SAFLA's safety framework implements comprehensive validation mechanisms, risk assessment, and rollback capabilities to ensure secure and reliable autonomous operation. Safety is not an afterthought but a core architectural principle woven throughout every component.
graph TB
subgraph "Multi-Layer Safety Architecture"
subgraph "Input Safety Layer"
INPUT_VAL[Input Validation]
SANITIZATION[Data Sanitization]
RATE_LIMIT[Rate Limiting]
AUTH[Authentication]
end
subgraph "Process Safety Layer"
CONSTRAINTS[Safety Constraints]
MONITORING[Real-time Monitoring]
CIRCUIT[Circuit Breakers]
TIMEOUT[Timeout Controls]
end
subgraph "Output Safety Layer"
OUTPUT_VAL[Output Validation]
CONTENT_FILTER[Content Filtering]
APPROVAL[Approval Gates]
AUDIT[Audit Logging]
end
subgraph "System Safety Layer"
RESOURCE[Resource Limits]
EMERGENCY[Emergency Stop]
ROLLBACK[Rollback System]
RECOVERY[Recovery Procedures]
end
INPUT_VAL --> CONSTRAINTS
SANITIZATION --> MONITORING
RATE_LIMIT --> CIRCUIT
AUTH --> TIMEOUT
CONSTRAINTS --> OUTPUT_VAL
MONITORING --> CONTENT_FILTER
CIRCUIT --> APPROVAL
TIMEOUT --> AUDIT
OUTPUT_VAL --> RESOURCE
CONTENT_FILTER --> EMERGENCY
APPROVAL --> ROLLBACK
AUDIT --> RECOVERY
end
style INPUT_VAL fill:#ffebee
style CONSTRAINTS fill:#fff3e0
style OUTPUT_VAL fill:#e8f5e8
style RESOURCE fill:#e1f5fe
The constraints engine enforces safety rules through a hierarchical system of hard and soft constraints.
graph LR
subgraph "Constraint Hierarchy"
subgraph "Hard Constraints"
MEMORY_HARD[Memory Limits]
CPU_HARD[CPU Limits]
SECURITY_HARD[Security Rules]
CRITICAL_HARD[Critical Operations]
end
subgraph "Soft Constraints"
PERFORMANCE_SOFT[Performance Targets]
QUALITY_SOFT[Quality Thresholds]
RESOURCE_SOFT[Resource Preferences]
USER_SOFT[User Preferences]
end
subgraph "Dynamic Constraints"
CONTEXT_DYN[Context-Aware Rules]
ADAPTIVE_DYN[Adaptive Limits]
LEARNING_DYN[Learning-Based Rules]
TEMPORAL_DYN[Time-Based Rules]
end
MEMORY_HARD --> PERFORMANCE_SOFT
CPU_HARD --> QUALITY_SOFT
SECURITY_HARD --> RESOURCE_SOFT
CRITICAL_HARD --> USER_SOFT
PERFORMANCE_SOFT --> CONTEXT_DYN
QUALITY_SOFT --> ADAPTIVE_DYN
RESOURCE_SOFT --> LEARNING_DYN
USER_SOFT --> TEMPORAL_DYN
end
style MEMORY_HARD fill:#ffcdd2
style PERFORMANCE_SOFT fill:#fff9c4
style CONTEXT_DYN fill:#e1f5fe
from safla.core.safety_validation import (
SafetyConstraint,
ConstraintType,
ViolationAction,
SafetyValidationFramework
)
# Define hard constraints (cannot be violated)
memory_constraint = SafetyConstraint(
name="memory_limit",
constraint_type=ConstraintType.HARD,
description="Maximum memory usage limit",
rule="memory_usage <= 1000000000", # 1GB
threshold=1000000000,
violation_action=ViolationAction.EMERGENCY_STOP,
priority=1 # Highest priority
)
cpu_constraint = SafetyConstraint(
name="cpu_limit",
constraint_type=ConstraintType.HARD,
description="Maximum CPU usage limit",
rule="cpu_usage <= 0.9", # 90%
threshold=0.9,
violation_action=ViolationAction.THROTTLE,
priority=1
)
# Define soft constraints (warnings and adjustments)
performance_constraint = SafetyConstraint(
name="response_time",
constraint_type=ConstraintType.SOFT,
description="Target response time",
rule="response_time <= 5.0", # 5 seconds
threshold=5.0,
violation_action=ViolationAction.WARNING,
priority=3
)
# Define dynamic constraints (context-aware)
adaptive_constraint = SafetyConstraint(
name="adaptive_load",
constraint_type=ConstraintType.DYNAMIC,
description="Adaptive load management",
