Independent reference. Not affiliated with any zero trust vendor. Updated Q2 2026.
ZeroTrustCost
Emerging cost area

Zero trust for AI cost: securing agents and machine identities

Autonomous AI agents are a new class of identity, and they already outnumber human users many times over. Extending zero trust to cover them is the fastest-growing new line in security budgets, but it does not price like the per-user controls that came before. This page covers the new budget lines AI adds, how non-human identity tooling is actually priced, and how to size the uplift on an existing zero trust programme. Figures dated June 2026 with sources at the foot of the page.

Why now

Machine identities already outnumber humans

The case for AI zero trust is a scale problem. Per-user controls cannot cover a population growing this fast.

100:1+
Machine-to-human identity ratio in nearly half of organisations (up to 500:1 in some sectors)
~85%
Of enterprises already using agentic AI
$11B to $27B
Non-human identity security market, 2025 to 2033 (about 11.9% CAGR)
The new budget lines

What AI adds on top of the five pillars

Securing agents does not replace classic zero trust; it adds controls for non-human identities on top of it. Five new cost centres, each priced by consumption.

New controlWhat it doesTypical pricing model
NHI governanceDiscover, own and lifecycle-manage service accounts, API keys, bots and agents.Per identity / entity
Secrets managementVault and rotate the credentials agents use; eliminate hardcoded secrets.Per secret / per entity
AI / LLM access gatewayBroker and inspect agent calls to models, tools and APIs.Per workload / consumption
Agent behavioural monitoringBaseline normal agent behaviour and flag deviation in real time.Per agent / consumption
Certificate + ephemeral credential automationIssue and rotate short-lived agent identities at machine scale.Per certificate / volume

Pricing models reflect how leading non-human identity and secrets platforms (for example HashiCorp Vault, Aembit, Akeyless) license: by entity, secret, workload or consumption rather than per user.

Sizing it

How to budget the AI uplift

Treat AI zero trust as an uplift on the identity pillar, scaled by how many agents and machine identities you actually run.

Start from your identity-pillar spend. Identity is already 30 to 40 percent of a zero trust budget. As a planning rule of thumb, budget an additional 10 to 25 percent of identity-pillar spend to extend the same controls to machine and agent identities, lower if you run few agents and have clean service-account hygiene, higher if you have sprawl and heavy agentic adoption.

Drive it by identity count, not headcount. The cost scales with the number of non-human identities, secrets and workloads, not the size of the workforce. An organisation with 200 employees but thousands of service accounts and agents will pay far more than the headcount implies.

Front-load discovery. The cheapest and highest-leverage spend is inventory and clean-up: finding hardcoded and over-privileged credentials and removing them. This reduces both risk and the consumption-based licence count before you pay to govern what remains.

Defer the heavy tooling. Behavioural identity and full certificate automation are the most expensive components. They pay back only once foundational NHI hygiene and secrets management are in place, the same phase discipline that governs classic zero trust.

Related

Build on the foundation first

AI zero trust is an uplift, not a standalone purchase. Price the foundation, then the agent layer.

Identity pillar cost
The pillar AI zero trust extends
Identity fabric cost
Unifying human + machine identity
NIST 800-207 cost
The framework AI zero trust extends
Cost calculator
Size the foundational programme
Five pillars
The classic zero trust cost base
Vendor framework
Evaluate the platforms
Frequently asked

