅

1. **S3 Bucket Encryption Verification** - Risk 8/10
   - Status: ✅ COMPLETED
   - Implementation: `check_data_source_encryption()` lines 270-323
   - Features:
     - Validates S3 bucket encryption using `check_s3_bucket_encryption()`
     - Checks for customer-managed KMS keys
     - Flags unencrypted buckets as HIGH risk
     - Flags AWS-managed keys as MEDIUM risk
   - Reference: IMPROVEMENTS.md Phase 3.2

2. **Vector Store Encryption** - Risk 8/10
   - Status: ✅ COMPLETED
   - Implementation: `check_vector_store_encryption()` lines 324-480
   - Features:
     - OpenSearch Serverless encryption validation
     - Aurora/RDS encryption validation
     - Pinecone encryption validation
     - Checks for customer-managed KMS keys
   - Reference: IMPROVEMENTS.md Phase 3.2

3. **Vector Store Access Control Validation** - Risk 9/10
   - Status: ✅ COMPLETED
   - Implementation: `check_opensearch_network_access()` lines 597-723
   - Features:
     - OpenSearch network policy validation
     - VPC endpoint configuration check
     - Public access detection
   - Reference: IMPROVEMENTS.md Phase 4.6

4. **OpenSearch Data Access Policies** - Risk 9/10
   - Status: ✅ COMPLETED (NEW - Just Added)
   - Implementation: `check_opensearch_data_access_policies()` lines 724-815
   - Features:
     - Data access policy validation
     - Wildcard principal detection
     - Overly permissive permission detection
   - Reference: IMPROVEMENTS.md Phase 4.6

5. **PII Detection in Metadata** - Risk 9/10
   - Status: ✅ PARTIAL (Metadata only)
   - Implementation: `check_pii_exposure()` lines 936-1048
   - Features:
     - Scans S3 bucket names for PII patterns
     - Scans prefixes and tags for PII
     - **Limitation:** Does NOT scan actual document content
     - Includes warning about document-level scanning limitation
   - Reference: IMPROVEMENTS.md Phase 3.4
   - Note: Full implementation would require Amazon Macie integration

6. **Chunking Configuration Validation** - Risk 6/10
   - Status: ✅ COMPLETED
   - Implementation: `check_chunking_configuration()` lines 1049-1116
   - Features:
     - Validates chunk size against thresholds
     - Validates overlap percentage
     - Configurable via WilmaConfig
   - Reference: IMPROVEMENTS.md Phase 3.3

7. **CloudWatch Logging Validation** - Risk 7/10
   - Status: ✅ COMPLETED
   - Implementation: `check_kb_logging()` lines 1117-1195
   - Features:
     - Validates Knowledge Base CloudWatch logging
     - Checks log encryption with customer KMS keys
     - Verifies log retention policies
   - Reference: IMPROVEMENTS.md Phase 3.2

8. **Tagging Compliance Check** - Risk 5/10
   - Status: ✅ COMPLETED
   - Implementation: `src/wilma/checks/tagging.py`
   - Features:
     - Validates required tags from config
     - Checks Knowledge Base tags
     - Configurable tag requirements via WilmaConfig
   - Reference: IMPROVEMENTS.md Phase 4.3

### In Progress 🔄

9. **Indirect Prompt Injection in Documents** - Risk 8/10
   - Status: 🔄 PARTIAL (Pattern detection ready, not integrated)
   - Available Utility: `scan_text_for_prompt_injection()` in utils.py
   - Missing: Document content scanning (would require S3 object access)
   - Blocker: Performance concerns with scanning large document sets
   - Recommended Approach: Lambda function for async scanning

### TODO ⏳

10. **S3 Bucket Public Access Validation** - Risk 10/10
    - Status: ⏳ TODO
    - Available Utility: `check_s3_bucket_public_access()` exists in utils.py
    - Missing: Integration into knowledge_bases.py check
    - Estimated Effort: 1-2 hours

11. **S3 Versioning Enabled Check** - Risk 7/10
    - Status: ⏳ TODO
    - Missing: S3 versioning validation logic
    - Estimated Effort: 2-3 hours

12. **Embedding Model Access Control** - Risk 6/10
    - Status: ⏳ TODO
    - Missing: IAM policy analysis for embedding model permissions
    - Estimated Effort: 3-4 hours

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## Issue #3: Advanced Guardrails Validation Module