rule="current_load <= adaptive_threshold",
threshold_function=lambda context: min(0.8, context.get('available_resources', 0.5)),
violation_action=ViolationAction.ADJUST,
priority=2
)
# Initialize safety framework
safety_framework = SafetyValidationFramework()
await safety_framework.start()
# Add constraints
safety_framework.constraint_engine.add_constraint(memory_constraint)
safety_framework.constraint_engine.add_constraint(cpu_constraint)
safety_framework.constraint_engine.add_constraint(performance_constraint)
safety_framework.constraint_engine.add_constraint(adaptive_constraint)# Evaluate constraints against current system state
system_state = {
"memory_usage": 800000000, # 800MB
"cpu_usage": 0.75, # 75%
"response_time": 3.2, # 3.2 seconds
"current_load": 0.6, # 60%
"available_resources": 0.7 # 70%
}
evaluation_result = await safety_framework.constraint_engine.evaluate_constraints(
system_state=system_state,
context={"operation": "memory_search", "priority": "normal"}
)
if evaluation_result.has_violations:
print("⚠️ Constraint violations detected:")
for violation in evaluation_result.violations:
print(f" - {violation.constraint_name}: {violation.message}")
print(f" Action: {violation.action}")
print(f" Severity: {violation.severity}")
else:
print("✅ All constraints satisfied")The validation pipeline implements multi-stage validation with comprehensive error handling and timeout controls.
graph TB
subgraph "Validation Pipeline"
subgraph "Pre-Processing Validation"
INPUT_CHECK[Input Format Check]
SCHEMA_VAL[Schema Validation]
RANGE_CHECK[Range Validation]
TYPE_CHECK[Type Validation]
end
subgraph "Security Validation"
AUTH_CHECK[Authentication Check]
AUTHZ_CHECK[Authorization Check]
INJECTION_CHECK[Injection Prevention]
MALWARE_CHECK[Malware Scanning]
end
subgraph "Business Logic Validation"
RULE_CHECK[Business Rule Check]
CONSISTENCY[Consistency Validation]
DEPENDENCY[Dependency Validation]
WORKFLOW[Workflow Validation]
end
subgraph "Post-Processing Validation"
OUTPUT_CHECK[Output Validation]
QUALITY_CHECK[Quality Assurance]
COMPLIANCE[Compliance Check]
FINAL_APPROVAL[Final Approval]
end
INPUT_CHECK --> AUTH_CHECK
SCHEMA_VAL --> AUTHZ_CHECK
RANGE_CHECK --> INJECTION_CHECK
TYPE_CHECK --> MALWARE_CHECK
AUTH_CHECK --> RULE_CHECK
AUTHZ_CHECK --> CONSISTENCY
INJECTION_CHECK --> DEPENDENCY
MALWARE_CHECK --> WORKFLOW
RULE_CHECK --> OUTPUT_CHECK
CONSISTENCY --> QUALITY_CHECK
DEPENDENCY --> COMPLIANCE
WORKFLOW --> FINAL_APPROVAL
end
style INPUT_CHECK fill:#e3f2fd
style AUTH_CHECK fill:#ffebee
style RULE_CHECK fill:#e8f5e8
style OUTPUT_CHECK fill:#fff3e0
from safla.core.safety_validation import ValidationPipeline, ValidationStage
# Create validation pipeline
pipeline = ValidationPipeline(
timeout=30.0, # 30 seconds total timeout
fail_fast=False, # Continue validation even if some stages fail
parallel_execution=True # Run independent stages in parallel
)
# Define validation stages
input_validation = ValidationStage(
name="input_validation",
validator=validate_input_format,
timeout=5.0,
required=True,
retry_count=0
)
security_validation = ValidationStage(
name="security_validation",
validator=validate_security,
timeout=10.0,
required=True,
retry_count=1
)
business_validation = ValidationStage(
name="business_validation",
validator=validate_business_rules,
timeout=15.0,
required=True,
retry_count=2
)
output_validation = ValidationStage(
name="output_validation",
