Topic starter
30/07/2026 11:21 pm
TL;DR: C1.ai says most identities in enterprise environments are now non-human, and its new agentic security layer is built to detect ownership gaps, misclassifications, shadow accounts, and missing anomaly detection across service accounts, workloads, integrations, and AI agents. Manual review cycles are too slow for identities that appear, act, and disappear at machine speed, so governance has to move to continuous detection and routed remediation.
Editorial analysis by NHI Mgmt Group, based on content published by C1.ai: “Introducing agentic security and intelligence: close identity risk with C1”.
By the numbers:
- Palo Alto Networks' 2026 Identity Security Landscape found organizations now manage 109 machine identities, including AI agents, for every human identity.
- The ratio of machine identities, including AI agents, to human identities rose from 82:1 to 109:1 in one year, according to Palo Alto Networks research cited by C1.ai.
Key questions
Q: What should teams do when an AI agent has no clear owner or business justification?
A: They should suspend or remove the agent until ownership, purpose, and access scope are documented.
Q: Why do AI agents create risk when they fall outside normal identity review cycles?
A: AI agents create risk because they can be provisioned quickly, used widely, and forgotten just as quickly.
Q: What are the signs that machine identity management is failing in an organisation?
A: Common signs include incomplete inventory, spreadsheet based tracking, manual renewal processes, unclear ownership, and repeated certificate expiry events.
Practitioner guidance
- Map ownership for every AI agent and service account Create a required owner field for each non-human identity and block remediation closure until the ownership gap is resolved.
- Prioritise identities with expanded reach Use access graphs to rank machine identities by the apps, accounts, and entitlements they can reach, then triage the highest-impact ones first.
- Route findings into existing governance workflows Send identity findings through the same approval, ticketing, and audit paths used for access changes so remediation is accountable and visible.
Bottom line: AI agents and other machine identities now create governance pressure faster than manual identity review cycles can absorb.
What's in the full announcement
C1.ai's full blog covers the operational detail this post intentionally leaves for the source:
- How agentic security findings are routed across connected systems and queued for action
- The access-graph context behind each flagged identity, including apps, accounts, and entitlements
- The remediation workflow for assigning owners, revoking access, and recording audit evidence
- How imported findings from other tools inherit the same tagging, routing, and audit trail
👉 Read C1.ai's post on agentic security and identity risk detection →
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30/07/2026 11:24 pm
Machine-speed identity growth has broken the assumptions behind manual review. Access review programmes were designed for identities that persist long enough to be observed, certified, and offboarded on a human cadence. AI agents and adjacent non-human identities can be created, assigned, and operational before that cadence ever catches up. The implication is that identity governance must move closer to issuance and runtime state, not depend on retrospective review alone.
A few things that frame the scale:
- Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
- 62% of organisations are at least experimenting with AI agents, but only 23% have scaled one into a single business function, according to McKinsey's 2025 State of AI survey.
A question worth separating out:
Q: What is the difference between identity inventory and identity context?
A: Inventory tells you what identities exist. Context shows what they can access, how they connect across systems, and which entitlements expand their reach. For AI agents and service accounts, context is what turns a list of accounts into a governance decision because it reveals which identities are actually risky.
👉 Read our full editorial: Agentic security for AI agents highlights the NHI risk gap