Artificial intelligence is influencing every area of technology, and identity security is no exception. Non-human and agentic identities have become commonplace, with the creation of machine identities outpacing human identities at a ratio of 82:1, according to CyberArk.
Their rapid rise has made identity security the new perimeter of AI control: if you control the identity, then you naturally control the AI, too. This mitigates the potential security risk of AI and allows businesses to embrace AI with confidence, unlocking greater speed, scale, and innovation in the process.
This blog explores non-human and agentic identities and how Identity Access Management (IAM) and Privileged Access Management (PAM) are key to enabling safe, sustainable impact from them. We’ll highlight the risks and opportunities involved, why IAM and PAM approaches need to evolve to accommodate these identities, and practical steps for getting started.
What are non-human and agentic identities?
First, let’s distinguish between non-human and agentic identities. Although both should be treated as identities because they authenticate and act, they aren’t one and the same.
Non-human identities authenticate to allow a predetermined action to be executed. Typical examples of non-human identities include applications, workloads, APIs, RPA bots, microservices, cloud services, IoT, service accounts, certificates, and secrets.
Agentic identities, on the other hand, decide what work needs to be done next. These AI agents act autonomously to initiate tasks and adapt to context — for example, AI agents that trigger workflows, analyze data, and take action across systems with limited or no human oversight. These agents need much wider access than non-human identities, which means they can achieve much more — but with a potentially higher level of risk.
Non-human and agentic identities: The risks
The creation of machine identities is occurring at such a fast pace. Organizations now have so many identities (of all types) to handle, that it’s becoming extremely time-consuming and complex. But this isn’t the only headache that machine identities can bring. Issues also commonly arise due to:
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Process mapping: Machine identities don’t naturally or cleanly map to joiner/mover/leaver processes. This makes it harder to clarify ownership, maintain control, and understand whether the access is still being used — as well as whether it’s still required. Not having this clarity means access may end up being provisioned for longer than required, creating unnecessary and potentially risky access points into your network.
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Speed of progress: As agentic identities move at machine speed, any problems that arise through poor management can spread far faster than they would in equivalent human identities
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Unintended vulnerabilities: Agentic identities introduce the possibility of lateral movement or privilege escalation without an attacker, because the identity is optimizing its path to a result.
Non-human and agentic identities: The opportunities
Although non-human identities have been around for years, agentic identities are a game-changer, as their autonomy can increase speed, reach, and also complexity.
The key is good management of all types of identities simultaneously, and agentic identities in particular. If an organization can manage agentic identities well, they’re better-placed to deploy AI with confidence, and use it to innovate, boost productivity and improve efficiency. But just as importantly, they’ll be able to do so without introducing more risk than is comfortable.
Organizations that don’t have strong management in place may have to restrict their AI ambitions — or take on a very high level of risk to match their competitors.
How do IAM and PAM work together for non-human and agentic identities?
With IAM governing identity and accountability, and PAM governing privileged access and secrets, they collectively deliver the scalable guardrails that allow AI and automation to expand without introducing unnecessary risk.
The division of responsibility is simple: IAM takes care of ownership, purpose, policy, approval logic and certification, while PAM covers secrets, privileged elevation, session control, rotation, and just-in-time workflows. Together, they enable less friction, more control, better auditability, and safer automation.
Traditional IAM and PAM approaches need to evolve
The current IAM and PAM strategies many organizations have in place aren’t mature enough to handle the management of these types of identities. While most organizations have the technology they need, they lack the visibility and operating model to deliver IAM and PAM effectively.
A hands-on, proactive approach is needed to manage non-human and agentic identities effectively, starting with discovery and classification. This means moving identities from “unknown and unmanaged” to “discovered and owned”, including non-persistent identities that are subject to continuous validation.
Organizations must also treat governance as a constantly evolving process because AI agents operate at machine speed. If they’re given permanent access with long-lived credentials, any error or compromise can escalate instantly — often before humans even realize something is wrong. This makes regular reviews essential as well as relevant accounts being created on execution and removed after tasks are completed.
