Auditor Selection: Prioritize firms with DeFi experience
Preparation: Provide complete codebase, documentation, test cases, deployment scripts
Process:
- Depend on Audit firm
- Final report with severity classification (Critical/High/Medium/Low)
Follow-up: Fix all Critical/High issues, re-audit if necessary

1.2. Application

Frequency: Quarterly

1.2.1. Backend API Security:

Authentication/Authorization: API key management
Input Validation: SQL injection, NoSQL injection, command injection testing
Rate Limiting: API abuse testing, brute force protection
Business Logic Flaws: Privilege escalation, workflow bypass attempts

1.2.2. Frontend Security:

XSS Testing: Reflected, Stored, DOM-based XSS in all input fields
CSRF Protection: Verify anti-CSRF tokens and SameSite cookies
Content Security Policy: Validate CSP headers effectiveness
Authentication Flow: Session management

1.2.3. Specific DeFi Focus:

Vault Logic Testing: Deposit limits, emergency withdrawals
Transaction Manipulation: Attempt to manipulate deposit/withdrawal amounts

1.3. Infrastructure

Frequency: Every 6 months

Implementation Process:

Cloud Security Assessment:
- Review policies and permissions
- Network security groups, VPC configuration
- Storage policies, database access controls
- Logging & monitoring setup validation
Container Security:
- Image vulnerability scanning
- Runtime security policies
- Secrets management in containers
CI/CD Pipeline Security:
- Source code repository access controls
- Build environment security
- Deployment permissions & approval workflows
- Secrets injection mechanisms

2. Internal Audits

2.1. Code Review Security

Frequency: Continuous (every PR)

Mandatory Security Checklist for each PR:

Complete input validation
Sensitive data not logged
Error handling doesn't leak information
Rate limiting for new endpoints
Database queries parameterized

SAST Tools Integration:

Detect hardcoded private keys/secrets
Identify unsafe arithmetic operations
Flag missing access controls

-> Block merge if issues exist

2.2 AI System Security

Frequency: Monthly

2.2.1. AI Execution Monitoring

Real-time Status Tracking

Health Check Logs:
- Monitor AI service availability with regular health checks
- Track API response times and system latency
- Monitor resource utilization (CPU, memory, network)
- Implement automated service restart on failures
Execution Status:
- Track whether AI decisions are being executed or skipped
- Monitor decision execution latency and success rates
- Log execution failures with detailed error information
- Implement decision queuing and retry mechanisms
Performance Metrics:
- Log processing time for each AI decision cycle
- Monitor memory usage and garbage collection patterns
- Track system load and resource bottlenecks
- Implement performance alerts and auto-scaling
Error Tracking:
- Capture and categorize all AI system exceptions
- Implement error severity levels and escalation procedures
- Track error patterns and root cause analysis
- Maintain error resolution documentation

Vault Information Monitoring

Position Changes:
- Log all vault position modifications triggered by AI decisions
- Track position entry and exit points with timestamps
- Monitor position size changes and exposure levels
- Implement position reconciliation checks
Balance Tracking:
- Monitor vault balance changes before and after AI decisions
- Track asset allocation changes and rebalancing events
- Implement balance reconciliation with external sources
- Alert on unexpected balance discrepancies
Transaction Validation:
- Verify AI-initiated transactions are executed correctly
- Compare intended vs. actual transaction outcomes
- Monitor transaction fees and slippage
- Implement transaction replay capabilities for debugging

2.2.2. AI Decision Output Validation

Request/Response Logging

Input Parameters:
- Log all parameters sent to AI (market data, vault state, risk parameters)
- Capture data timestamps and source information
- Track parameter changes and their impact on decisions
- Implement parameter validation and sanitization
Decision Output:
- Capture AI recommendations (buy/sell/hold, quantities, prices)
- Log decision confidence scores and reasoning metadata
- Track decision timing and execution delays
- Implement decision format validation
Execution Results:
- Track actual execution versus AI recommendations
- Monitor execution slippage and market impact
- Log execution success/failure rates
- Implement execution quality metrics

Output Accuracy Assessment

Decision Verification:
- Compare AI output against predefined business rules
- Validate decision logic consistency
- Implement rule-based decision validation
- Track rule violation rates and patterns
Constraint Validation:
- Ensure AI decisions respect position limits and risk parameters
- Validate compliance with regulatory requirements
- Implement automatic constraint enforcement
- Log constraint violations and corrective actions
Consistency Checks:
- Validate decision logic consistency across similar market conditions
- Track decision pattern variations and anomalies
- Implement decision similarity scoring
- Alert on inconsistent decision patterns
Performance Tracking:
- Measure AI decision accuracy over time
- Track profitability and risk-adjusted performance
- Implement performance benchmarking
- Generate performance attribution reports
Backtesting Results:
- Regular validation against historical performance data
- Track backtesting methodology and parameter settings
- Monitor backtesting accuracy and prediction quality
- Implement backtesting result validation and verification

2.2.3. Data Source Validation & Tracking

API Response Monitoring

Data Feed Validation:
- Log all API calls to price feeds with response details
- Capture response times, data freshness, and completeness
- Monitor for API failures, timeout errors, and rate limiting
- Track data source reliability scores and uptime metrics
Response Quality Assessment:
- Validate API response formats and data integrity
- Monitor for missing or corrupted data fields
- Implement data quality scoring mechanisms
- Track data provider SLA compliance

Input Data Verification

Price Data Validation:
- Compare prices across multiple sources for consistency
- Log price deviations and outliers with severity levels
- Track data latency and staleness metrics
- Validate price format and implement range checks
Market Data Tracking:
- Monitor trading volume and liquidity metrics
- Track order book depth and spread data
- Log market volatility indicators and trend analysis
- Capture external market events affecting decisions
Data Lineage Tracking:
- Track data flow from sources through AI processing
- Maintain audit trail of data transformations
- Implement data versioning and historical tracking
- Monitor data pipeline performance and bottlenecks

2.3. Data Service

Frequency: Bi-weekly

Data Integrity Checks:

Price sanity checks
Volume validation
Liquidity checks

Database Security:

Query Performance: Monitor for suspicious query patterns
Backup Integrity: Test backup restoration procedures
Access Control Review: Verify least privilege principles

Monitoring Setup:

Real-time API response time monitoring
Data freshness alerts (stale data detection)
Error rate thresholds and alerting
Suspicious access pattern detection

3. Continuous Monitoring

Frequency: Real-time Security Monitoring (24/7)

Smart Contract Monitoring:

Large Withdrawals: Alert if single withdrawal > $100k
Emergency Pause Events: Immediate alert if emergency functions triggered
Failed Transactions: Pattern detection for repeated failures
Gas Price Anomalies: Alert if gas usage deviates >50% from normal

Authentication Anomalies:

Impossible Travel: Login from 2 distant locations in short timeframe
New Device Logins: Alert when login from unrecognized devices
Privilege Escalation: Alert when user role changes

Transaction Pattern Analysis:

Suspicious Patterns: High-frequency micro transactions
Liquidity Manipulation: Large deposits followed by immediate withdrawals

Dashboard Requirements:

Real-time transaction volume & success rates
Active vault positions & risk metrics
Security incident timeline
Network health metrics (latency, throughput)

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