AI Agents: New Actors or the Dawn of Hidden Threats? The central figure emerging on the front lines of enterprise security recently is none other than the Artificial Intelligence (AI) agent. As discussions at recent cybersecurity conferences and among tech experts increasingly focus on this topic, some specialists are highlighting that AI agents are not merely 'new actors' but rather 'delegated actors.' They argue that security issues related to AI agents stem not from isolated technical limitations but from fundamental structural gaps. So, what exactly is this 'delegation gap,' and why is it becoming a critical issue for our society and businesses? On April 24, 2026, The Hacker News, a global cybersecurity publication, pointed out that AI agents are exposing structural gaps in enterprise security, and the essence of this problem lies in the agents' characteristic as 'delegated actors.' Unlike traditional AI platforms, AI agents do not operate with independent authority. Their triggering, calling, or provisioning is carried out through existing enterprise identity systems, such as human users, machine identities, bots, and service accounts. The problem arising from this process is precisely the 'AI Agent Authority Gap.' More accurately, it can be termed the 'delegation gap.' Agents are fundamentally different from humans or traditional software, yet they are inextricably linked to both. The core issue is that enterprises cannot effectively control AI agents—these new actors—without first managing the entities that delegate authority, namely human users and machine identities. While existing security frameworks focus on tracking and managing the activities of humans or traditional machine identities, AI agents can transcend these boundaries, amplifying hidden access privileges or creating unmanaged execution paths. The Hacker News warned that agents can efficiently amplify hidden access, hidden privileges, and hidden execution paths. Furthermore, their inextricable link to other identity systems within an enterprise significantly increases the likelihood of unpredictable, threatening behaviors. To resolve AI agent security issues, the first priority must be to reduce 'identity dark matter' prevalent across the enterprise. Identity dark matter literally refers to a state where all details of a specific identity are not properly managed or understood. The Hacker News emphasized that the solution for safe agent AI adoption must begin not by approaching agents in isolation, but by reducing identity dark matter across the entire domain of traditional actors. Specifically, this means identifying all human and traditional machine identities within the application environment, understanding how they authenticate, where their credentials are contained, how workflows are actually executed, and where unmanaged privileges exist. Without securing such visibility, it's impossible to understand how the authority delegated to AI agents is being used or what risks are latent. Existing IAM (Identity and Access Management) systems were designed to answer the simple question: 'Who has what access rights?' However, in complex and dynamic environments like those involving AI agents, this needs to expand to a more intricate question: 'What authority is being delegated, by whom, under what conditions, for what purpose, and within what scope?' The Hacker News pointed out that while traditional IAM was built to answer narrow questions, the era of AI agents demands an understanding of the complex layers of authority delegation. The 'Delegation Gap' Threatening Enterprise Security: Its Essence and Problems To achieve this, continuous observability and precise analysis are essential. Continuous observability must function as the decision-making engine in the era of AI agents. This means not merely capturing a snapshot of privilege status at a single point in time, but continuously tracking how privileges evolve, are delegated, and are used over time. Only this approach can effectively manage the dynamic and unpredictable behavioral patterns generated by AI agents. In South Korea, this issue is not confined to theoretical discussions either. In the Korean market, the adoption of AI technology is rapidly expanding across various sectors, including finance, healthcare, and manufacturing. Particularly in the financial sector, the use of AI agents for customer service, anomaly detection, and credit assessment is increasing. In healthcare, AI technology is being introduced for diagnostic assistance and patient data analysis. In manufacturing, AI agents are being utilized for process optimization, quality control, and predictive maintenance, establishing themselves as core components of enterprise operations. However, many domestic companies are adopting AI-based systems while maintaining their existing security frameworks, leading to a vulnerable structure concerning privilege delegation management. Many companies are introducing AI agents bu
Related Articles