Companies routinely rely on authenticating the identities of humans who access sensitive company data across their networks. There’s an established and accepted set of protocols, done upfront and in a zero-trust approach – either we know who you are and let you in, or you’re locked out. The same framework needs to be applied to AI agents, posthaste.
Companies are increasingly using AI agents developed in-house, or by their vendors, partners, or clients, or, unfortunately, criminals. This can quickly add up to thousands of agents across their infrastructure and supply chains. Non-human identities outnumber human ones 45 to 1 on average in the enterprise and 144 to 1 in cloud-native environments, an increase of 44% in one year, per a recent report.
The scale and speed of this growth are beyond the ability to manually track these agents. And they have limited ability to authenticate these agents’ identities. The risks are growing. Without proper authentication, untrusted agents could access sensitive financial or customer data and share it, purposely or not, with an outside vendor, and thereby expose both the customer and the company to serious legal and compliance risk.
Many companies and security professionals are focusing on what AI agents are allowed to do in terms of permissions. But before doing that, there’s a more fundamental question they need to ask each agent: Whose agent is this? And do I trust the owner/creator? About 68% of security professionals can’t tell legitimate AI agents from malicious actors. And that question isn’t addressed at the growing scale that AI is reaching.
Email solved identity
In email, we have seen a similar story. Emails faced challenges identifying whether a sender was really who they said they were. Due to how it originated, email had no built-in authentication framework. This led to 89% of all phishing attacks having one common denominator: the sender was lying about who they were. To address this, in 2015 my cofounder and I launched a company, Valimail (valid email), to automate the emerging email authentication standard, DMARC. If I received an email claiming to be from Bank of America, DMARC checks the sender’s DNS records to authenticate whether BofA actually authorized it. Today, 100,000s of companies use the DMARC authentication standard for their inbound and outbound email.
Fast forward to today, and these ideas and patents are directly applicable to the world of AI. DMARC doesn’t rely on spotting AI-generated deception. Instead, it uses DNS as the source of truth, which is much harder to falsify.
The agent passport
With AI, we can take an analogous authentication approach. When you travel somewhere, you have a passport and a visa. The passport verifies who you are, and the visa says where you’re allowed to go/what you’re allowed to do. Similarly, today, an agent passport based on Domain Zero trust authentication can identify who the agent is (its provenance) and authorize it for the system that the agent is trying to access. Then systems can verify what an agent is allowed to do (equivalent to a visa). But first, when an agent shows up claiming to represent a vendor, the receiving system verifies its identity by checking that vendor’s DNS, applying a strict zero-trust yes/no authentication – zero ambiguity, and importantly, creating an audit trail to hold the creator accountable for any actions the Agent takes.
Given the “bad” agents outnumber “good” agents 10-100:1, a zero-trust approach focuses on the limited good/approved and the rest (near infinite bad) are rejected. Stakes are High
Without this kind of authentication, many scenarios can unfold. Almost half of employees who are using unapproved AI agents are sharing HR-related information with them and 39% transmit confidential company documents. But only 34% of organizations use the same security controls for agents as they do for human employees.
Consider this: An agent claiming to represent a third-party vendor, such as a marketing automation platform, gets access because no one confirmed it was indeed that approved vendor’s agent making the request. It accesses financial records or customer data, either downloads or modifies records, and by the time this is noticed, the damage is done.
This is a quickly moving risk. Fake invoices can appear to be authentic, and they can be generated at a scale no human fraud operation could match. Authentication used to take a phone call or a manual check. But this now has to happen instantly and automatically, at scale.
Why this matters now
Some may think email authentication is an old problem. DMARC, they say, is a decade-old fix, and the real conversation today is about AI. About 94% of organizations are dealing with increased machine identities, largely due to AI agent deployment, per Gartner, and machine identities now outnumber human accounts.
But the most common form of phishing that occurs today is when AI convincingly pretends to be someone who the target already knows and trusts. The tools for generating that deceptive attack have vastly improved. Yet the proven process for authenticating identity hasn’t kept pace, at least not for agents. This is why identity authentication is no longer optional and is now a precondition for everything else that companies want to safely do with AI agents.
What this doesn’t solve
Identity alone doesn’t solve the whole security problem. In addition, once an agent is authenticated, there are additional systems to verify what the agent is allowed to do and whether the content it produces can be trusted. Those questions come after this one. But both rely on the first one: Who is this?
Reputation
To build a reputation, you need a known identity. Once a system knows which vendor an agent represents, it can start tracking the vendor’s behavior over time and someone becomes accountable for what its agents do. And decide if the vendor should be approved and for which actions (visas). But without strong, repeatable identity authentication of agents, other security and compliance measures for agents can’t function. It’s this foundation that supports the rest of the security stack. You can’t hold an agent accountable or decide what it should be able to do until you know who it actually is. Identity is the foundation on which the rest of AI agent security must be built.