roadmap.md

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

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

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

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

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

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

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

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

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

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

# 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

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

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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.