validator=validate_output,
timeout=5.0,
required=True,
retry_count=0
)
# Add stages to pipeline
pipeline.add_stage(input_validation)
pipeline.add_stage(security_validation)
pipeline.add_stage(business_validation)
pipeline.add_stage(output_validation)
# Execute validation pipeline
validation_result = await pipeline.execute(
data=request_data,
context=validation_context
)
if validation_result.is_valid:
print("✅ Validation passed")
proceed_with_operation(validation_result.validated_data)
else:
print("❌ Validation failed")
for error in validation_result.errors:
print(f" - Stage: {error.stage_name}")
print(f" Error: {error.message}")
print(f" Code: {error.error_code}")async def validate_input_format(data, context):
"""Validate input data format and structure."""
errors = []
# Check required fields
required_fields = ["content", "metadata", "operation_type"]
for field in required_fields:
if field not in data:
errors.append(f"Missing required field: {field}")
# Validate data types
if "content" in data and not isinstance(data["content"], str):
errors.append("Content must be a string")
# Validate content length
if "content" in data and len(data["content"]) > 10000:
errors.append("Content exceeds maximum length (10000 characters)")
return ValidationResult(
is_valid=len(errors) == 0,
errors=errors,
validated_data=data if len(errors) == 0 else None
)
async def validate_security(data, context):
"""Validate security aspects of the request."""
errors = []
# Check for potential injection attacks
dangerous_patterns = ["<script>", "javascript:", "eval(", "exec("]
content = data.get("content", "")
for pattern in dangerous_patterns:
if pattern.lower() in content.lower():
errors.append(f"Potentially dangerous pattern detected: {pattern}")
# Validate user permissions
user_id = context.get("user_id")
operation = data.get("operation_type")
if not await check_user_permission(user_id, operation):
errors.append("Insufficient permissions for requested operation")
return ValidationResult(
is_valid=len(errors) == 0,
errors=errors,
validated_data=data if len(errors) == 0 else None
)The risk assessment engine quantifies potential risks using weighted factors and provides risk mitigation recommendations.
graph TB
subgraph "Risk Assessment Framework"
subgraph "Risk Categories"
SECURITY[Security Risks]
PERFORMANCE[Performance Risks]
RELIABILITY[Reliability Risks]
COMPLIANCE[Compliance Risks]
end
subgraph "Risk Factors"
MEMORY_RISK[Memory Usage Risk]
CPU_RISK[CPU Usage Risk]
NETWORK_RISK[Network Risk]
DATA_RISK[Data Integrity Risk]
end
subgraph "Risk Calculation"
WEIGHTING[Factor Weighting]
AGGREGATION[Risk Aggregation]
SCORING[Risk Scoring]
CLASSIFICATION[Risk Classification]
end
subgraph "Risk Response"
MITIGATION[Risk Mitigation]
MONITORING[Risk Monitoring]
ESCALATION[Risk Escalation]
REPORTING[Risk Reporting]
end
SECURITY --> MEMORY_RISK
PERFORMANCE --> CPU_RISK
RELIABILITY --> NETWORK_RISK
COMPLIANCE --> DATA_RISK
MEMORY_RISK --> WEIGHTING
CPU_RISK --> AGGREGATION
NETWORK_RISK --> SCORING
DATA_RISK --> CLASSIFICATION
WEIGHTING --> MITIGATION
AGGREGATION --> MONITORING
SCORING --> ESCALATION
CLASSIFICATION --> REPORTING
end
style SECURITY fill:#ffebee
style MEMORY_RISK fill:#fff3e0
style WEIGHTING fill:#e8f5e8
style MITIGATION fill:#e1f5fe
from safla.core.safety_validation import RiskFactor, RiskAssessment
# Define risk factors
def calculate_memory_risk(data):
"""Calculate risk based on memory usage."""
