Security Features

Detailed documentation on Tora's security features and how to implement them

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Biometric Trust Layer

The Biometric Trust Layer is an adaptive identity framework that establishes trust between agents based on real-time behavioral patterns. It ensures every interaction is verifiably authentic by analyzing patterns in agent behavior, communication, and decision-making.

Key Features

  • Behavioral fingerprinting for unique agent identification
  • Continuous authentication throughout agent lifecycle
  • Anomaly detection for identifying compromised agents
  • Trust scoring system for agent-to-agent interactions

Implementation

// Enable biometric trust layer for an agent
await client.security.enableBiometricTrust({
  agentId: 'agent-123',
  settings: {
    sensitivityLevel: 'high',
    learningPeriod: '7d',
    authenticationFrequency: 'continuous'
  }
});

// Get trust score for an agent
const trustScore = await client.security.getTrustScore({
  agentId: 'agent-123'
});

console.log('Agent trust score:', trustScore);
// Example output: { score: 0.95, confidence: 0.89, lastUpdated: '2025-01-28T12:34:56Z' }

Best Practices

  • Allow for a sufficient learning period (at least 7 days) for accurate behavioral profiling
  • Set appropriate sensitivity levels based on your security requirements (higher sensitivity may lead to more false positives)
  • Regularly review trust scores and investigate any significant changes
  • Implement graduated response policies for different trust score thresholds (e.g., restrict access, require additional verification, etc.)

Real-Time Threat Firewall

The Real-Time Threat Firewall instantly detects and blocks rogue or unauthorized behaviors using continuous agent behavior analysis. It acts as a behavioral intrusion prevention system, monitoring all agent activities and communications for signs of compromise or malicious intent.

Key Features

  • Real-time monitoring of agent behavior and communications
  • Automatic blocking of suspicious or unauthorized activities
  • Customizable threat detection rules and thresholds
  • Detailed threat intelligence and forensic analysis

Implementation

// Configure the threat firewall for an agent
await client.security.configureThreatFirewall({
  agentId: 'agent-123',
  settings: {
    blockingMode: 'automatic', // or 'manual' for review-based blocking
    alertThreshold: 'medium',
    responseActions: ['block', 'log', 'notify']
  }
});

// Define custom threat detection rules
await client.security.createThreatRules({
  agentId: 'agent-123',
  rules: [
    {
      name: 'Unusual API Access Pattern',
      condition: {
        type: 'frequency',
        target: 'api.access',
        threshold: 50,
        timeWindow: '5m'
      },
      actions: ['block', 'notify']
    },
    {
      name: 'Suspicious Data Exfiltration',
      condition: {
        type: 'pattern',
        target: 'data.transfer',
        pattern: 'large_outbound_transfer'
      },
      actions: ['block', 'log', 'notify']
    }
  ]
});

Best Practices

  • Start with a learning mode to understand normal agent behavior before enabling blocking
  • Create graduated response policies based on threat severity (e.g., log-only for low severity, block for high severity)
  • Regularly review and update threat detection rules based on new threat intelligence
  • Implement a human-in-the-loop review process for high-impact blocking decisions to reduce false positives

Autonomous Watchdog

The Autonomous Watchdog is a self-operating monitoring system that runs 24/7. It enforces compliance with predefined rules and autonomously mitigates risks without manual oversight, ensuring your agents remain secure even when you're offline.

Key Features

  • Continuous monitoring without human intervention
  • Automated response to security incidents
  • Self-learning capabilities to improve detection over time
  • Offline operation capability for disconnected environments

Implementation

// Deploy the autonomous watchdog for an agent
await client.security.deployWatchdog({
  agentId: 'agent-123',
  settings: {
    autonomyLevel: 'full', // or 'supervised' for human approval of critical actions
    learningMode: true,
    offlineCapability: true,
    responseActions: {
      low: ['log'],
      medium: ['restrict', 'notify'],
      high: ['isolate', 'terminate', 'notify']
    }
  }
});

// Configure watchdog policies
await client.security.configureWatchdogPolicies({
  agentId: 'agent-123',
  policies: [
    {
      name: 'Resource Usage Policy',
      description: 'Monitor and control resource usage',
      rules: [
        {
          condition: 'cpu_usage > 90% for 5m',
          severity: 'medium',
          action: 'restrict'
        },
        {
          condition: 'memory_usage > 85% for 10m',
          severity: 'medium',
          action: 'restrict'
        }
      ]
    },
    {
      name: 'Communication Policy',
      description: 'Monitor and control communication patterns',
      rules: [
        {
          condition: 'outbound_connections > 100 per 1m',
          severity: 'high',
          action: 'isolate'
        }
      ]
    }
  ]
});

Best Practices

  • Start with supervised mode and gradually transition to full autonomy as confidence in the watchdog increases
  • Define clear escalation paths for different severity levels, with appropriate response actions for each
  • Regularly review watchdog logs and performance metrics to ensure it's operating as expected
  • Test the watchdog's offline capabilities regularly to ensure it can operate effectively during connectivity disruptions