# TraceCtrl — Full Documentation for LLMs

> Security Observability & Control for Agentic AI

## Overview

TraceCtrl is the security observability and control platform purpose-built for agentic AI. As organizations deploy autonomous AI agents that make decisions, call tools, access data, and interact with external systems, TraceCtrl provides the security infrastructure to monitor, protect, and govern these agents at scale.

The platform addresses a critical gap: traditional application security tools were not designed for the non-deterministic, multi-step, multi-agent workflows that define agentic AI. TraceCtrl fills this gap with deep observability, runtime protection, and proactive security testing.

## Architecture & Approach

TraceCtrl is built on two foundational pillars:

### Pillar 1: Trace (Complete Observability)

**TraceCtrl Core** provides full observability into agent behavior:

- **Real-time Agent Monitoring**: Every agent action, tool invocation, data access, and inter-agent communication is captured in real time.
- **OpenTelemetry-Native**: Telemetry extraction is built on the OpenTelemetry standard, ensuring compatibility across any platform and framework. Agents are instrumented without code changes.
- **Cross-Cloud Agent Inventory**: Automatic discovery and cataloging of all AI agents running across AWS, GCP, and Azure environments.
- **Immutable Audit Logs**: Complete audit trail from alert to root cause, stored immutably for compliance and forensic analysis.
- **Span-Level Tracing**: Hierarchical span trees showing the full execution path of agent workflows — from initial prompt through all intermediate reasoning, tool calls, and final output.

### Pillar 2: Control (Security Enforcement)

Three products provide layered security enforcement:

**TraceCtrl Guard — Runtime Protection**
- ShieldPrompt: Multi-layer prompt injection defense that detects and blocks injection attempts in real time.
- 4-Point Guardrails: Input validation, output sanitization, tool call authorization, and data access control.
- Data Leakage Prevention: Monitors and prevents sensitive data from being exposed through agent responses or tool outputs.
- Policy Enforcement: Security policies are enforced at runtime without impacting agent performance or developer velocity.

**TraceCtrl Risk — Attack Surface Mapping**
- TAGAAI (Threat-informed Attack Graphs for Agentic AI): A proprietary threat modeling framework that:
  - Maps agent topologies into directed attack graphs
  - Identifies all possible attack paths through agent workflows
  - Scores risk at the node level (individual agents, tools, data sources)
  - Highlights vulnerable nodes (e.g., agents with excessive tool access, unvalidated external inputs)
  - Integrates with the LVD (LLM Vulnerability Database)
- Compliance Dashboard: Maps security posture to regulatory frameworks and generates attestation reports.
- Risk Scoring: Quantitative risk scores for individual agents, workflows, and the overall agent fleet.

**TraceCtrl Strike — Offensive Security**
- Automated Red Team Testing: Uses TAGAAI-generated attack paths to automatically test agents for vulnerabilities.
- Adversary Simulation: Tests agents the way real adversaries would — prompt injection, jailbreaking, tool misuse, data exfiltration, privilege escalation.
- Continuous Testing: Runs continuously in CI/CD pipelines and production environments.

## TAGAAI Framework

TAGAAI (Threat-informed Attack Graphs for Agentic AI) is TraceCtrl's core threat modeling innovation. It works by:

1. **Topology Discovery**: Automatically maps the full topology of an agentic system — agents, tools, data sources, external APIs, inter-agent communication paths.
2. **Attack Graph Generation**: Converts the topology into a directed graph where nodes represent assets and edges represent potential attack vectors.
3. **Threat Modeling**: Applies known attack patterns (from the LVD and custom threat intelligence) to identify exploitable paths.
4. **Risk Scoring**: Assigns quantitative risk scores based on exploitability, impact, and blast radius.
5. **Attack Path Generation**: Produces concrete attack paths that can be executed by TraceCtrl Strike for automated testing.

## Supported Ecosystem

### Agent Frameworks
- LangChain
- CrewAI
- Agno
- Strands
- Google ADK (Agent Development Kit)

### Cloud Platforms
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Microsoft Azure

### LLM Providers
- OpenAI (GPT-4, GPT-4o, etc.)
- Anthropic (Claude)
- Google (Gemini)
- Meta (LLaMA)
- Mistral
- Cohere

### Standards & Integrations
- OpenTelemetry (OTEL) native
- LVD (LLM Vulnerability Database) integration
- SOC 2, ISO 27001, GDPR compliance mapping

## Company Information

- **Name**: TraceCtrl
- **Website**: https://tracectrl.ai
- **Contact**: info@tracectrl.ai
- **Category**: AI Security / Cybersecurity / Developer Tools
- **Tagline**: Trace every agent. Control every risk.

## Key Differentiators

1. **Purpose-built for agentic AI**: Not a retrofit of traditional AppSec tools. Designed from the ground up for the unique challenges of autonomous AI agents.
2. **TAGAAI**: The only platform with threat-informed attack graph analysis specifically for agentic AI topologies.
3. **Full lifecycle**: Covers observability, runtime protection, risk assessment, and offensive testing in a single platform.
4. **Framework agnostic**: Works across all major agent frameworks and LLM providers.
5. **OpenTelemetry native**: No proprietary agents or SDKs — uses the industry standard for telemetry.