Agentic AI security gaps: what happens when adoption outruns governance?

TL;DR: Enterprise AI adoption is rising faster than governance, with agentic workflows exposing two gaps that hurt organisations most: unsafe AI supply chains and runtime control failures, according to Jozu's webinar recap. The old stack fails because policy, review, and gateway models were built for slower, deterministic systems, not agents that request tools and act at machine speed.

Editorial analysis by NHI Mgmt Group, based on content published by Jozu: “Your Organization Is Further Along the AI Curve Than You Think. That’s the Problem.”.

By the numbers:

• 40% of enterprise applications will embed AI agents by the end of 2026, up from 5% in 2025.
• Documented AI failures, attacks, and misuse rose more than 55% in 2025 alone.

Key questions

Q: What should IAM and security teams do first when AI adoption accelerates?

A: They should start with a unified inventory of sensitive data and the tools that can reach it.

Q: Why does AI make software supply chain risk harder to control?

A: AI increases the amount of code produced, which reduces the time available for review and makes malicious or unauthorized changes harder to spot.

Q: Where do traditional AI gateways and guardrails fail in agentic environments?

A: They fail where the action actually happens.

Practitioner guidance

• Map the real highest-risk AI first Identify which AI systems are already handling sensitive data, tool calls, or workflow automation, then compare that reality with the approved programme boundary.
• Define the next 12-month operating state Set the access, logging, and approval model you will need once adoption expands, not the one that only fits today's pilot footprint.
• Bring security, data science, and platform teams together Align on what acceptable agent behaviour looks like, who approves new artifacts, and which runtime actions must be blocked regardless of user demand.

Bottom line: Agentic AI changes the control problem from software security to identity governance, because the actor can invoke tools and move across systems at runtime.

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