Security Platform for Agentic AI Systems to Prevent Adversarial Exploitation at Runtime

Agentic AI systems include autonomous software agents like AI coding assistants (e.g., GitHub Copilot), robotic process automation bots, LLM-powered customer service agents, autonomous trading systems, and multi-agent AI orchestration platforms like AutoGPT or LangChain-based pipelines. These systems execute multi-step tasks with minimal human intervention, making runtime security essential to prevent adversarial exploitation.

The four primary types of agentic AI are: (1) Reactive agents, which respond to immediate inputs; (2) Deliberative agents, which plan actions using internal models; (3) Hybrid agents, combining reactive and deliberative capabilities; and (4) Multi-agent systems, where multiple AI agents collaborate or compete. Each type presents distinct adversarial attack surfaces that require tailored runtime security controls.

Standard ChatGPT operates as a conversational AI rather than a fully agentic system. However, when integrated with tools, APIs, or plugins—such as in ChatGPT's 'Agents' or 'Operator' modes—it exhibits agentic behavior by autonomously planning and executing multi-step tasks. These agentic configurations are precisely where adversarial threats like prompt injection become critical security risks requiring dedicated runtime protection.

Adversarial exploitation refers to attacks that manipulate an AI agent's inputs, model behavior, or decision-making process to achieve malicious outcomes. Common attacks include prompt injection, where hidden instructions override system prompts; model poisoning, which corrupts training data; and jailbreaking, which bypasses safety guardrails. These attacks can cause AI agents to leak sensitive data, execute unauthorized actions, or behave unpredictably.

Runtime protection for LLMs involves deploying an intelligent security layer that continuously monitors all inputs and outputs flowing through a language model during live operation. Protectt.ai's runtime protection detects adversarial prompts, anomalous behavioral patterns, and policy violations in real time—blocking threats before they influence the model's actions. It operates with zero performance overhead, ensuring your AI agents remain fast and reliable.

AI Red Teaming is the practice of simulating real-world adversarial attacks against your AI systems in a controlled environment to identify exploitable vulnerabilities before malicious actors do. For agentic AI, which executes autonomous decisions at scale, red teaming is critical—it exposes weaknesses in tool-use logic, prompt handling, and access controls. Protectt.ai automates this process across the full AI development and production lifecycle.

A Model Scanner applies zero-trust verification to every ML model artifact entering your environment—checking for tampering, poisoning, hidden backdoors, or malicious payloads embedded during training or distribution. As organizations increasingly source pre-trained models from third-party repositories, supply chain attacks on AI models are a growing risk. Protectt.ai's Model Scanner ensures only verified, trusted models power your agentic AI systems.

Protectt.ai holds ISO 42001 (AI Management System), ISO 27001 (Information Security Management), ISO 22301 (Business Continuity Management), and PCI DSS (Payment Card Industry Data Security Standard) certifications. These credentials validate that our security practices, AI governance frameworks, and data protection controls meet internationally recognized standards—giving enterprises confidence in the rigor and reliability of our Agentic AI Security Platform.