memory_usage = data.get('memory_usage', 0)
memory_limit = data.get('memory_limit', 1000000000) # 1GB
usage_ratio = memory_usage / memory_limit
if usage_ratio < 0.5:
return 0.1 # Low risk
elif usage_ratio < 0.7:
return 0.3 # Medium risk
elif usage_ratio < 0.9:
return 0.7 # High risk
else:
return 0.95 # Critical risk
def calculate_cpu_risk(data):
"""Calculate risk based on CPU usage."""
cpu_usage = data.get('cpu_usage', 0)
if cpu_usage < 0.5:
return 0.1
elif cpu_usage < 0.7:
return 0.3
elif cpu_usage < 0.9:
return 0.6
else:
return 0.9
def calculate_operation_risk(data):
"""Calculate risk based on operation type."""
operation_type = data.get('operation_type', 'read')
risk_levels = {
'read': 0.1,
'write': 0.3,
'delete': 0.7,
'admin': 0.9,
'system_modify': 0.95
}
return risk_levels.get(operation_type, 0.5)
# Create risk factors
memory_risk = RiskFactor(
name="memory_risk",
description="Risk based on memory usage",
weight=0.3,
calculator=calculate_memory_risk
)
cpu_risk = RiskFactor(
name="cpu_risk",
description="Risk based on CPU usage",
weight=0.2,
calculator=calculate_cpu_risk
)
operation_risk = RiskFactor(
name="operation_risk",
description="Risk based on operation type",
weight=0.5,
calculator=calculate_operation_risk
)
# Initialize risk assessment
risk_assessment = RiskAssessment()
risk_assessment.add_risk_factor(memory_risk)
risk_assessment.add_risk_factor(cpu_risk)
risk_assessment.add_risk_factor(operation_risk)
# Assess risk for a specific operation
operation_data = {
'memory_usage': 750000000, # 750MB
'memory_limit': 1000000000, # 1GB
'cpu_usage': 0.8, # 80%
'operation_type': 'write'
}
risk_result = await risk_assessment.assess_risk(operation_data)
print(f"Overall Risk Score: {risk_result.overall_score:.3f}")
print(f"Risk Level: {risk_result.risk_level}")
print(f"Risk Factors:")
for factor_name, factor_score in risk_result.factor_scores.items():
print(f" - {factor_name}: {factor_score:.3f}")
# Get risk mitigation recommendations
if risk_result.overall_score > 0.7:
recommendations = await risk_assessment.get_mitigation_recommendations(risk_result)
print("🚨 High risk detected. Recommendations:")
for rec in recommendations:
print(f" - {rec.action}: {rec.description}")
print(f" Expected risk reduction: {rec.risk_reduction:.2%}")The rollback system provides safe reversion capabilities through automated checkpoint creation and recovery procedures.