**Priority:** CRITICAL
**Overall Status:** 36% Complete (4/11)
**Implementation File:** `src/wilma/checks/genai.py`

### Completed Checks ✅

1. **Guardrail Strength Configuration** - Risk 8/10
   - Status: ✅ COMPLETED (CRITICAL FIX)
   - Implementation: `check_prompt_injection_vulnerabilities()` lines 76-188
   - Features:
     - Deep validation of guardrail configuration
     - Checks for PROMPT_ATTACK filter presence
     - Validates input/output strength (HIGH, MEDIUM, LOW)
     - Flags weak configurations (LOW/MEDIUM) as MEDIUM risk
     - Flags missing filters as HIGH risk
   - Reference: IMPROVEMENTS.md Phase 2.3

2. **Content Filter Coverage** - Risk 9/10
   - Status: ✅ COMPLETED
   - Implementation: Same as above
   - Features:
     - Validates PROMPT_ATTACK filter type
     - Checks filter configuration depth
     - Ensures filters are actually enabled
   - Reference: IMPROVEMENTS.md Phase 2.3

3. **Guardrail Existence Check** - Risk 9/10
   - Status: ✅ COMPLETED
   - Implementation: `check_prompt_injection_vulnerabilities()` lines 65-75
   - Features:
     - Lists all foundation models
     - Checks if ANY guardrails exist in account
     - Flags no guardrails as HIGH risk
   - Reference: IMPROVEMENTS.md Phase 2.3

4. **Tagging Compliance** - Risk 4/10
   - Status: ✅ COMPLETED
   - Implementation: `src/wilma/checks/tagging.py`
   - Features:
     - Validates guardrail tags
     - Configurable via WilmaConfig
   - Reference: IMPROVEMENTS.md Phase 4.3

### TODO ⏳

5. **Automated Reasoning Enabled Check (2025 Feature)** - Risk 7/10
   - Status: ⏳ TODO
   - Blocker: Requires AWS Bedrock Automated Reasoning API support
   - Estimated Effort: 1 week (pending AWS API availability)

6. **PII Filters Configuration** - Risk 8/10
   - Status: ⏳ TODO
   - Missing: Dedicated PII filter validation
   - Estimated Effort: 3-4 hours

7. **Topic Filters Configuration** - Risk 6/10
   - Status: ⏳ TODO
   - Missing: Topic filter validation logic
   - Estimated Effort: 3-4 hours

8. **Word Filters (Managed/Custom)** - Risk 5/10
   - Status: ⏳ TODO
   - Missing: Word filter validation logic
   - Estimated Effort: 3-4 hours

9. **Guardrail Coverage Analysis** - Risk 9/10
   - Status: ⏳ TODO
   - Missing: Cross-resource analysis (which models/agents lack guardrails)
   - Estimated Effort: 1 week

10. **Version Management Validation** - Risk 7/10
    - Status: ⏳ TODO
    - Missing: DRAFT vs production version validation
    - Estimated Effort: 4-6 hours

11. **Contextual Grounding Sources for RAG** - Risk 7/10
    - Status: ⏳ TODO
    - Missing: RAG grounding source validation
    - Estimated Effort: 1 week

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## Issue #1: AWS Bedrock Agents Security Module

**Priority:** CRITICAL
**Overall Status:** 0% Complete (0/10)
**Implementation File:** `src/wilma/checks/agents.py` (placeholder only)

### TODO ⏳

All 10 security checks are **not yet implemented**:

1. Agent action confirmation (requireConfirmation=ENABLED) - Risk 9/10
2. Guardrail configuration validation - Risk 9/10
3. Service role permission audit (least privilege) - Risk 8/10
4. Lambda function permission validation - Risk 8/10
5. Memory persistence encryption (customer KMS) - Risk 7/10
6. Knowledge base access validation - Risk 7/10
7. Tagging compliance check - Risk 5/10
8. PII detection in agent names/descriptions - Risk 6/10
9. Prompt injection pattern scanning - Risk 8/10
10. CloudWatch logging validation - Risk 7/10

**Estimated Total Effort:** 2-3 weeks

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## Issue #4: Model Fine-Tuning Security Module

**Priority:** HIGH
**Overall Status:** 0% Complete (0/11)
**Implementation File:** `src/wilma/checks/fine_tuning.py` (placeholder only)

### TODO ⏳

All 11 security checks are **not yet implemented**:

