Topic starter
11/08/2026 3:15 pm
TL;DR: Recent incidents show AI agents deleting databases, leaking credentials, and exfiltrating private repository data when they inherit broad access and untrusted context, according to Curity and cited research. The core issue is not model quality but broken privilege assumptions that existing IAM and approval workflows were never designed to contain.
Editorial analysis by NHI Mgmt Group, based on content published by Curity: “5 Agentic AI Security Incidents & Vulnerabilities”.
By the numbers:
- Only 4% of enterprise permissions were used in the last 90 days, according to Curity.
Key questions
Q: What breaks when AI agents are given broad inherited permissions?
A: Broad inherited permissions break the assumption that access is tied to a narrow business need.
Q: Why do agentic workflows increase the risk of indirect prompt injection?
A: Because they blend untrusted content and trusted instructions inside the same execution path.
Q: Should security teams require just-in-time access for AI agents?
A: Yes, when the agent's task is time-bound and the environment can enforce short-lived entitlements.
Practitioner guidance
- Define agent-specific access profiles Inventory every AI agent, the data sources it can reach, and the actions it can trigger.
- Gate destructive actions behind explicit approval Require human approval for database changes, deletions, repository writes, and outbound sharing actions that could affect production or private data.
- Replace standing access with short-lived execution rights Use just-in-time access and zero standing privilege for agents that need temporary access to APIs, repositories, or infrastructure.
Bottom line: AI agents are exposing a basic governance mismatch: persistent human-style permissions are being reused for runtime systems that can take multiple actions without renewed review.
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11/08/2026 3:28 pm
Over-permissioned access is the core governance failure in agentic AI. These incidents do not primarily show model failure. They show what happens when agents inherit human-level authority that was never intended for runtime autonomy. The control problem is therefore identity scope, not model accuracy. Practitioners should read every agent deployment as a privilege design decision.
A few things that frame the scale:
- DeepSeek alone generated 113,000 new exposed API keys in 2025, illustrating how new AI providers create credential exposure before security guardrails catch up, according to the State of Secrets Sprawl 2026.
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: AI agent over-permissioning is exposing production systems and data