Tora AI

Security Policies

Effective security policies are the foundation of a robust AI agent security strategy. They define the rules, constraints, and behaviors that your agents must adhere to, and provide a framework for responding to security incidents.

Policy Structure

A well-structured security policy should include the following components:

Scope: Define which agents and systems the policy applies to
Rules: Specific constraints and requirements for agent behavior
Monitoring: How agent behavior will be monitored and logged
Response: Actions to take when policy violations occur
Review: Schedule and process for reviewing and updating the policy

Example Policy

// Create a comprehensive security policy
const policy = await client.policies.create({
  name: 'Production Agent Security Policy',
  description: 'Security policy for production-level AI agents',
  scope: {
    agentTypes: ['assistant', 'processor', 'analyzer'],
    environments: ['production']
  },
  rules: [
    {
      name: 'Resource Usage Limits',
      description: 'Prevent resource abuse',
      conditions: [
        { metric: 'cpu_usage', operator: '<', value: 80, duration: '5m' },
        { metric: 'memory_usage', operator: '<', value: 75, duration: '5m' },
        { metric: 'api_calls', operator: '<', value: 1000, duration: '1h' }
      ],
      action: 'restrict'
    },
    {
      name: 'Data Access Control',
      description: 'Control access to sensitive data',
      conditions: [
        { resource: 'customer_data', permission: 'read' },
        { resource: 'financial_data', permission: 'deny' }
      ],
      action: 'enforce'
    },
    {
      name: 'Communication Restrictions',
      description: 'Restrict external communications',
      conditions: [
        { direction: 'outbound', destinations: ['approved_apis'], action: 'allow' },
        { direction: 'outbound', destinations: ['*'], action: 'deny' }
      ],
      action: 'enforce'
    }
  ],
  monitoring: {
    logLevel: 'info',
    metrics: ['cpu', 'memory', 'network', 'api_calls'],
    alertThresholds: {
      policy_violation: 'medium',
      suspicious_activity: 'high'
    }
  },
  response: {
    automatic: {
      low: ['log'],
      medium: ['restrict', 'notify'],
      high: ['isolate', 'notify']
    },
    manual: {
      approvers: ['security_team'],
      timeoutAction: 'restrict'
    }
  },
  review: {
    frequency: 'quarterly',
    approvers: ['security_lead', 'ai_lead']
  }
});

Best Practices

Start with restrictive policies and gradually relax them as needed
Use a layered approach with different policies for different agent types and environments
Regularly review and update policies based on security incidents, threat intelligence, and changing requirements
Test policies in a staging environment before deploying to production to avoid disrupting legitimate agent operations
Document the rationale behind each policy rule to ensure understanding and proper implementation

Threat Modeling

Threat modeling is a structured approach to identifying, quantifying, and addressing security risks in your AI agent systems. It helps you understand potential threats, vulnerabilities, and attack vectors, and prioritize your security efforts accordingly.

Threat Modeling Process

Identify assets: Determine what you're trying to protect (data, functionality, resources)
Create system model: Document how your agents interact with each other and external systems
Identify threats: Determine potential threats to your system using frameworks like STRIDE
Analyze risks: Assess the likelihood and impact of each threat
Mitigate risks: Implement controls to address the identified risks

Common AI Agent Threats

Prompt Injection

Malicious inputs designed to manipulate agent behavior or extract sensitive information.

Data Poisoning

Corrupting training or operational data to influence agent behavior or decisions.

Model Extraction

Attempts to steal or reverse-engineer the agent's underlying model or algorithms.

Resource Exhaustion

Overwhelming the agent with requests to cause denial of service or excessive resource consumption.

Unauthorized Access

Gaining unauthorized control of an agent to perform malicious actions or access sensitive data.

Communication Interception

Intercepting or manipulating communications between agents or between agents and external systems.

