Agentic Runtime Security Explained: Securing Non‑Human Identities

Agentic AI represents a paradigm shift, but its deployment exposes critical vulnerabilities in traditional Identity and Access Management (IAM) systems. The core issue is that existing security frameworks are overwhelmingly designed for human identities, yet the IT landscape is now dominated by non-human identities. While about 80% of cyberattacks already target compromised identities on the human side, the scale of the problem explodes with AI, where there can be 45 to 90 non-human identities (workloads, microservices, AI agents) for every single human one.

The Four Gaping Holes in Agentic AI Security

Traditional, static IAM protects the initial interaction between a user and an application, but it breaks down once AI agents begin interacting with each other and with backend systems. This introduces four crucial security gaps:

1. Accountability: Without a system to assign a unique identifier to every instance of an agent, it’s impossible to trace its actions. This lack of a clear audit trail means you can’t determine what an agent did or why.
2. Overprivilege: Developers, unsure of the precise permissions an agent needs, often grant broad, static privileges. An HR agent, for example, might have the standing ability to onboard or offboard employees, even when not actively performing that task. This violates the principle of least privilege and creates a significant attack surface.
3. Delegation and Impersonation: Agents are often designed to act on a user’s behalf. This becomes a major risk when an agent “lazily” inherits the user’s identity instead of using its own unique, non-human identity. This completely destroys accountability, as security systems cannot distinguish between the user’s actions and the agent’s. This is a common pattern with co-work agents that operate on a user’s desktop.
4. The “Last Mile” Problem: This is the most critical and difficult challenge. When an agent, which may already be overprivileged or impersonating a user, attempts to access sensitive data, there is often no final check to validate the access. The authorization decision was made when the agent was deployed, not at the moment of access. There is no real-time verification to ask, “For this specific context and this specific action, is this agent still authorized to access this data?” This final “last hop” to the database or mainframe is a massive blind spot.

The Five Imperatives for Secure AI Deployment

To address these gaps, a new “definition of done” is required for any AI project, based on five core principles that apply Zero Trust concepts to the agentic runtime.

• 1. Register Your Agents: Every agent must be formally registered, assigned a unique identity, and have its potential risk quantified, especially since nearly all agents make external connections.
• 2. Strip All Privileges: Move away from static permissions. Privileges should be stripped by default and granted dynamically, just-in-time, and scoped to the specific session and action required. An agent should only have the ability to offboard an employee for the brief moment it’s executing that specific, authorized workflow.
• 3. Tie All Actions to Intent: Create an unbreakable audit chain that links a user’s request to the specific actions taken by one or more agents. This is crucial for accountability, especially for high-stakes operations like financial transactions or infrastructure provisioning.
• 4. Enforce at Point of Use: Solve the “last mile” problem by implementing enforcement at the final hop. Every time an agent attempts to connect to a resource, a real-time policy and risk analysis must occur to authorize that specific action for that specific session.
• 5. Prove Control: Maintain complete and continuous auditability across the entire process—from the human identity to the non-human agents, their actions, and the system’s responses. This is non-negotiable for organizations in regulated industries.

Technologies and Organizational Strategy

Implementing this framework requires a combination of technology and organizational alignment.

• Orchestration: An engine is needed to manage the complex interactions and policies between the human and non-human identity worlds, ensuring accountability is built-in.
• Governance: Policies must be applied and enforced at every step of the process to prove who did what and ensure compliance.
• Observability: A unified observability platform is essential to bridge the typical silos between the CISO, IT, and development teams. This platform must provide:
  • Posture Management: The ability to see and manage the security posture of non-human identities, such as identifying and consolidating a sprawl of 13 different secrets managers into a manageable few.
  • Threat Management: Real-time detection of anomalous behavior. For instance, instead of waiting for a post-incident audit, security teams should be able to instantly detect an agent that appears and acts without first acquiring a unique identity from a secrets manager.

• Tags:
• Agentic AI
• AI Security
• Feature
• IAM
• Zero Trust
• Non-Human Identity
• Runtime Security