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08/06/2026 4:19 pm
TL;DR: Agentic AI systems build trust through transparency, human oversight, technical guardrails, security controls, and continuous improvement, according to Twine Security’s guide on building confidence in autonomous software. The hard question is not whether agents can be trusted, but which identity controls still assume a human-paced approval model and therefore fail when decisions happen at runtime.
NHIMG editorial — based on content published by Twine Security: Building Trust in Agentic AI
By the numbers:
- Only 5.7% of organisations have full visibility into their service accounts.
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
Questions worth separating out
Q: How should security teams govern agentic AI that can make access decisions at runtime?
A: They should treat agentic AI as a governed non-human identity, not as a simple automation layer.
Q: Why do human approval workflows break down for agentic AI?
A: Because human approval assumes access persists long enough to be reviewed before an action completes.
Q: What is the difference between monitoring an agent and governing an agent?
A: Monitoring tells you what the agent did after the fact.
Practitioner guidance
- Define approval boundaries for agent actions Map which decisions an AI agent may execute autonomously, which require human sign-off, and which remain permanently restricted.
- Require decision traceability for every agent action Log the prompt context, policy inputs, tool calls, and final outcome for any agent that can touch access or identity workflows.
- Treat agent credentials as governed NHI assets Inventory agent identities separately from user identities, then apply lifecycle controls for issuance, review, revocation, and offboarding.
What's in the full article
Twine Security's full blog covers the operational detail this post intentionally leaves for the source:
- The article's five trust-building strategies in full, including how the vendor frames transparency, oversight, safeguards, and continuous improvement.
- The discussion of Alex as a digital employee and how the vendor positions autonomous IAM task execution.
- The vendor's security and compliance claims, including audit trails, role-based access controls, and data protection language.
- The practical framing of how customers can progressively increase delegated responsibility as confidence builds.
👉 Read Twine Security's guide on building trust in agentic AI →
Agentic AI trust and IAM controls: what changes for practitioners?
Explore further
08/06/2026 5:53 pm
Agentic AI turns access governance into runtime governance. The core shift is that identity decisions no longer happen only at provisioning or review time. Once an agent can act during execution, IAM must account for context, tool selection, and outcome as part of the access model itself. That means the governance question is no longer whether the identity is authorised in general, but whether each runtime action stays within the intended boundary. Practitioners should treat agent activity as governed identity behaviour, not as a simple automation layer.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to Ultimate Guide to NHIs.
- 79% of organisations have experienced secrets leaks, with 77% of these incidents resulting in tangible damage.
A question worth separating out:
Q: Who should own agentic AI access risk inside the enterprise?
A: Ownership should sit jointly with IAM, security architecture, and the business team running the agent, because the risk spans identity, policy, and operational intent. If ownership sits only with the AI project team, access controls tend to weaken. If it sits only with IAM, the system context is usually missed.
👉 Read our full editorial: Building trust in agentic AI means rethinking IAM controls