Implementing Threat Modeling with Tora

// Use Tora's threat modeling tools
const threatModel = await client.security.createThreatModel({
  name: 'Agent System Threat Model',
  description: 'Comprehensive threat model for our agent ecosystem',
  assets: [
    { name: 'customer_data', sensitivity: 'high', location: 'database' },
    { name: 'agent_model', sensitivity: 'high', location: 'agent' },
    { name: 'api_credentials', sensitivity: 'high', location: 'agent_config' }
  ],
  systemComponents: [
    { name: 'assistant_agent', type: 'agent', interfaces: ['user', 'database', 'api'] },
    { name: 'processor_agent', type: 'agent', interfaces: ['assistant_agent', 'database'] },
    { name: 'database', type: 'storage', interfaces: ['assistant_agent', 'processor_agent'] },
    { name: 'external_api', type: 'external', interfaces: ['assistant_agent'] }
  ],
  threats: [
    {
      name: 'prompt_injection',
      category: 'manipulation',
      target: 'assistant_agent',
      likelihood: 'high',
      impact: 'high',
      mitigations: [
        { control: 'input_validation', effectiveness: 'medium' },
        { control: 'behavioral_monitoring', effectiveness: 'high' }
      ]
    },
    {
      name: 'unauthorized_access',
      category: 'access_control',
      target: 'database',
      likelihood: 'medium',
      impact: 'high',
      mitigations: [
        { control: 'authentication', effectiveness: 'high' },
        { control: 'access_control', effectiveness: 'high' },
        { control: 'activity_monitoring', effectiveness: 'medium' }
      ]
    }
    // Additional threats...
  ]
});

// Generate a threat assessment report
const report = await client.security.generateThreatAssessment({
  threatModelId: threatModel.id,
  format: 'detailed'
});

console.log('Threat assessment report:', report);

Best Practices

Conduct threat modeling early in the development process and update regularly
Involve cross-functional teams including security experts, AI developers, and domain experts
Use a structured approach like STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, Elevation of privilege)
Prioritize threats based on risk (likelihood × impact) to focus on the most critical issues first
Document and share threat models to ensure all stakeholders understand the security risks and mitigations

Performance Optimization

Optimizing the performance of your security measures is crucial to ensure they provide effective protection without negatively impacting the functionality and responsiveness of your AI agents.

Performance Considerations

Latency: Security checks should add minimal latency to agent operations
Resource usage: Security measures should use resources efficiently
Scalability: Security infrastructure should scale with your agent deployment
Reliability: Security measures should be highly available and fault-tolerant

Optimization Techniques

// Configure performance-optimized security settings
await client.security.configurePerformance({
  agentId: 'agent-123',
  settings: {
    // Use tiered security checks
    securityTiers: {
      // Fast, lightweight checks for all operations
      tier1: {
        checks: ['basic_validation', 'rate_limiting'],
        latencyTarget: '5ms'
      },
      // More intensive checks for sensitive operations
      tier2: {
        checks: ['behavioral_analysis', 'content_scanning'],
        triggers: ['sensitive_data_access', 'configuration_change'],
        latencyTarget: '50ms'
      },
      // Comprehensive checks for high-risk operations
      tier3: {
        checks: ['deep_behavioral_analysis', 'threat_intelligence'],
        triggers: ['unusual_behavior', 'security_alert'],
        latencyTarget: '200ms'
      }
    },
    
    // Enable caching for security decisions
    caching: {
      enabled: true,
      ttl: '5m',
      maxSize: 10000,
      excludedChecks: ['deep_behavioral_analysis']
    },
    
    // Configure batching for security telemetry
    telemetry: {
      batchSize: 100,
      flushInterval: '30s',
      compressionEnabled: true
    },
    
    // Set resource limits for security components
    resources: {
      cpuLimit: '10%',
      memoryLimit: '256MB',
      priorityClass: 'high'
    }
  }
});

// Monitor security performance metrics
const metrics = await client.security.getPerformanceMetrics({
  agentId: 'agent-123',
  timeRange: {
    start: '2025-01-20T00:00:00Z',
    end: '2025-01-27T23:59:59Z'
  },
  metrics: [
    'security_check_latency',
    'security_check_throughput',
    'false_positive_rate',
    'detection_rate',
    'resource_usage'
  ]
});

console.log('Security performance metrics:', metrics);

Best Practices

Use a tiered approach to security checks, with fast, lightweight checks for all operations and more intensive checks only for sensitive or high-risk operations
Implement caching for security decisions to avoid redundant checks for identical or similar operations
Batch security telemetry data to reduce network overhead and improve efficiency
Set appropriate resource limits for security components to prevent them from consuming excessive resources
Regularly monitor and analyze security performance metrics to identify bottlenecks and optimization opportunities
Consider using dedicated hardware or specialized infrastructure for compute-intensive security operations
Implement graceful degradation mechanisms to maintain basic security even under high load or resource constraints