1. Training data S3 bucket security audit - Risk 10/10 for public, 8/10 for weak encryption
2. Training data PII detection (Macie integration) - Risk 9/10
3. Model data replay risk assessment - Risk 8/10
4. VPC isolation for training jobs - Risk 7/10
5. Training job logging validation - Risk 7/10
6. Output model encryption (customer KMS) - Risk 7/10
7. S3 access logging for training data - Risk 6/10
8. Training job IAM roles audit - Risk 8/10
9. Custom model tagging compliance - Risk 5/10
10. Training data source validation - Risk 9/10
11. Model card documentation check - Risk 4/10

**Estimated Total Effort:** 2 weeks

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## Recent Improvements (v1.1.0)

The following improvements were made in the comprehensive refactoring completed on 2025-11-05:

### Core Infrastructure
- ✅ Created 600+ line utils.py with reusable security utilities
- ✅ Created configuration system with YAML support (config.py)
- ✅ Enhanced CLI with flexible options

### Critical Bug Fixes
- ✅ Fixed cost anomaly check (was 100% false positive)
- ✅ Fixed IAM policy validation (added AWS-managed policy scanning)
- ✅ Fixed guardrail validation (deep configuration checking)

### High Priority Additions
- ✅ Expanded model coverage from 40% to 100%
- ✅ Added bedrock-agent VPC endpoint checking
- ✅ Added CloudWatch log encryption validation
- ✅ Eliminated PII detection false positives
- ✅ Added document scanning limitation warnings
- ✅ Added inline IAM policy checking

### Code Quality
- ✅ Added pagination support for unlimited resources
- ✅ Improved error handling across all checks
- ✅ Eliminated duplicate code with helper functions
- ✅ Added OpenSearch data access policy validation

### Testing
- ✅ Created comprehensive test suite (82 tests)
- ✅ 100% passing rate for utility tests
- ✅ Full AWS service mocking (no credentials required)

See IMPROVEMENTS.md for complete details.

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## Recommended Implementation Order

Based on risk scores and dependencies:

### Quarter 1 (Next 3 months)
1. **Complete Knowledge Bases Module** (3 remaining checks)
   - S3 public access validation (1-2 hours)
   - S3 versioning check (2-3 hours)
   - Embedding model access control (3-4 hours)
   - **Total:** ~1 week

2. **Implement Agents Module** (all 10 checks)
   - Highest attack vector risk
   - **Total:** 2-3 weeks

### Quarter 2 (Months 4-6)
3. **Complete Guardrails Module** (7 remaining checks)
   - Focus on PII, topic, and word filters first
   - Defer Automated Reasoning until AWS API available
   - **Total:** 2-3 weeks

4. **Implement Fine-Tuning Module** (all 11 checks)
   - Critical for training data security
   - **Total:** 2 weeks

### Total Estimated Time
- **Remaining work:** ~10-12 weeks
- **Current completion:** 27%
- **Target completion:** Q2 2025

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## GitHub Issue Tracking

Issues will be created with the following structure:

- **Main Issue #1:** [PRIORITY 1] AWS Bedrock Agents Security Module
  - Sub-issue #1.1: Agent action confirmation check
  - Sub-issue #1.2: Guardrail configuration validation
  - ... (10 total)

- **Main Issue #2:** [PRIORITY 1] AWS Bedrock Knowledge Bases Security Module
  - Sub-issue #2.1: S3 bucket encryption ✅ CLOSED
  - Sub-issue #2.2: Vector store encryption ✅ CLOSED
  - ... (12 total, 8 closed, 1 in progress, 3 open)

- **Main Issue #3:** [PRIORITY 1] Advanced Guardrails Validation Module
  - Sub-issue #3.1: Guardrail strength configuration ✅ CLOSED
  - Sub-issue #3.2: Content filter coverage ✅ CLOSED
  - ... (11 total, 4 closed, 7 open)

- **Main Issue #4:** [PRIORITY 1] Model Fine-Tuning Security Module
  - Sub-issue #4.1: Training data S3 bucket security
  - Sub-issue #4.2: Training data PII detection
  - ... (11 total, all open)

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## Contributing

To contribute to any of these features:

1. Check this document for current status
2. Find the corresponding GitHub issue
3. Comment on the issue to claim it
4. Reference the issue in your PR
5. Update this document when work is completed

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## License

Copyright (C) 2024  Ethan Troy
Licensed under GPL v3
