Runtime Trust: The Essential Security Framework for the AI Agent Era

Enterprise AI is entering a new phase. Traditionally, AI has primarily served as an 'assistant' that answers questions, but autonomous agents are now spreading through business environments—systems that can invoke multiple tools, access business systems, and complete multi-step tasks with minimal human intervention. Along with this shift, new risks that traditional security management approaches cannot adequately address are becoming apparent.

Conventional software simply executes logic that developers have written in advance, in a predetermined sequence. In contrast, AI agents dynamically determine which tools to use, which APIs to call, and what information to retrieve in order to achieve their objectives. While this flexibility creates significant business value, it also introduces new security challenges. The fundamental difficulty is that it becomes hard to ascertain—using conventional management methods—whether the autonomous actions of an AI agent are truly safe as it continues to operate.

Most current AI security discussions focus on issues like prompt injection attacks (inserting malicious instructions), model vulnerabilities, and data breaches. These are indeed important challenges, but they address only part of the problem. A critical oversight is that once an AI agent has passed legitimate authentication and entered a system, conventional security controls provide little protection during its autonomous operation phase.

Corporate security has traditionally been designed around three core questions: 'Who are you?', 'What can you access?', and 'What operations are permitted?'. Mechanisms such as multi-factor authentication (MFA), role-based access control, and zero trust architecture function effectively for human users and traditional applications. However, AI agents continue to act autonomously even after passing authentication with a legitimate ID and obtaining authorized access to business systems like Microsoft 365, ServiceNow, Salesforce, and GitHub. While authentication only verifies 'who you are', a critical gap exists in current systems: the lack of continuous verification of 'what the agent is actually doing' during execution.

To address this challenge, a new concept called 'Runtime Trust' is being proposed. This refers to an approach that continuously monitors an agent's behavior even after authentication, and verifies in real time whether it is acting in accordance with user intent and organizational policy. A fundamental shift in thinking is required—moving from 'verification at the entry point' to 'continuous verification while operations are ongoing'.

Modern AI agents operate within a complex ecosystem that includes large language models (LLMs), Model Context Protocol (MCP) servers, RAG (retrieval-augmented generation) systems, vector databases, enterprise APIs, SaaS platforms, internal knowledge repositories, and even other AI agents. In such an interconnected environment, a single compromised tool or the introduction of malicious data into a knowledge source poses the risk of cascading impacts across the entire system. As automation delivers greater benefits, the structural challenge emerges: the attack surface expands proportionally.

As enterprise adoption of AI agents accelerates, the focus of security is shifting from 'entry point control' to 'continuous behavioral monitoring'. The question of how to construct a verification framework that harnesses an agent's autonomous decision-making and action capabilities while validating that its behavior aligns with organizational intent is becoming unavoidable in enterprise AI strategy.