graph TB
subgraph "Rollback System Architecture"
subgraph "Checkpoint Creation"
AUTO_CHECKPOINT[Automatic Checkpoints]
MANUAL_CHECKPOINT[Manual Checkpoints]
SCHEDULED_CHECKPOINT[Scheduled Checkpoints]
EVENT_CHECKPOINT[Event-Triggered Checkpoints]
end
subgraph "State Preservation"
MEMORY_STATE[Memory State]
CONFIG_STATE[Configuration State]
MODEL_STATE[Model State]
METADATA_STATE[Metadata State]
end
subgraph "Recovery Procedures"
PARTIAL_ROLLBACK[Partial Rollback]
FULL_ROLLBACK[Full Rollback]
SELECTIVE_ROLLBACK[Selective Rollback]
EMERGENCY_ROLLBACK[Emergency Rollback]
end
subgraph "Verification"
INTEGRITY_CHECK[Integrity Verification]
CONSISTENCY_CHECK[Consistency Verification]
PERFORMANCE_CHECK[Performance Verification]
SAFETY_CHECK[Safety Verification]
end
AUTO_CHECKPOINT --> MEMORY_STATE
MANUAL_CHECKPOINT --> CONFIG_STATE
SCHEDULED_CHECKPOINT --> MODEL_STATE
EVENT_CHECKPOINT --> METADATA_STATE
MEMORY_STATE --> PARTIAL_ROLLBACK
CONFIG_STATE --> FULL_ROLLBACK
MODEL_STATE --> SELECTIVE_ROLLBACK
METADATA_STATE --> EMERGENCY_ROLLBACK
PARTIAL_ROLLBACK --> INTEGRITY_CHECK
FULL_ROLLBACK --> CONSISTENCY_CHECK
SELECTIVE_ROLLBACK --> PERFORMANCE_CHECK
EMERGENCY_ROLLBACK --> SAFETY_CHECK
end
style AUTO_CHECKPOINT fill:#e8f5e8
style MEMORY_STATE fill:#fff3e0
style PARTIAL_ROLLBACK fill:#e1f5fe
style INTEGRITY_CHECK fill:#fce4ec
from safla.core.safety_validation import CheckpointManager, CheckpointType
# Initialize checkpoint manager
checkpoint_manager = CheckpointManager(
storage_path="./checkpoints",
max_checkpoints=50,
compression_enabled=True,
encryption_enabled=True
)
# Create manual checkpoint
checkpoint_id = await checkpoint_manager.create_checkpoint(
name="before_major_update",
description="Checkpoint before implementing new memory algorithm",
checkpoint_type=CheckpointType.MANUAL,
include_components=[
"memory_system",
"meta_cognitive_engine",
"safety_framework"
]
)
print(f"✅ Checkpoint created: {checkpoint_id}")
# Configure automatic checkpoints
await checkpoint_manager.configure_auto_checkpoints(
interval=timedelta(hours=6), # Every 6 hours
triggers=[
"before_system_modification",
"before_model_update",
"on_error_threshold"
],
retention_policy={
"daily_checkpoints": 7, # Keep 7 daily checkpoints
"weekly_checkpoints": 4, # Keep 4 weekly checkpoints
"monthly_checkpoints": 12 # Keep 12 monthly checkpoints
}
)
# List available checkpoints
checkpoints = await checkpoint_manager.list_checkpoints(
limit=10,
sort_by="creation_time",
filter_by={"checkpoint_type": CheckpointType.AUTOMATIC}
)
for checkpoint in checkpoints:
print(f"📁 {checkpoint.name} ({checkpoint.creation_time})")
print(f" Size: {checkpoint.size_mb:.1f} MB")
print(f" Components: {', '.join(checkpoint.components)}")# Perform rollback to specific checkpoint
rollback_result = await checkpoint_manager.rollback_to_checkpoint(
checkpoint_id=checkpoint_id,
components=["memory_system"], # Selective rollback
verify_integrity=True,
create_pre_rollback_checkpoint=True
)
if rollback_result.success:
print("✅ Rollback completed successfully")
print(f"Rollback duration: {rollback_result.duration:.2f} seconds")
print(f"Components restored: {', '.join(rollback_result.restored_components)}")
else:
print("❌ Rollback failed")
print(f"Error: {rollback_result.error_message}")
print(f"Failed components: {', '.join(rollback_result.failed_components)}")
# Emergency rollback (fastest possible recovery)
emergency_result = await checkpoint_manager.emergency_rollback(
target_state="last_known_good",
timeout=30.0 # Maximum 30 seconds
)
# Verify system state after rollback
verification_result = await safety_framework.verify_system_integrity()
if verification_result.is_healthy:
print("✅ System integrity verified after rollback")
else:
print("⚠️ System integrity issues detected:")
for issue in verification_result.issues:
print(f" - {issue.component}: {issue.description}")The emergency stop system provides immediate system shutdown capabilities for critical safety situations.
