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14/08/2026 6:34 pm
TL;DR: Machine and service identities now outnumber human users 109:1, and organisations anticipate 85% growth in AI agent adoption over the next year, according to Zero Networks, while IBM reports fewer than a third extend granular access controls and zero trust to NHI. Standing access, weak visibility, and lateral-movement risk are becoming the default failure mode for identity programmes.
NHIMG editorial — based on content published by Zero Networks: Machine Identity Security: How to Protect Service Accounts and AI Agents
By the numbers:
- Machine and service identities now outnumber human users 109:1.
- Organisations anticipate 85% growth in AI agent adoption over the next year.
Questions worth separating out
Q: What breaks when hybrid-cloud service accounts are over-privileged?
A: Over-privileged hybrid-cloud service accounts let attackers reuse trusted access paths instead of escalating from scratch.
Q: Why do non-human identities complicate zero trust architecture?
A: Because zero trust assumes access can be verified continuously, yet many machine identities are created for automation, reused widely, and left in place long after their original purpose ends.
Q: What do security teams get wrong about AI access risk?
A: Many teams focus on the model while ignoring the identity path that reaches it.
Practitioner guidance
- Discover every service account and AI agent Build continuous discovery that maps identities, tools, and network connections so shadow AI and forgotten service accounts are not invisible to the IAM programme.
- Scope access to observed operational need Use a learning period to identify the actual assets and logon types each identity requires, then remove any privilege that is not supported by observed behaviour.
- Enforce identity-based segmentation Restrict service accounts and AI agents to pre-approved assets and logon types so a compromised credential cannot reach sensitive systems by default.
What's in the full article
Zero Networks' full article covers the operational detail this post intentionally leaves for the source:
- Step-by-step discovery and learning-period workflow for mapping every identity, asset, and connection in the environment.
- Operational examples of how a deterministic policy engine turns observed behaviour into least-privilege rules.
- Implementation detail on identity-driven microsegmentation for service accounts and AI agents across changing network conditions.
- The source article's framing of how Zero Networks positions staged rollout and simulation before enforcement.
👉 Read Zero Networks' analysis of machine identity security for service accounts and AI agents →
Machine identity security and AI agents: are your controls keeping up?
Explore further
14/08/2026 6:41 pm
Standing privilege is the core machine-identity failure mode. Service accounts and AI agents are most dangerous when access outlives the business need that created it. That is not a visibility problem alone. It is a governance failure in how organisations treat non-human access as persistent infrastructure instead of scoped identity.
A few things that frame the scale:
- Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them, according to Ultimate Guide to NHIs.
- 79% of organisations have experienced secrets leaks, and 77% of those incidents resulted in tangible damage, according to Ultimate Guide to NHIs.
A question worth separating out:
Q: Who is accountable when a machine credential is abused?
A: Accountability should sit with the team that owns the workload, the identity lifecycle, and the connected business process, not with security alone. In regulated environments, that usually means engineering, platform, and IAM teams share responsibility for discovery, rotation, and offboarding while compliance verifies that the process is repeatable.
👉 Read our full editorial: Machine identity security gaps are widening as AI agents scale
