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.
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.
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:
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.
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.
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.
There are four key steps that can put your IAM and PAM on the right path to success with machine identities:
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:
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.
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.