Real-Time AI Prevention

PrecogX provides microsecond-level real-time interception and prevention capabilities that act as an AI firewall, blocking malicious actions before they can execute. This comprehensive security layer works across all integration methods - from SDK to no-code platforms.

🚀 What is Real-Time Prevention?

Real-Time Prevention is PrecogX's core capability that intercepts AI agent actions before execution and makes instant ALLOW/BLOCK decisions based on security policies. Unlike traditional monitoring that detects threats after they occur, PrecogX prevents threats from happening in the first place.

Key Characteristics

• ⚡ Sub-100ms Response Times: All validation decisions complete within 100 milliseconds
• 🛡️ Pre-Execution Interception: Actions are validated before they execute
• 🎯 Function-Level Security: Every function call can be intercepted and validated
• 🔄 Real-Time Decision Making: Instant ALLOW/BLOCK responses from backend
• 🌐 Universal Integration: Works across SDK, HTTP API, and no-code platforms

🏗️ Architecture Overview

graph TD
    A[User Input] --> B[AI Agent Function]
    B --> C[PrecogX Interceptor]
    C --> D{Real-Time Validation}
    D -->|ALLOW <100ms| E[Function Execution]
    D -->|BLOCK <100ms| F[Security Exception]
    E --> G[AI Response]
    F --> H[Blocked Action Log]
    G --> I[Post-Processing Telemetry]
    H --> I
    I --> J[Dashboard & Analytics]

⚡ Microsecond-Level Performance

Response Time Breakdown

Validation Step	Time	Description
Agent Quarantine Check	~5ms	Single database query for agent status
Function Policy Check	~1ms	In-memory lookup of disallowed functions
Shell Command Check	~10ms	Pattern matching for dangerous commands
Privilege Check	~5ms	Context-based permission validation
File Operation Check	~5ms	Pattern matching for dangerous file ops
Network Overhead	~50ms	HTTP request/response latency
Total Response Time	<100ms	Complete validation cycle

Performance Optimizations

• In-Memory Security Rules: Function blacklists and patterns stored in memory
• Optimized Database Queries: Single-query agent status checks
• Efficient Pattern Matching: Regex patterns optimized for speed
• Minimal Network Payload: Compact request/response formats
• Connection Pooling: Reused HTTP connections for low latency

🔧 Integration Methods

PrecogX provides real-time prevention across multiple integration approaches, ensuring every AI agent can be protected regardless of how it's built.

1. SDK Integration

Best for: Custom Python applications, LangChain, LiteLLM

Capabilities:

• Decorator-based function wrapping
• Automatic interception of all decorated functions
• Seamless integration with existing codebases
• Exception-based error handling

Use Cases:

• Custom AI applications
• LangChain agent workflows
• LiteLLM integrations
• Python-based AI systems

2. HTTP API Integration

Best for: Custom applications in any language, middleware, proxies

Capabilities:

• Direct REST API calls for validation
• Language-agnostic implementation
• Programmatic control over validation timing
• Custom error handling and retry logic

Use Cases:

• Multi-language AI applications
• Custom middleware development
• Proxy server implementations
• Existing system integrations

3. No-Code Platform Integration

Best for: Flowise, n8n, Dify, and other visual workflow platforms

Capabilities:

• HTTP Request node integration
• Visual workflow configuration
• Webhook-based validation
• Platform-specific optimizations

Use Cases:

• Visual AI workflow builders
• No-code automation platforms
• Rapid prototyping and deployment
• Business user implementations

🛡️ Security Validation Rules

PrecogX applies comprehensive security rules during real-time validation to prevent various types of threats.

Function-Level Protection

Disallowed Functions:

• System execution functions (exec_shell, os.system, subprocess.run)
• Code evaluation functions (eval, exec, compile)
• Import manipulation (__import__, globals, locals)

Protection Scope:

• Function name validation
• Argument pattern analysis
• Context-aware permission checks
• Tool call validation

Command-Level Protection

Dangerous Shell Patterns:

• File system destruction (rm -rf, del /s)
• System shutdown commands (shutdown, reboot)
• Process termination (kill -9, taskkill)
• Disk formatting (format)

Protection Scope:

• Command argument analysis
• Pattern matching against known threats
• Context-based privilege validation
• Multi-platform command detection

Context-Aware Security

Validation Context:

• User roles and permissions
• Environment settings (production/staging)
• Session information
• Agent metadata

Dynamic Rules:

• Role-based access control
• Environment-specific policies
• Session-based restrictions
• Agent-specific configurations

🔄 Real-Time Decision Flow

sequenceDiagram
    participant Agent as AI Agent
    participant Interceptor as PrecogX Interceptor
    participant Backend as PrecogX Backend
    participant DB as Database
    
