# AI Safety Lab - Development Roadmap

This document outlines the future development trajectory for the AI Safety Lab platform, focusing on enterprise-grade safety evaluation capabilities and compliance integration.

## Version 1.0 - Current Release

### ✅ Implemented Features
- **Core DSPy Agents**: RedTeamingAgent and SafetyJudgeAgent with optimization
- **Hugging Face Integration**: Model interface with API and local loading
- **Orchestration Loop**: Multi-iteration evaluation with DSPy optimization
- **Gradio UI**: Professional web interface for safety evaluation
- **Comprehensive Metrics**: Risk assessment, performance tracking, and reporting
- **Modular Architecture**: Clean separation of concerns and extensible design

## Version 2.0 - Enterprise Integration (Q1 2026)

### 🔮 Policy-as-Code Integration

#### Safety Policy Framework
```python
class SafetyPolicy:
    """Configurable safety policy framework"""
    
    def __init__(self, policy_config: Dict[str, Any]):
        self.rules = self._load_rules(policy_config)
        self.thresholds = policy_config.get("thresholds", {})
        self.enforcement = policy_config.get("enforcement", "recommend")
    
    def evaluate_output(self, model_output: str) -> PolicyViolation:
        """Policy-compliant output evaluation"""
        pass
```

**Implementation Goals:**
- YAML/JSON-based policy definitions
- Customizable risk thresholds
- Automated policy compliance checking
- Version-controlled policy management

#### Policy Templates
- **Industry Standards**: Healthcare, finance, education
- **Regulatory Compliance**: GDPR, HIPAA, CCPA
- **Organizational Policies**: Custom corporate guidelines
- **Age-Appropriate Content**: K-12, adult content policies

### 🔮 Human-in-the-Loop Escalation

#### Escalation Framework
```python
class EscalationManager:
    """Human review and escalation system"""
    
    def should_escalate(self, judgment: SafetyJudgment) -> bool:
        """Determine if human review is required"""
        pass
    
    def create_escalation_ticket(self, judgment: SafetyJudgment) -> EscalationTicket:
        """Create human review ticket"""
        pass
```

**Features:**
- Automatic escalation for high-risk discoveries
- Human review workflow integration
- Case management and tracking
- Feedback loop for model improvement

### 🔮 Safety Memory / Casebook

#### Knowledge Management
```python
class SafetyCasebook:
    """Persistent safety knowledge base"""
    
    def add_case(self, case: SafetyCase):
        """Store new safety discovery"""
        pass
    
    def search_similar_cases(self, prompt: str) -> List[SafetyCase]:
        """Find relevant historical cases"""
        pass
```

**Capabilities:**
- Persistent storage of safety discoveries
- Case similarity search and retrieval
- Pattern recognition across evaluations
- Knowledge base for training and improvement

## Version 3.0 - Advanced Analytics (Q2 2026)

### 🔮 Compliance Mapping & Reporting

#### Regulatory Framework Integration
```python
class ComplianceMapper:
    """Maps safety findings to regulatory requirements"""
    
    def map_to_nist_framework(self, metrics: SafetyMetrics) -> NISTReport:
        """Generate NIST AI RMF compliance report"""
        pass
    
    def map_to_ai_act(self, findings: List[SafetyJudgment]) -> AIActReport:
        """Generate EU AI Act compliance assessment"""
        pass
```

**Supported Frameworks:**
- **NIST AI Risk Management Framework**
- **EU AI Act Requirements**
- **ISO/IEC 23894 AI Guidelines**
- **Industry-Specific Regulations** (FDA, SEC, etc.)

#### Automated Compliance Reporting
- Scheduled compliance assessments
- Risk threshold monitoring
- Regulatory filing preparation
- Audit trail maintenance

### 🔮 Advanced Analytics & Visualization

#### Risk Analytics Dashboard
```python
class RiskAnalytics:
    """Advanced risk analysis and visualization"""
    
    def calculate_trend_metrics(self, history: List[EvaluationReport]) -> TrendAnalysis:
        """Analyze risk trends over time"""
        pass
    
    def generate_comparative_analysis(self, reports: List[EvaluationReport]) -> ComparisonReport:
        """Compare models or configurations"""
        pass
```

**Visualizations:**
- Risk heatmaps and trend charts
- Model comparison matrices
- Attack vector effectiveness analysis
- Compliance score dashboards

### 🔮 Multi-Model Evaluation

#### Comparative Safety Analysis
```python
class ComparativeEvaluator:
    """Multi-model safety comparison framework"""
    
    def compare_models(self, model_configs: List[ModelConfig]) -> ComparisonReport:
        """Run comparative safety evaluation"""
        pass
    
    def benchmark_safety_performance(self, models: List[str]) -> BenchmarkReport:
        """Industry safety benchmarking"""
        pass
```

**Features:**
- Parallel multi-model evaluation
- Comparative safety scoring
- Industry benchmarking capabilities
- Model selection recommendations

## Version 4.0 - Intelligence & Automation (Q3 2026)

### 🔮 Adaptive Red-Teaming

#### Intelligent Attack Discovery
```python
class AdaptiveRedTeam:
    """Self-improving red-teaming system"""
    
    def discover_new_vectors(self, model_behavior: Dict) -> List[AttackVector]:
        """Discover novel attack vectors"""
        pass
    
    def adapt_strategies(self, effectiveness_metrics: Dict) -> RedTeamStrategy:
        """Adapt attack strategies based on effectiveness"""
        pass
```

