Securing AI Requires Moving NHI Into The Three Pillars of Identity

The rise of AI is fundamentally changing everything we thought we knew about cybersecurity. As AI systems become more autonomous and pervasive, the traditional idea of a "Non-Human Identity" (NHI) - things like a service account or an API key - just doesn't cut it anymore. It's a foundational concept, sure, but it's only one piece of a much bigger, more complex puzzle: AI Identity Security.

To really get a handle on this, we have to stop seeing AI identities as simple, monolithic things. They are not. They are multifaceted and they require a completely new way of thinking, built on three distinct, yet deeply interconnected pillars: the Identity of the Model, the Identity of the Action, and the Identity of the Purpose.

Pillar 1: The Identity of the Model 

At its most basic, every AI system starts with a model. This model is the brain, trained on massive datasets, fine-tuned over countless iterations and finally deployed to do a specific job. The Identity of the Model is essentially the digital fingerprint of this entire intellectual property.

This covers:

• Training Data Provenance: Where did the data come from? Was it clean? Is it biased? Securing the data supply chain is paramount to trust in the model.
• Model Versioning: Each iteration of a model carries distinct characteristics and potential vulnerabilities. Robust version control is crucial for auditing, rollback, and identifying compromised versions.
• Ownership and Lineage: Who built, trained, and deployed this specific model? This ties back to human accountability, even for autonomous systems.
• Integrity Verification: Ensuring the model itself hasn't been tampered with, that no malicious code or poisoned data has been injected post-training or during deployment.

A compromised model identity is a serious threat because it can lead to data poisoning attacks, where malicious data is fed into the training process, causing the AI to make incorrect or even harmful decisions. It also opens the door to model evasion, where attackers manipulate inputs to bypass the model's intended security functions. And, of course, there's the risk of intellectual property theft - unauthorized replication/exploitation of your proprietary AI models, which can be devastating for your business.

Pillar 2: The Identity of the Action

An AI model isn't just a static thing. It's a doer. It's constantly making decisions and interacting with other systems. The Identity of the Action is a transient, hyper-specific identity that an AI gets for a single task. It's less about "what the AI is" and more about "what it's doing right now."

This covers:

• Just-in-Time Privileges: Access is granted only for the duration of a single, defined task, then immediately revoked. This aligns perfectly with Zero Trust principles.
• Contextual Authorization: The identity and its associated permissions are dynamic, adapting to the specific context of the action (e.g., time of day, data being accessed, originating request).
• Micro-segmentation of Capabilities: Instead of giving an AI broad "read" access to a database, it receives an identity that allows it to "read customer address for shipping label generation" for one transaction.

Traditional NHIs often have standing permissions, which is a problem because a single compromise can affect a huge part of your system. The Identity of the Action helps fix this by minimizing the attack surface and limiting an attacker’s window of opportunity. It also provides a detailed record of every autonomous action the AI takes, which makes it much easier to figure out what went wrong. And crucially, it also prevents lateral movement.

Pillar 3: The Identity of the Purpose

Finally, we need to consider the "why." Why is the AI doing what it's doing? The Identity of the Purpose is a kind of meta-identity that links an AI's actions back to a sanctioned business objective. It's the big picture, the reason for being, and it allows for something that traditional security has never had: truly risk-based authorization.

This covers:

• Business Justification: Every action an AI takes should ultimately trace back to an approved business goal or policy (e.g., "detect fraud," "optimize supply chain," "respond to customer query").
• Risk Context: High-impact or sensitive operations trigger a higher-risk "Purpose Identity," demanding more stringent oversight and authentication checks.
• Compliance Alignment: The purpose identity ensures that AI actions remain within regulatory boundaries (e.g., GDPR, HIPAA).
• Human Oversight Link: It establishes the ultimate human accountability for the AI's intent, even if the execution is autonomous.

The Identity of the Purpose is critical for preventing "runaway" AI, ensuring that autonomous systems don't deviate from their intended, approved functions. It also enables risk-adaptive security, allowing your security policies to dynamically adjust based on the criticality of the AI's purpose. Finally, it strengthens governance, providing a framework for continuous monitoring and auditing against established organizational goals and compliance requirements.

AI Identity Security isn’t just an evolution of what we have known with non-human identities, it’s a whole new paradigm. It’s about reframing how we think about trust in systems that can act, decide, and even learn on their own. Models, actions, and purposes together form a living, breathing ecosystem of identity that needs to be secured at every layer. If we treat AI like any other system account or API key, we will miss the nuances that make it uniquely powerful - and uniquely vulnerable. Ignore that, and you invite chaos. Get it right, and you create clarity, accountability, and resilience in a landscape where AI is no longer optional, it’s inevitable.