Topic starter
30/09/2026 6:28 pm
TL;DR: MCP gateways can filter tools, but AI agent security still requires runtime authorization across the full action chain, including the originator, task context, policy and resource entitlement, according to P0 Security. The governance assumption that access can be decided once at the gateway breaks when identical requests need different decisions at runtime.
NHIMG editorial: based on content published by P0 Security: Controlling agent access beyond MCP gateways
Questions worth separating out
Q: What breaks when AI agents bypass a centralized MCP gateway?
A: When agents bypass a centralized MCP gateway, security controls fragment across notebooks, scripts, and individual servers.
Q: Why do standing privileges increase risk for AI agents?
A: Standing privileges increase risk because the agent keeps a valid path into systems even when the original need has passed.
Q: What are the signs that agent authorisation is too coarse?
A: A common sign is when identical agent requests always receive the same answer even though the originator, task or target resource differs.
Practitioner guidance
- Define runtime authorisation boundaries for agents Map which decisions must be made at execution time rather than at gateway entry, especially where the same request can be safe or unsafe depending on originator and task context.
- Bind agent requests to full action-chain context Capture the originator, agent, task, policy state and underlying entitlement as a single governance record so identical requests can be evaluated differently when context changes.
- Extend zero standing privilege to agent execution Remove persistent privilege where possible, but also require the agent to receive only the minimum runtime entitlement needed for the current action.
What's in the full article
P0 Security's full resource covers the operational detail this post intentionally leaves for the source:
- How the Runtime Access platform evaluates the originator, task context and policy state at execution time
- How blended identity and runtime access control changes authorisation decisions for identical agent requests
- How the platform presents discovery, control and audit trail functions across agents, users and machines
👉 Read P0 Security's resource on controlling AI agent access beyond MCP gateways →
AI agent access beyond MCP gateways: what changes for IAM teams?
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30/09/2026 6:28 pm
Runtime authorisation, not gateway filtering, is the new control boundary for AI agents. The article is pointing to a governance boundary shift that many IAM teams have not yet internalised. If the decision is made only at the gateway, identical agent requests are treated as equivalent even when origin, task and resource entitlement differ. Practitioners should treat runtime decisioning as the authoritative control plane for agent access.
A few things that frame the scale:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should IAM teams govern AI agents as identity programmes mature?
A: Treat AI agents as identities that need discovery, entitlement boundaries, and continuous oversight. They do not wait for ticket queues or static review cadences, so governance has to adapt to runtime behaviour. The practical test is whether the programme can control access at machine speed without relying on manual approval loops.
👉 Read our full editorial: Controlling AI agent access requires runtime authorization beyond MCP gateways
