At a glance
- Most AI agents run with static credentials and overly broad IAM roles, which leaves standing access to sensitive systems.
- P0 governs AI agent privileged access by scoping each agent to the human identity it acts for.
- Privileged production access is granted just in time, with human-in-the-loop approval.
- Every session is recorded and tied to the agent and the accountable human.
AI agents are increasingly playing a part in how modern developer teams build, automate, and scale. Whether it’s using AWS Bedrock, Google Vertex, or custom-built LLM-powered systems, these first-party agents now interact directly with cloud resources, data, and applications, often without a human in the loop.
While their autonomy drives innovation, it also introduces a new kind of risk that’s playing a larger role in enterprise environments. Most AI agents today operate with static credentials and overly broad IAM roles. Once deployed, they hold standing access to sensitive systems and data. Access that’s rarely monitored, often unmanaged, and nearly impossible to audit due to deferred accountability. For teams running agentic AI in production, AI agent privileged access is now a production access problem, not just a visibility one.
P0’s Runtime Access platform now solves for that with capabilities for closed-loop agentic governance.
Control access proactively for first-party AI Agents
With this release, P0 extends its least-privilege, just-in-time (JIT) access model beyond human users and workloads to include the AI agents driving this latest era of automation. The same centralized Runtime Access platform now brings AI agent access control to organizations, so they can secure, govern, and audit access by first-party agents built on platforms like AWS Bedrock and Google Vertex.
When an agent requests access to data or cloud resources, P0 applies secondary authorization control based on the human identity that is interacting with the agent. This allows narrowing the broad permissions of the specific agent to the specific scope that the end-user needs them to have. P0 can then orchestrate a just-in-time, human-in-the-loop approval workflow for privileged production access.
Every session is fully recorded and auditable, giving security teams a tamper-proof record of what actions were taken, by which agent, including the accountable human user that triggered the agentic workflow for granular audit logs.
Human PAM vs AI agent access control
Agentic privileged access management keeps the goals of human PAM (least privilege, just-in-time access and a full audit trail), but the identity is different: an agent acts on behalf of a person or process.
| Dimension | Human PAM | AI agent access control |
| Who asks for access | A person, for themselves | An agent, on behalf of a person or process |
| Whose permissions apply | The person’s role | The agent’s permissions narrowed to the human it acts for (secondary authorization) |
| Approval | Just-in-time request and approval | Just-in-time, human-in-the-loop approval for privileged production access |
| Credentials | Vaulted or federated credentials | No static credentials; time-bound access per task |
| Audit | Session recorded against the user | Session recorded against the agent and the accountable human |
Uncover, govern, and audit agentic access with P0 Security
This powerful capability is embedded within the P0 platform, giving customers a single control plane for all identities (users, workloads, or agents) across any production environment.
Key features:
- JIT, human-in-the-loop provisioning: no vaults, static credentials, or overly permissive keys
- Session-level audit and replay: complete visibility into every action taken by an agent, tied back to the corporate IdP defined user for accountability
- Deep cloud-native coverage: granular entitlement control across major cloud providers and services: AWS, GCP, Azure, OCI, on-prem and CLI
- Closed-loop governance: extends P0’s proven Runtime Access platform to autonomous workloads for centralized visibility and policy enforcement
How to govern AI agent permissions in production
To secure AI agents’ production access, start with the identity behind each request, then narrow what the agent can do with it:
- Discover every first-party agent and the identities, roles and credentials it uses.
- Tie each agent to the human or process it acts for. This is the core of AI agent identity governance.
- Grant privileged production access just in time, with human-in-the-loop approval.
- Narrow each request to what the end user is allowed to do, and remove static credentials.
- Record every session and tie each action to the agent and the accountable human.
For the wider set of agentic AI controls, see ten things to understand when using agentic AI applications.
Central control designed for security, IAM and platform teams
P0’s Runtime Access platform serves leaders who sit at the intersection of business enablement and security. Security leaders gain governance over a rapidly expanding class of non-human identities. Identity leaders can enforce consistent policies across AWS, GCP, Azure, OCI and on-prem resources. Platform engineering and developers can seamlessly deploy agentic productivity apps without introducing sprawl and access risk.
- Right-size agentic privilege with secondary access controls, based on the human end-user that’s interacting with the AI
- Scale autonomous AI responsibly by enabling productivity enhancements without opening the door to ungoverned identity sprawl
- Make audits painless with session-level replay and automated evidence trails for simplified regulatory review prep
Getting started
P0’s agentic controls and governance capabilities are part of the Runtime Access platform. If your team is running first-party agentic applications or exploring how to securely scale AI in production, we’d love to show you how it works.
Reach out to your P0 representative or schedule a demo to see it in action.
Key takeaways
- Visibility shows what an agent could access; privileged access control limits what it can do.
- Agentic privileged access management keeps the PAM goals but changes the identity: agents act for people.
- Secondary authorization scopes each agent to the human user it is working for.
- Just-in-time approvals and session-level audit cover privileged production access.
Frequently asked questions
Why is visibility into AI agents not enough without privileged access control?
Visibility into AI agents isn’t enough on its own because knowing what an agent could access doesn’t stop it from misusing standing permissions; privileged access control is what actually limits the blast radius.
How does P0 Security extend just-in-time, least-privilege access to AI agents?
P0 Security extends just-in-time, least-privilege access to AI agents by applying the same runtime authorization it uses for human users, scoped to each agent action.
What is secondary authorization for AI agents and how does it right-size permissions?
Secondary authorization for AI agents checks every agent action against the permissions of the human user it’s acting for, right-sizing access instead of granting broad standing rights.
How do you audit AI agent actions and the accountable human user behind them?
Teams audit AI agent actions and the accountable human user behind them through P0’s session logs, which tie every action to its triggering user.
What is the difference between human PAM and AI agent access control?
Human PAM and AI agent access control share the same goals of least privilege, just-in-time access and full audit, but AI agent access control narrows an agent’s permissions to the human it acts for and ties every session to both the agent and that person.
What security controls do AI agents need for production access?
AI agents need no static credentials, just-in-time human-in-the-loop approval for privileged access, permissions scoped to the person or process they act for and session-level audit; our guide to agentic AI applications covers the broader set of controls.