**Capabilities:**
- Automated attack vector discovery
- Strategy adaptation based on model responses
- Zero-day vulnerability detection
- Continuous learning from evaluation results

### 🔮 Predictive Risk Assessment

#### Proactive Safety Modeling
```python
class PredictiveRiskModel:
    """Predictive risk assessment capabilities"""
    
    def predict_failure_modes(self, model_characteristics: Dict) -> List[PotentialFailure]:
        """Predict potential failure modes"""
        pass
    
    def estimate_risk_trajectory(self, evaluation_history: List[EvaluationReport]) -> RiskProjection:
        """Project future risk trends"""
        pass
```

**Features:**
- Predictive risk modeling
- Failure mode analysis
- Risk trajectory projection
- Early warning systems

### 🔮 Automated Remediation

#### Real-Time Safety Enforcement
```python
class SafetyEnforcer:
    """Automated safety enforcement system"""
    
    def apply_safety_filters(self, model_output: str, context: Dict) -> FilteredOutput:
        """Apply real-time safety filters"""
        pass
    
    def recommend_mitigations(self, risk_assessment: SafetyJudgment) -> List[MitigationStrategy]:
        """Generate mitigation recommendations"""
        pass
```

**Capabilities:**
- Real-time safety filtering
- Automated content moderation
- Dynamic safety policy enforcement
- Mitigation strategy recommendation

## Version 5.0 - Ecosystem Integration (Q4 2026)

### 🔮 Third-Party Integrations

#### Model Registry Integration
- **MLflow Integration**: Model lifecycle management
- **AWS SageMaker**: Cloud-based model deployment
- **Azure ML**: Enterprise AI platform integration
- **Google Vertex AI**: Google Cloud AI platform

#### Monitoring & Alerting
- **Prometheus/Grafana**: Metrics collection and visualization
- **Splunk**: Log analysis and monitoring
- **PagerDuty**: Alerting and incident response
- **Slack/Teams**: Team collaboration integration

### 🔮 API & SDK Development

#### REST API
```python
# API endpoints for programmatic access
POST /api/v1/evaluations
GET /api/v1/evaluations/{id}
GET /api/v1/models/available
POST /api/v1/policies/validate
```

#### Python SDK
```python
from ai_safety_lab import SafetyLab, EvaluationConfig

# Programmatic safety evaluation
lab = SafetyLab(api_key="your-key")
config = EvaluationConfig(model_id="gpt-4", objective="harmful-content")
report = lab.evaluate(config)
```

### 🔮 Enterprise Features

#### Multi-Tenancy
- Organization-based access control
- Resource isolation and quotas
- Custom branding and white-labeling
- Audit logging and compliance

#### Scalability & Performance
- Distributed evaluation processing
- Load balancing and auto-scaling
- Caching and optimization
- Cost management and monitoring

## Technical Debt & Infrastructure

### 🔮 Architecture Improvements

#### Microservices Migration
- **Agent Services**: Containerized agent deployments
- **Evaluation Service**: Scalable evaluation orchestration
- **Metrics Service**: Centralized metrics collection
- **API Gateway**: Unified API management

#### Data Layer Enhancements
- **Time-Series Database**: InfluxDB for metrics storage
- **Document Store**: MongoDB for evaluation results
- **Search Engine**: Elasticsearch for case lookup
- **Cache Layer**: Redis for performance optimization

### 🔮 Security & Compliance

#### Enhanced Security
- **Zero-Trust Architecture**: Secure-by-design principles
- **Data Encryption**: At-rest and in-transit encryption
- **Access Management**: RBAC and SSO integration
- **Audit Logging**: Comprehensive audit trails

#### Compliance Automation
- **SOC 2 Type II**: Automated compliance reporting
- **ISO 27001**: Security management integration
- **GDPR**: Data protection and privacy controls
- **FedRAMP**: Government compliance capabilities

## Implementation Timeline

### Phase 1: Foundation (Current - Q1 2026)
- ✅ Core platform implementation
- 🔄 Policy-as-code framework
- 🔄 Human escalation workflows
- 🔄 Safety casebook development

### Phase 2: Intelligence (Q2 - Q3 2026)
- 🔄 Advanced analytics and visualization
- 🔄 Compliance mapping
- 🔄 Adaptive red-teaming
- 🔄 Predictive risk assessment

### Phase 3: Enterprise (Q4 2026 - Q1 2027)
- 🔄 Third-party integrations
- 🔄 API and SDK development
- 🔄 Multi-tenancy support
- 🔄 Scalability improvements

## Success Metrics

### Technical Metrics
- **Evaluation Throughput**: Number of evaluations per hour
- **Detection Accuracy**: Precision and recall of safety issues
- **System Availability**: Uptime and reliability
- **Response Time**: Average evaluation completion time

### Business Metrics
- **Risk Reduction**: Measured decrease in safety incidents
- **Compliance Score**: Regulatory compliance percentage
- **User Adoption**: Active users and evaluations
- **Cost Efficiency**: Resource utilization and cost savings

### Quality Metrics
- **Code Coverage**: Test coverage percentage
- **Bug Density**: Defects per thousand lines of code
- **Documentation**: API and system documentation completeness
- **Customer Satisfaction**: User feedback and NPS scores

---

This roadmap represents our commitment to building the most comprehensive and effective AI safety evaluation platform. Each iteration is designed to provide tangible value while building toward our vision of fully automated, intelligent safety assessment capabilities.

**Note**: Timeline and priorities are subject to change based on user feedback, technical constraints, and evolving industry requirements.