AI agents are quickly moving from experiments to real execution layers inside organizations. They don’t just generate content - they take actions: calling APIs, accessing systems, modifying data.
From a security perspective, the underlying mechanisms are familiar. The same protocols are still in use: HTTP, SSH, file systems. But what has changed is the decision-making layer. Instead of deterministic automation, we now have systems that decide how to use their access. That shift matters.
It challenges traditional assumptions around identity, access, and control — especially in Privileged Access Management (PAM), where models have historically been built around humans and relatively static service identities.
Before getting into controls and risks, it’s worth aligning on terminology.
Let’s start with terminology
An AI agent is a system that can plan and execute tasks, either on behalf of a user or autonomously based on some trigger. It is not just a model - it is a combination of reasoning, memory, and tool usage.
An AI model is the underlying intelligence layer. It predicts. The agent is what turns those predictions into actions. An AI agent runtime is the environment where the agent executes - local machine, Kubernetes, serverless, or SaaS. The runtime defines the trust boundary: what the agent can access, how its identity can be verified, and what controls can be enforced.
An AI tool is any capability the agent uses to interact with the outside world: APIs, file systems, databases, SSH, browsers.
An MCP server (Model Context Protocol) is an emerging standard for exposing tools and context in a structured way. It simplifies how agents discover capabilities, but under the hood still results in API calls or system interactions.
On the security side, authentication verifies identity, while authorization defines what that identity can do. A non-human identity (NHI) includes services, workloads, scripts — and now AI agents. A sovereign AI agent operates independently, while delegated identity becomes relevant when an agent acts on behalf of a user.
These distinctions matter because AI agents introduce a new category: dynamic, short-lived actors running in varying environments. They don’t fit neatly into existing PAM models.
What makes AI agents different?
At a practical level, AI agents are execution engines. They take an objective, break it down, and interact with systems or data to complete it. The key difference is that they are non-deterministic. You don’t get the same sequence of actions every time. This is also where the risk comes from.
You are no longer just granting access - you are allowing a system to decide how to use that access. If permissions are too broad, misuse doesn’t need to be explicitly coded or intended.
Agents are also model-agnostic. The same agent can use a local model today and a SaaS model tomorrow, which means the trust boundary is not fixed. From a protocol perspective, nothing fundamentally new is happening. What’s new is the autonomy behind the interactions.
What actually needs to be protected?
The attack surface is largely the same — but easier to abuse.
If an agent has access to the local filesystem, it can read sensitive data, modify configurations, or exfiltrate information. This is why sandboxing is becoming critical. Remote systems are also in scope: APIs, databases, cloud services — anything the agent can reach.
The real control points remain the protocols:
- HTTP APIs
- SSH / SFTP
- File systems and shares
Agents don’t invent new access paths - they automate existing ones. That means existing PAM controls are still highly relevant. Data loss prevention (DLP) becomes especially important when agents can move data across boundaries without human oversight.
For critical systems, the risk is not just access, but interaction - what commands can be run, what API endpoints can be called, and whether state can be modified.
How should this be protected?
This is where PAM becomes directly relevant - with some adaptation.
First, outside traditional PAM scope, is sandboxing the local runtime. If the agent runs on an endpoint, its visibility and access must be controlled.
Then comes identity. AI agents are ephemeral - they spin up, perform a task, and disappear. This requires identities that can be issued on demand and retired just as quickly.
Authentication should go through indirection. Instead of giving agents direct credentials, they authenticate to PAM, which brokers access to target systems or data.
When acting on behalf of users, delegated identity must be controlled - not full privilege cloning, but scoped delegation with clear limits.
Authorization needs to move beyond coarse roles. Fine-grained control is required, especially for APIs: not just which service, but which endpoint, method, and conditions.
Finally, observability is critical. Session recording, audit logs, and real-time streaming to SIEM systems are non-negotiable.
AI agent identities are still evolving
There is no widely adopted standard for managing AI agent identities that fits their lifecycle. Many implementations rely on static API keys or reused service identities, which are not well suited for dynamic environments.
What’s needed is ephemeral, attestable identity - where the agent can prove where it’s running and what it is. Technologies like SPIFFE/SPIRE start to make sense here. They were designed for non-human identities and solve a similar problem: short-lived, verifiable identities for workloads.
Authentication and authorization
Two main authentication patterns exist today:
- PAM-mediated access, where the agent authenticates to PAM and access is brokered
- Vaulted credentials, issued to the agent and rotated regularly
The first provides stronger control and avoids exposing secrets directly. When agents act on behalf of users, delegation must be constrained by time, scope, and approvals. Blindly copying user permissions creates unnecessary risk.
Authorization is where most of the real control should live. RBAC still has a place, but it’s not sufficient. More granular models like ABAC or PBAC allow policies to be defined at the level of API endpoints, methods, and context. Behavioral monitoring (UEBA) adds another layer by detecting anomalies when agents behave unexpectedly.
Visibility is non-negotiable
Every action an agent takes - commands, file transfers, API calls - must be traceable.Not just logged, but structured in a way that enables analysis and response.This data should flow into SIEM systems so both humans and automated responses can react in near real time. Without this, autonomous systems operate without accountability.
What can be done today?
Most of the core building blocks already exist. The challenge is applying them to a new type of identity.
PrivX acts as a central control point between AI agents and target systems by brokering machine-to-machine access over SSH and HTTP. Instead of exposing credentials directly, it supports ephemeral authentication using short-lived certificates, while still enabling controlled credential handling when necessary.
Access can be enforced at a granular level - down to individual API endpoints, methods, or SSH commands - and all activity can be recorded and streamed into SIEM systems for visibility and response.
The key is combining these capabilities with dynamic controls. Access should be issued just-in-time and scoped to a specific task, limiting the blast radius when agents behave unexpectedly.
What still needs to be built
Several gaps remain:
- Ephemeral machine identity integrated into PAM
- Stronger, context-aware authentication for API-based access
- Governance at the MCP layer as it becomes more widely adopted
- Safe and controlled user privilege delegation to AI agents
The bottom line
AI agents don’t introduce entirely new security problems - but they amplify existing ones.
PAM already solves many of these problems. It just needs to adapt to identities that are short-lived, autonomous, and unpredictable.
The real challenge is understanding how these agents are used in practice: what they access, what protocols they rely on, what data they interact with, and whether they operate independently or via delegated user privileges.
Final thought
AI agents are already becoming part of real-world infrastructure. The question is not whether they will need privileged access - but how that access is controlled.
PrivX provides a practical way to apply modern PAM controls to this new class of identities - from just-in-time access and ephemeral authentication to fine-grained authorization and full session visibility.
Learn how PrivX can help secure AI agent access >>>
