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02/08/2026 12:14 pm
TL;DR: MCP proxies place a policy and audit layer between AI agents and tools so requests can be routed, validated, logged, and blocked before execution, according to Akto. That matters because agentic workflows expand the blast radius of prompt injection, unauthorized tool access, and data exfiltration faster than conventional IAM assumes.
NHIMG editorial — based on content published by Akto: Agentic AI Security MCP Proxy: Security, Observability and Governance of Enterprise AI
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams govern AI agents that choose tools at runtime?
A: Security teams should treat runtime tool choice as a governed access event, not a normal application call.
Q: Why do AI agents increase non-human identity risk?
A: AI agents increase non-human identity risk because they can execute many actions quickly once they inherit a credential or tool permission.
Q: What breaks when prompt injection is not controlled in agentic workflows?
A: When prompt injection is not controlled, the agent can follow malicious instructions hidden in web content and perform actions the user never intended.
Practitioner guidance
- Enforce proxy-first tool access Route every AI agent request through a single MCP proxy so no server or API is reachable without policy inspection, routing control, and logging.
- Scope permissions by agent role and task Define the smallest usable tool set for each agent identity, then centralise revocation so access can be updated without editing each server configuration.
- Inspect outbound requests for data leakage Scan parameters and payloads before forwarding them to external tools or APIs, especially where prompts could cause the agent to disclose credentials, PII, or internal context.
What's in the full article
Akto's full blog post covers the operational detail this post intentionally leaves for the source:
- Request-flow examples showing how the proxy intercepts, validates, forwards, and logs agent traffic.
- Configuration patterns for policy engines, authentication layers, and protocol adapters in mixed AI stacks.
- Use-case examples for copilots, developer assistants, automation agents, and customer support bots.
- Best-practice examples for restricting tool permissions and monitoring invocation patterns.
👉 Read Akto's analysis of MCP proxy governance for AI agents →
MCP proxy governance for AI agents: are your controls keeping up?
Explore further
02/08/2026 1:16 pm
MCP proxy governance is becoming the practical control plane for agentic AI. Once AI systems can choose tools at runtime, identity and access management has to move from login-centric thinking to invocation-centric thinking. A proxy is not just middleware, it is where authorisation, inspection, and auditability converge for non-human identities. Practitioners should treat agent-to-tool traffic as a governed identity path, not an application side effect.
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, inappropriately sharing sensitive data, and revealing access credentials, 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: How can organisations prove they have control over agent actions?
A: By keeping immutable logs for every request, response, and policy decision, then tying those records to specific agent identities and tool permissions. That evidence supports incident investigations, access review, and compliance questions. If the logs stop at the application boundary, the governance story is incomplete.
👉 Read our full editorial: MCP proxy governance is becoming the control plane for AI agents
