Topic starter
24/12/2025 9:52 am
TL;DR: Agentic AI systems can access databases, call APIs, and execute tasks across enterprise workflows, turning passive models into active non-human identities with real privilege, memory, and tool-use risk, according to Noma Security. That shift makes governance, discovery, and least-privilege controls urgent because existing IAM assumptions were built for users, not autonomous software.
Editorial analysis by NHI Mgmt Group, based on content published by Noma Security: “Understanding agentic ai – the shift to ai that acts”.
Key questions
Q: How should security teams govern machine identity credentials in agentic AI environments?
A: Security teams should extend secrets scanning to cover MCP configuration files, enforce short-lived credentials for all agent workloads, and assign clear ownership to every non-human identity regardless of its origin , human-created or AI-generated.
Q: When does agentic AI create more risk than value?
A: Agentic AI creates more risk than value when it can reach sensitive systems without strong task boundaries, when credentials are shared, or when audit trails cannot reconstruct its actions.
Q: What breaks when an AI system can choose tools and actions on its own?
A: What breaks is the assumption that access can be safely provisioned once and reviewed later.
Practitioner guidance
- Inventory every AI agent integration Build an inventory of sanctioned and unsanctioned agents, including business agents, coding agents and embedded assistants, with the tools, data sources and APIs each can reach.
- Scope tools and data per agent type Define separate access boundaries for database access, application APIs, knowledge bases and code execution so authorisation matches the actual function of each agent.
- Vet external tool connections Treat MCP servers, API connectors and other external integrations as privileged dependencies and approve them with the same scrutiny used for third-party access.
Bottom line: Agentic AI changes the identity problem because software can now choose tools, reach data and execute tasks, not just generate content.
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This topic was modified 10 months ago by Abdelrahman
This topic was modified 2 days ago by NHI Mgmt Group
08/10/2026 5:40 pm
Agentic AI expands the NHI problem from credential management to runtime authority. Once a model can remember context, select tools and execute actions, it stops behaving like a passive system and starts behaving like an identity with decision power. That changes the control question from "does it have credentials" to "what can it decide to do with them." Practitioners should read this as an identity governance shift, not just an AI feature shift.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between AI chatbots and agentic AI from an IAM perspective?
A: Chatbots generate responses. Agentic AI can authenticate to tools, execute tasks, and chain decisions across systems. That difference matters because an agent needs identity, privilege, and lifecycle controls, while a chatbot usually does not. If the system can change state in enterprise software, it should be governed like an identity with access risk.
👉 Read our full editorial: Agentic AI is expanding the non-human identity attack surface