from safla.core.safety_validation import EmergencyStopSystem, StopReason
# Initialize emergency stop system
emergency_stop = EmergencyStopSystem(
response_time_ms=100, # 100ms maximum response time
graceful_shutdown_timeout=30.0, # 30 seconds for graceful shutdown
force_shutdown_timeout=5.0 # 5 seconds for forced shutdown
)
# Register emergency stop triggers
emergency_stop.register_trigger(
name="memory_overflow",
condition=lambda state: state.get('memory_usage', 0) > 0.95,
priority=1 # Highest priority
)
emergency_stop.register_trigger(
name="security_breach",
condition=lambda state: state.get('security_alert', False),
priority=1
)
emergency_stop.register_trigger(
name="constraint_violation",
condition=lambda state: state.get('hard_constraint_violated', False),
priority=2
)
# Manual emergency stop
await emergency_stop.trigger_emergency_stop(
reason=StopReason.MANUAL,
message="Manual emergency stop triggered by administrator",
immediate=True
)
# Check emergency stop status
status = await emergency_stop.get_status()
if status.is_stopped:
print("🚨 System in emergency stop state")
print(f"Reason: {status.stop_reason}")
print(f"Stopped at: {status.stop_time}")
print(f"Can resume: {status.can_resume}")
# Resume from emergency stop (if safe)
if status.can_resume:
resume_result = await emergency_stop.resume_system(
verify_safety=True,
run_diagnostics=True
)
if resume_result.success:
print("✅ System resumed successfully")
else:
print(f"❌ Resume failed: {resume_result.error_message}")Real-time safety monitoring provides continuous oversight of system health and safety metrics.
from safla.core.safety_validation import SafetyMonitor
# Initialize safety monitor
safety_monitor = SafetyMonitor(
monitoring_interval=1.0, # Check every second
alert_thresholds={
"memory_usage": 0.8,
"cpu_usage": 0.8,
"error_rate": 0.05,
"response_time": 5.0
}
)
# Start monitoring
await safety_monitor.start_monitoring()
# Get real-time safety metrics
metrics = await safety_monitor.get_current_metrics()
print(f"🔍 Safety Metrics:")
print(f" Memory Usage: {metrics['memory_usage']:.1%}")
print(f" CPU Usage: {metrics['cpu_usage']:.1%}")
print(f" Error Rate: {metrics['error_rate']:.2%}")
print(f" Response Time: {metrics['response_time']:.2f}s")
print(f" Safety Score: {metrics['safety_score']:.3f}")
# Get safety alerts
alerts = await safety_monitor.get_active_alerts()
if alerts:
print("⚠️ Active Safety Alerts:")
for alert in alerts:
print(f" - {alert.severity}: {alert.message}")
print(f" Component: {alert.component}")
print(f" Time: {alert.timestamp}")
else:
print("✅ No active safety alerts")
# Configure alert handlers
async def handle_memory_alert(alert):
"""Handle memory usage alerts."""
if alert.severity == "CRITICAL":
await emergency_stop.trigger_emergency_stop(
reason=StopReason.MEMORY_OVERFLOW,
message=f"Critical memory usage: {alert.value:.1%}"
)
elif alert.severity == "WARNING":
await memory_system.trigger_cleanup()
safety_monitor.register_alert_handler("memory_usage", handle_memory_alert)Comprehensive safety testing ensures the reliability of safety mechanisms.
import pytest
from safla.core.safety_validation import SafetyTestSuite
class TestSafetyFramework:
"""Comprehensive safety framework tests."""
