ADR

Product Introduction

1. Definition: ADR (Agentic AI Detection and Response) is a production-proven, enterprise-grade security framework specifically designed for AI agents built on the Model Context Protocol (MCP). It falls under the technical categories of AI Security, Agent Security, and Detection and Response (DR) systems.
2. Core Value Proposition: ADR exists to solve the critical security challenges of deploying AI agents at scale in enterprise environments. It provides comprehensive observability, robust threat detection, and scalable protection against novel attacks like prompt injection and credential exposure, enabling secure and reliable agentic AI operations.

Main Features

1. ADR Sensor (High-Fidelity Agentic Telemetry): This component provides deep observability into AI agent operations. Unlike traditional Endpoint Detection and Response (EDR) tools that only see low-level system events, the ADR Sensor captures the full "agentic reasoning" context. This includes the agent's internal prompts, tool calls, reasoning steps, and the causal chain linking user intent to final execution, creating a high-fidelity audit trail for security analysis.
2. ADR Explorer (Systematic Red Teaming & Hard-Example Generation): ADR Explorer is a pre-deployment security testing framework. It enables systematic red teaming against AI agents to proactively discover vulnerabilities. It uses automated techniques to generate "hard examples"—complex, adversarial inputs that stress-test the agent's defenses—which are then used to train and improve the online detection models, implementing a robust "shift-left" security practice.
3. ADR Detector (Scalable Two-Tier Online Detection): This is the core runtime detection engine. It employs a two-tier architecture for efficiency and accuracy at scale. The first tier uses fast, rule-based or lightweight ML models for initial triage to filter out benign activity. The second tier performs deep, context-aware reasoning using more sophisticated models (like LLMs) only on the suspicious sessions flagged by the first tier. This hybrid approach balances high detection accuracy with low operational cost.

Problems Solved

1. Pain Point: Limited Observability in Agentic AI. Traditional security tools lack visibility into the reasoning process of AI agents, creating blind spots where attacks like prompt injection can occur undetected.
2. Target Audience: Enterprise Security Teams and AI Platform Engineers at large organizations deploying AI agents. Specifically, Security Operations Center (SOC) analysts, AI red team specialists, and platform architects responsible for the safe deployment of agentic workflows using frameworks like MCP.
3. Use Cases: Securing Enterprise AI Assistants that have access to internal tools and data; Protecting AI-Powered Workflow Automation from manipulation; Preventing Credential Leakage and Data Exfiltration via compromised agents; and Compliance Auditing for AI agent actions within regulated industries.

Unique Advantages

1. Differentiation: Unlike static rule-based firewalls (e.g., traditional web application firewalls) or standalone LLM-based scanners, ADR is a holistic, production-tested system. It combines deep telemetry, proactive testing, and a cost-effective two-tier detection architecture, whereas competitors often address only one piece of the puzzle (e.g., only prompt injection detection) without the scale or integration for enterprise deployment.
2. Key Innovation: The integration of systematic red teaming (ADR Explorer) directly into the detection lifecycle is a key innovation. It creates a continuous feedback loop where discovered attacks automatically improve the detector, making the system adaptive and increasingly robust against evolving threats. Furthermore, its native integration with the Model Context Protocol (MCP) provides unparalleled context that generic tools cannot access.

Frequently Asked Questions (FAQ)

1. What is ADR in AI security? ADR stands for Agentic AI Detection and Response, a specialized security system designed to monitor, detect, and respond to threats targeting AI agents, particularly those built using the Model Context Protocol (MCP), by analyzing their full reasoning context.
2. How does ADR detect prompt injection attacks? ADR detects prompt injection and other agent attacks by using its Sensor to capture the agent's complete reasoning chain (prompts, tool calls). Its two-tier Detector then analyzes this context, using fast triage and deep LLM-based analysis to identify malicious intent or deviations from expected behavior that indicate an injection attempt.
3. Is ADR compatible with any AI agent framework? ADR is specifically designed and optimized for AI agents built on the Model Context Protocol (MCP), as its Sensor leverages MCP's structured communication to gather essential telemetry. Its principles may apply to other agent frameworks, but its full functionality is tied to MCP integration.
4. What are the performance metrics for ADR in production? According to its deployment at Uber, ADR has demonstrated 97.2% precision in production, processing over 10,000 agent sessions daily across 7,200+ unique hosts, while uncovering hundreds of credential exposures with minimal false positives.
5. How does ADR reduce the cost of LLM-based security detection? ADR reduces high LLM inference costs through its two-tier detection architecture. A fast, inexpensive first tier filters out the vast majority of benign sessions, allowing the more expensive, context-aware LLM analysis to be applied only to a small subset of high-risk sessions, making scalable enterprise deployment economically feasible.