Steps for getting started — and pitfalls to avoid
There are four key steps that can put your IAM and PAM on the right path to success with machine identities:
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Start with visibility: Discover and make an inventory of machine identities across cloud, DevOps, apps, and infrastructure, with full categorization of service accounts, API keys, certificates, workloads, bots, and agents.
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Define the operating model: Define ownership and purpose by assigning accountable owners and establishing why each identity exists, what it can access, and what “good behavior” looks like.
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Reduce risk: Remove static secrets and standing privileges by vaulting and rotating credentials, replacing hard-coded secrets, and applying least-privilege and time-bound access.
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Scale in step with governance: Extend governance through continuous validation — including certification and reviews for machine identities, policy-based access decisions, and monitoring that reflects how machines actually behave. Frameworks like NIST, ISO 27001, and NIS2 can also help as guardrails here.
These steps are based around principles of discovery, risk reduction that support speed, and technical controls that are paired with clear accountability. In turn, those principles can also help you avoid some of the common mistakes that organizations make in this process, many of which can leave major blind spots and vulnerabilities:
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Poor lifecycle models that treat machine identities like humans
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Hard-coded, unrotated and long-life access — often deployed and forgotten — that leaves key secrets unmanaged
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A lack of ownership and audit trail leads to an assumption that DevOps can self-govern
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Standing privilege and broad access that allows AI to be over-provisioned for convenience
In summary: Enabling an innovative future with machine identities
Non-human identities will continue to outnumber humans by a wide margin, which means the long-term goal must be safe adoption of them, rather than restriction. With the future bringing more identities, more autonomous workflows, and machines that create more machines, proactively managing non-human identities will be essential.
In this context, governance needs to be real-time, with continuous validation, and IAM and PAM converging as an “identity fabric”. Organizations that modernize governance of machine identities now will be better placed to move faster later and to scale AI safety and competitively.
Working with an expert partner like Turnkey Consulting can help you define governance, integrate IAM/PAM controls, and establish an operating model that supports automation and AI. Contact us to find out more and try a no-obligation assessment to give you clarity on your scale, risk exposure, and priorities.
Frequently asked questions: Non-human and agentic identities
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Are all non-human and agentic identities privileged?
Not necessarily. Some non-human identities perform narrow, low-risk tasks with limited access. But in practice, many of them become privileged because they interact directly with systems, data stores, APIs, and infrastructure, and therefore need elevated permissions to function effectively.
Agentic identities, as they operate autonomously, and may initiate actions, trigger workflows, or access multiple systems, should be treated as privileged by design. This means the safest mindset is to assume least privilege, just-in-time access, and continuous monitoring and reviews as a matter of course. -
Can AI and agentic systems really escalate their own access?
Yes. This doesn’t happen maliciously, but it can happen unintentionally as they act in pursuit of an outcome or tasks — for example, identifying faster or broader paths to the data they need or exploiting overly broad or permanent permissions.
This can create new risk patterns, such as lateral movement without an attacker or privilege expansion without human intent, which traditional security tools may not detect as anomalous behavior. For this reason, proactive identity governance, based on dynamic access decisions, and guardrails that operate at machine speed, are critical. -
How can organizations start governing non-human and agentic identities without slowing innovation?
The starting point is visibility rather than restriction. This means understanding what exists in terms of non-human identities across cloud, DevOps, automation, and legacy systems, before trying to control it. After this, you can go on to:
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Treat non-human identities as first-class identities, with assigned ownership and purpose, and governance expectations applied consistently.
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Shift from static to dynamic access models, with just-in-time access, zero standing privilege, and context-aware authorization.
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Embed governance into automation and DevOps workflows, avoiding manual approval bottlenecks, and letting controls operate automatically at machine speed.
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Use IAM for lifecycle, ownership, and policy, and PAM for privilege elevation, secrets management, and session control.
It’s also important to note that maturity is incremental. You don’t need to try and solve everything at once, as each step gradually reduces risk while preserving agility.
