AI agents need identity before gateway, security expert says

Enterprise artificial intelligence is rapidly evolving beyond simple question-answering assistants to autonomous agents capable of reasoning, invoking tools, and executing complex business workflows with minimal human oversight. This fundamental shift in software operation unlocks significant business value but also introduces novel security risks.

While traditional security discussions often focus on issues like prompt injection and model vulnerabilities, a critical challenge emerges after an AI agent is authenticated and begins acting autonomously. Current security controls offer limited visibility into whether these agents continue to operate safely during execution. This necessitates a new security paradigm: runtime trust.

Authentication verifies an agent's identity, but it does not guarantee its trustworthiness throughout its operation. An AI agent might be legitimately authenticated with valid credentials and access, but its dynamic reasoning and decision-making processes during execution can deviate from user intent or organizational policy. This gap requires continuous validation of an agent's actions, not just its initial identity.

New threats such as goal drift, excessive tool invocation, memory poisoning, and context manipulation exploit the autonomous nature of these agents. In multi-agent systems, one agent's misbehavior can be amplified by others, leading to cascading failures across enterprise workflows. MITRE's ATLAS framework details these adversarial AI behaviors.

Runtime trust extends security by continuously validating AI behavior during execution. This involves ensuring that autonomous decisions remain aligned with organizational policies through capabilities like intent validation, behavioral monitoring, and policy enforcement. By validating proposed actions before sensitive operations and monitoring tool usage, organizations can ensure that AI agents operate securely and as intended.