Why AI agents aren't the same as non-human identities: Rotem Zach, VP of Innovation at Silverfort, explains what sets them apart—and what we can do to enable secure digital environments.
AI is transforming how enterprises operate. From streamlining workflows to supercharging productivity, the promise of AI is already becoming a reality. At the forefront of this evolution are AI agents: software entities capable of executing tasks autonomously, making decisions on the fly, and navigating complex environments with little to no human input.
But with great autonomy comes great complexity, especially when it comes to securing these new types of digital actors. AI agents are neither human nor machine in the traditional sense. They can’t be secured like people, and they don’t behave like traditional non-human identities (NHIs), such as service accounts or OAuth tokens. They represent a fundamentally new category of identity—and a new frontier of risk.
Human users can be secured and monitored with identity security controls like MFA, behavioural analysis, and access governance. Non-human identities are designed for predictable, repetitive tasks, like running scripts, accessing APIs, authenticating backend services. While NHIs present their own identity security challenges, they operate within tight, predefined scopes.
AI agents are different. They’re autonomous. They make their own decisions. They learn. And they can interact directly with the systems we trust most: our email, CRM, codebase, customer data, and more.
That level of access isn’t new. After all, traditional NHIs have held it for years. But AI agents add a new layer of complexity because they can, essentially, think. That means their behaviour isn’t hardcoded or easily forecasted. The same task might lead to a dozen different outcomes depending on inputs, prompts, or model behaviour, which makes preventing unintended consequences significantly more difficult. It's one of the reasons many organisations hesitate to fully embrace the AI revolution.
It’s tempting to lump AI agents in with non-human identities. After all, they’re both ‘non-human’ in a sense. But this overlooks the critical distinctions that make AI agents uniquely challenging and uniquely powerful, and it could lead to costly security mistakes.
As we’ve already discussed, AI agents are autonomous software entities that can reason, adapt, and make decisions based on goals or prompts. They don’t just follow instructions—they interpret them, apply logic, and take action accordingly.
As such, the behaviour of AI agents is dynamic and highly context-driven. They can respond to changing inputs or environments, and their actions may vary even when tasked with the same goal. This unpredictability means AI agents can go off-piste in pursuit of a goal and could take an action it deem helpful, even if that action causes harm.
On the flipside, a traditional NHI will never act unless told to. They are static digital identities designed to perform narrowly defined tasks exactly as instructed, and they won’t deviate from those instructions unless explicitly told to do so. This predictability makes it far easier to identify when an NHI has fallen foul of a malicious actor and is being used for nefarious purposes—assuming, of course, you have visibility into your full NHI inventory.
Interestingly, I’d argue that this is one area where NHIs and AI agents share some key similarities, in that their risks aren’t particularly well understood but fundamentally stem from limited visibility, overprivileged access, and a lack of real-time control over activity.
Still, there are differences. The risks associated with AI agents are closer in nature to the risks posed by human users and arise from their autonomy. They can take unintended actions, misinterpret context, or interact with systems in ways not anticipated by their creators. This even makes them stand apart from other types of AI. A chatbot giving a wrong answer is an inconvenience, but an AI agent sending a sensitive email to the wrong recipient, deleting production data, or granting unintended access is something else entirely.
They can learn, and they can make mistakes. As such, securing them requires governance not only over what systems they can access but also over how they behave once inside those systems.
The lifecycle of an AI agent is dynamic and fluid. An agent’s role, capabilities, or even internal logic may evolve over time, especially if it's trained on new data or integrated into new workflows. This makes traditional lifecycle management approaches, such as static access provisioning or periodic reviews, insufficient.
Non-human identities, by comparison, should follow a relatively straightforward lifecycle. Ideally, they are created for a specific function, used in a predictable manner, and eventually decommissioned. The reality is that this simple lifecycle is difficult to achieve at scale when NHIs are created ad hoc—but that’s a story for another day.
AI agents have the potential to transform how organisations operate. But to realise that potential, enterprises must first trust them, and trust requires control. AI agents aren’t just another kind of machine—they’re something new and they need a new kind of security.
About Rotem Zach
Rotem Zach leads Silverfort’s exceptional research team, which tackles some of the most complex challenges in cybersecurity, authentication, and big data analytics. He joined Silverfort after many years of research and leadership roles at the 8200 elite cyber unit of the Israel Defense Forces. Rotem holds a B.Sc. in Mathematics and Computer Science, Summa Cum Laude, and an M.Sc. in Computer Science from Tel Aviv University.
About Silverfort
At Silverfort, we’re developing a solution purpose-built to observe, analyse and protect the access activity of AI agents with active inline enforcement. Our goal is to help organisations confidently and securely adopt AI agents and enjoy the massive business potential they bring. You can find out more about Silverfort AI Agent Security here.
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