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06/06/2026 2:08 am
TL;DR: Centralized routing, runtime policy enforcement, audit logs, and least-privilege controls are being positioned as core safeguards for autonomous agents that process trillions of tokens per month, according to Palo Alto Networks, with Portkey set to become the AI Gateway for Prisma AIRS. The move signals that AI agent governance is shifting from pilot oversight to production identity control.
NHIMG editorial — what this means for AI and NHI governance
Questions worth separating out
Q: How should security teams govern autonomous AI agents that use tools and APIs?
A: Treat autonomous agents as governed identities, not as generic automation.
Q: Why do autonomous AI agents change least privilege requirements?
A: Autonomous agents change least privilege because their access cannot be assumed to stay fixed for the full life of a session.
Q: What breaks when AI agents are given broad standing access?
A: Broad standing access breaks governance because the agent can move from one task to another without a fresh authorization check.
Practitioner guidance
- Inventory all agent-to-tool connections Build a complete map of which autonomous agents can reach which models, APIs, MCP servers, and internal systems, then assign an owner to each path.
- Move authorization checks into the runtime path Require policy enforcement at the AI gateway or equivalent mediation layer so that decisions are checked at the point of use, not only at provisioning.
- Tie audit logs to agent identity and tool use Make every log line answer four questions: which agent acted, what policy allowed it, which tool it used, and what data or system it touched.
What's in the full announcement
Palo Alto Networks' full post covers the operational detail this post intentionally leaves for the source:
- Architecture details on how the AI Gateway sits inside Prisma AIRS and where policy checks are enforced.
- Claims about 99.99% uptime, semantic routing, failover behaviour, and telemetry depth for autonomous workloads.
- Descriptions of centralized artifact management, model access control, and unified access to thousands of LLMs and MCP tools.
- Commercial and integration context for existing Portkey customers after the transaction closes.
👉 Read Palo Alto Networks' intent to acquire Portkey and secure AI agents →
Portkey inside Palo Alto Networks: what changes for AI agent control?
Explore further
06/06/2026 3:38 am
Autonomous agents expose an identity governance problem, not just a tooling gap. The article is really about what happens when a software actor can select actions at runtime across internal and external systems. That behaviour pushes agent identity beyond the boundaries of normal workload governance and into a control plane where authorization must follow the transaction, not the deployment. Practitioner implication: identity programmes need to treat agent behaviour as a first-class governance domain.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- Only 44% of organisations have implemented any policies to govern AI agents, even though 92% agree governance is critical to enterprise security.
A question worth separating out:
Q: Who is accountable when an autonomous agent takes an unsafe action?
A: Accountability should sit with the owner of the agent, the approver of the policy, and the team operating the downstream system. If those responsibilities are not explicit, incident review becomes a blame exercise instead of a control review. The safest model is to predefine ownership before deployment, then validate it through access and audit processes.
👉 Read our full editorial: Palo Alto Networks Portkey acquisition raises the bar for AI agent governance