@pytest.mark.asyncio
async def test_constraint_enforcement(self):
"""Test that safety constraints are properly enforced."""
# Test hard constraint violation
violation_data = {"memory_usage": 1200000000} # Exceeds 1GB limit
result = await safety_framework.validate_system_modification(violation_data)
assert not result.is_approved, "Hard constraint violation should be rejected"
assert "memory_limit" in result.violated_constraints
@pytest.mark.asyncio
async def test_emergency_stop_response_time(self):
"""Test emergency stop response time."""
start_time = time.time()
await emergency_stop.trigger_emergency_stop(
reason=StopReason.MANUAL,
immediate=True
)
stop_time = time.time()
response_time = (stop_time - start_time) * 1000 # Convert to milliseconds
assert response_time < 100, f"Emergency stop took {response_time:.1f}ms (should be < 100ms)"
@pytest.mark.asyncio
async def test_rollback_integrity(self):
"""Test rollback system integrity."""
# Create checkpoint
checkpoint_id = await checkpoint_manager.create_checkpoint(
name="test_checkpoint",
checkpoint_type=CheckpointType.MANUAL
)
# Modify system state
await memory_system.store_vector_memory(
content="Test modification",
embedding=[0.1] * 512
)
# Rollback to checkpoint
rollback_result = await checkpoint_manager.rollback_to_checkpoint(
checkpoint_id=checkpoint_id,
verify_integrity=True
)
assert rollback_result.success, "Rollback should succeed"
assert rollback_result.integrity_verified, "Integrity should be verified"
@pytest.mark.asyncio
async def test_risk_assessment_accuracy(self):
"""Test risk assessment accuracy."""
# Test low-risk scenario
low_risk_data = {
"memory_usage": 100000000, # 100MB
"cpu_usage": 0.3,
"operation_type": "read"
}
low_risk_result = await risk_assessment.assess_risk(low_risk_data)
assert low_risk_result.overall_score < 0.3, "Low-risk scenario should have low score"
# Test high-risk scenario
high_risk_data = {
"memory_usage": 950000000, # 950MB
"cpu_usage": 0.95,
"operation_type": "system_modify"
}
high_risk_result = await risk_assessment.assess_risk(high_risk_data)
assert high_risk_result.overall_score > 0.7, "High-risk scenario should have high score"
# Run safety tests
if __name__ == "__main__":
pytest.main([__file__, "-v"])# safety_config.yaml
safety_framework:
constraints:
hard_constraints:
- name: "memory_limit"
threshold: 1000000000 # 1GB
action: "emergency_stop"
- name: "cpu_limit"
threshold: 0.9 # 90%
action: "throttle"
soft_constraints:
- name: "response_time"
threshold: 5.0 # 5 seconds
action: "warning"
- name: "error_rate"
threshold: 0.05 # 5%
action: "alert"
validation:
timeout: 30.0
retry_count: 3
parallel_execution: true
fail_fast: false
risk_assessment:
factors:
- name: "memory_risk"
weight: 0.3
- name: "cpu_risk"
weight: 0.2
- name: "operation_risk"
weight: 0.5
thresholds:
low: 0.3
medium: 0.6
high: 0.8
critical: 0.9
monitoring:
interval: 1.0 # seconds
alert_thresholds:
memory_usage: 0.8
cpu_usage: 0.8
error_rate: 0.05
response_time: 5.0
emergency_stop:
response_time_ms: 100
graceful_timeout: 30.0
force_timeout: 5.0
checkpoints:
auto_interval: 21600 # 6 hours
max_checkpoints: 50
compression: true
encryption: true
retention:
daily: 7
weekly: 4
monthly: 12Next: Delta Evaluation - System improvement quantification
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