    Agent->>Interceptor: Function Call Request
    Interceptor->>Backend: Validation Request
    Backend->>DB: Agent Status Check (~5ms)
    DB-->>Backend: Agent Status
    Backend->>Backend: Function Policy Check (~1ms)
    Backend->>Backend: Command Pattern Check (~10ms)
    Backend->>Backend: Privilege Check (~5ms)
    Backend-->>Interceptor: ALLOW/BLOCK Decision (~50ms)
    
    alt ALLOW Decision
        Interceptor-->>Agent: Execute Function
        Agent-->>Interceptor: Function Result
    else BLOCK Decision
        Interceptor-->>Agent: Security Exception
    end
    
    Interceptor->>Backend: Telemetry Data (Async)

📊 Integration Method Comparison

Feature	SDK	HTTP API	No-Code
Real-Time Prevention	✅	✅	✅
Response Time	<100ms	<100ms	<100ms
Setup Complexity	Low	Medium	Low
Language Support	Python	Any	Platform-specific
Function Wrapping	Automatic	Manual	Node-based
Error Handling	Exception-based	Custom	Platform-native
Context Support	Rich	Rich	Platform-dependent

🎯 Use Case Examples

E-commerce Order Processing Agent

Scenario: AI agent processes PDF orders with OCR and extracts order data

Protection Points:

• OCR extraction validation before file processing
• Order data extraction validation before JSON parsing
• Tool call validation for external API integrations
• Context-aware validation based on customer tier

Real-Time Benefits:

• Prevents malicious PDF processing
• Blocks injection attempts in order data
• Validates tool usage before execution
• Maintains sub-100ms response times

Customer Service Chatbot

Scenario: AI chatbot handles customer inquiries with tool integrations

Protection Points:

• Message processing validation before LLM calls
• Tool call validation for database queries
• Response generation validation before sending
• Context validation based on customer permissions

Real-Time Benefits:

• Prevents prompt injection attacks
• Blocks unauthorized database access
• Validates response content before delivery
• Maintains conversational flow with minimal latency

Content Generation Workflow

Scenario: AI workflow generates marketing content with approval gates

Protection Points:

• Content generation validation before processing
• Approval workflow validation before publishing
• Tool call validation for external services
• Context validation based on user roles

Real-Time Benefits:

• Prevents inappropriate content generation
• Blocks unauthorized publishing actions
• Validates external service integrations
• Maintains workflow efficiency with instant decisions

🚀 Getting Started

Choose Your Integration Method

1. SDK Integration: For Python applications with LangChain or LiteLLM
2. HTTP API Integration: For custom applications in any language
3. No-Code Integration: For Flowise, n8n, Dify, and other visual platforms

Implementation Steps

1. Get API Key: Create your PrecogX API key in the dashboard
2. Choose Method: Select the integration approach that fits your setup
3. Configure Validation: Set up real-time validation for your functions
4. Test Integration: Verify sub-100ms response times and blocking behavior
5. Monitor Results: Track prevention events in the PrecogX dashboard

Best Practices

• Start with Critical Functions: Begin by protecting your most sensitive operations
• Test Response Times: Ensure validation completes within 100ms for your use case
• Monitor Prevention Events: Track blocked actions to refine security policies
• Gradual Rollout: Implement protection incrementally across your AI agents
• Context Optimization: Provide rich context for better security decisions

📈 Benefits of Real-Time Prevention

Security Benefits

• Proactive Protection: Prevents threats before they execute
• Zero-Day Coverage: Blocks unknown threats through pattern analysis
• Comprehensive Coverage: Protects all function calls and tool usage
• Context-Aware Security: Adapts protection based on user roles and environment

Performance Benefits

• Minimal Latency: Sub-100ms validation doesn't impact user experience
• Efficient Resource Usage: Lightweight validation with minimal overhead
• Scalable Architecture: Handles high-volume validation requests
• Optimized Network: Compact request/response formats

Operational Benefits

• Unified Security: Consistent protection across all integration methods
• Easy Integration: Simple setup regardless of platform or language
• Rich Monitoring: Comprehensive dashboard for prevention analytics
• Flexible Configuration: Customizable security policies and rules

🔗 Related Documentation

• Getting Started Guide - Connect your first agent
• SDK Integration - Detailed SDK setup and usage
• No-Code Integration - Platform-specific integration guides
• Security Architecture - Comprehensive security overview
• API Reference - Complete API documentation

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Ready to protect your AI agents with real-time prevention? Start with our Getting Started Guide or explore integration options for your specific platform.