Zero trust for AI cost questions

What does zero trust for AI cost?
There is no settled per-agent list price yet; this is an emerging category and pricing is consumption-based, not per-user. The cost is best understood as an uplift on an existing zero trust programme: new budget lines for non-human identity (NHI) governance, secrets management, an AI or LLM access gateway, agent behavioural monitoring, and certificate lifecycle automation. For most organisations these are priced by consumption (number of machine identities, secrets, workloads or tokens) rather than by headcount. As a planning rule of thumb, organisations already running zero trust should budget an additional 10 to 25 percent of identity-pillar spend to bring autonomous agents and machine identities under the same controls, scaling with how many agents and service accounts you actually run.
Why does AI break traditional zero trust pricing?
Zero trust licensing has historically been priced per human user. AI agents and machine identities are not users, and they already outnumber humans heavily, surveys put machine-to-human ratios above 100:1 in nearly half of organisations and as high as 500:1 in some sectors. Pricing a population that large on a per-seat model does not work, so the tools that secure non-human and agent identities (HashiCorp Vault, Aembit, Akeyless and similar) price by consumption: number of entities, secrets, clients or workloads. Budgeting for AI zero trust means switching from a per-user mental model to a per-identity or per-workload one.
What new cost lines does securing AI agents add?
Five. (1) Non-human identity governance, discovering, owning and lifecycle-managing service accounts, API keys, bots and agents. (2) Secrets management, vaulting and rotating the credentials agents use, priced per secret or per entity. (3) An AI or LLM access gateway that brokers and inspects agent calls to models and tools. (4) Agent behavioural monitoring, baselining what an autonomous agent normally does and flagging deviation. (5) Certificate and ephemeral-credential lifecycle automation, since certificates are the practical backbone of agent identity at scale. These sit on top of, not instead of, the five classic zero trust pillars.
Is agentic zero trust a real framework or marketing?
It is an extension of NIST SP 800-207, the same standard the rest of zero trust is built on, applied to autonomous AI systems. Research formalised in 2026 describes additional control patterns for agents (token isolation, agent persona, and behavioural identity) on top of the standard verify-explicitly and least-privilege principles. The underlying idea is uncontroversial: an autonomous agent that sets its own goals and calls tools and APIs without supervision needs an identity, least-privilege access, and continuous verification, exactly like a human user, but at machine scale and machine speed.
How big is the non-human identity security market?
Large and growing fast. The non-human identity access management market was estimated at about $11.14 billion in 2025 and is projected to reach roughly $27.33 billion by 2033, a compound annual growth rate of about 11.9 percent. The driver is scale: machine identities multiply with every microservice, API, bot and now AI agent, and with about 85 percent of enterprises already using agentic AI, the population of identities that need zero trust controls is expanding far faster than the human workforce.
Where should we start with zero trust for AI?
Discovery first, the same as any zero trust programme. You cannot govern machine identities you cannot see, and most organisations badly underestimate how many service accounts, API keys and agent credentials they already have. Inventory non-human identities, kill the hardcoded and over-privileged credentials (the most common and cheapest-to-fix risk), then bring the survivors under secrets management and least-privilege access. Add an AI access gateway and behavioural monitoring once agents move from pilot to production. Defer the heavier behavioural-identity and certificate-automation tooling until the foundational NHI hygiene is in place.
Sources
  1. Cequence, Agentic Zero Trust (definition and research, extension of NIST SP 800-207 to autonomous agents). https://www.cequence.ai/agentic-zero-trust/
  2. HashiCorp, Zero trust for agentic systems: managing non-human identities at scale. https://www.hashicorp.com/en/blog/zero-trust-for-agentic-systems-managing-non-human-identities-at-scale
  3. Grand View Research, Non-Human Identity Access Management Market ($11.14B 2025, $27.33B 2033, 11.9% CAGR). https://www.grandviewresearch.com/industry-analysis/non-human-identity-access-management-market-report
  4. The Hacker News, The Non-Human Identity Crisis (machine-to-human ratios above 100:1). https://thehackernews.com/expert-insights/2026/05/the-non-human-identity-crisis-why-your.html
  5. Cisco, Zero Trust for the Agentic AI Workforce. https://www.cisco.com/site/us/en/solutions/artificial-intelligence/security/securing-agentic-ai/index.html

Market and adoption figures are sourced as cited. Pricing models reflect how leading non-human identity platforms license. The 10-25% identity-pillar uplift is a planning rule of thumb for an emerging category, not a vendor quote. Accessed and verified June 2026.