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03/06/2026 11:01 am
TL;DR: Palo Alto Networks closed its acquisition of Portkey to extend Prisma AIRS with an AI gateway control plane for monitoring, orchestrating, and governing autonomous agents, while also adding agent identity security and runtime protection for AI traffic, according to Palo Alto Networks. The core issue is not model quality but the trust gap created when AI can act independently and use tools without human approval.
NHIMG editorial — what this means for NHI practitioners
Questions worth separating out
Q: How should security teams govern autonomous AI agents that can use tools at runtime?
A: Treat each agent as a governed identity with a defined tool scope, data reach, and interruption point.
Q: Why do autonomous AI agents complicate least privilege models?
A: Least privilege is usually assigned before execution and reviewed after the fact, but autonomous agents can decide, act, and complete work within one session.
Q: What breaks when AI gateway controls are treated like ordinary API security?
A: Ordinary API security assumes stable clients and predictable request paths.
Practitioner guidance
- Map AI gateway functions to identity controls Identify which gateway functions enforce authentication, authorization, logging, policy, and interruption of unsafe requests.
- Classify autonomous agents as governed identities Create an inventory that records each agent, its tool set, its data reach, and its approval boundaries.
- Review policy for runtime scope drift Test whether an agent can move from an approved request to an unapproved action within the same session.
Teams should pair gateway policy with lifecycle governance and review whether their current approval model still makes sense when execution is measured in seconds rather than sessions?
👉 Read Palo Alto Networks' announcement on the Portkey acquisition and Idira →
Explore further
03/06/2026 11:04 am
AI gateway governance is becoming the identity boundary for autonomous systems. Once an agent can choose tools and execute without human approval, the control point moves from endpoint or app policy into the gateway itself. That shifts the governance burden from static access assignment to runtime mediation, and it places agent identity at the centre of enterprise control design. Practitioners should treat this as a category change, not a feature addition.
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 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: Who is accountable when an autonomous agent misuses access or exposes data?
A: Accountability should sit with the team that owns the agent lifecycle, policy, and runtime enforcement, not with the agent itself or with the model provider alone. If multiple groups share the control plane, they still need one named governance owner for recertification, monitoring, and incident escalation. Otherwise, the gap becomes a governance failure, not a technical one.
👉 Read our full editorial: Palo Alto Networks acquisition of Portkey reframes AI gateway governance
