Beyond Human Profiles: Defining Identity for Autonomous AI

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Author: Denis Avetisyan

As artificial intelligence moves beyond assistance and into true agency, current identity systems prove inadequate, demanding a new framework built on verifiable behavior and declared intent.

This review identifies critical gaps in AI identity infrastructure and proposes a shift towards agent governance utilizing Decentralized Identifiers, Zero-Trust Architecture, and Verifiable Credentials.

As AI agents increasingly operate autonomously across organizational boundaries, current identity infrastructures-built for embodied, legally-defined entities-prove fundamentally inadequate. This report, ‘AI Identity: Standards, Gaps, and Research Directions for AI Agents’, defines AI identity not as a static attribute, but as a continuous relationship between an agent’s declared intent and observed behavior, revealing critical asymmetries with human identity across substrate, persistence, verifiability, and legal standing. Our analysis identifies five structural gaps-spanning semantic intent verification to operational sustainability-that neither existing technology nor regulation adequately addresses, suggesting that simply more engineering will not suffice. Can foundational research into AI identity unlock the governance mechanisms required for trustworthy, boundary-crossing autonomous systems?

The Eroding Foundations of Digital Trust

The rapid emergence of AI Agents presents a fundamental challenge to existing security and accountability structures, quickly outpacing the capabilities of traditional identity frameworks designed for human actors. These frameworks rely heavily on concepts like legal personhood, biometrics, and established reputations – attributes inherently absent in autonomous, non-biological entities. Consequently, verifying the origins, intentions, and actions of AI Agents becomes significantly more complex. Existing systems struggle to address issues like spoofing, malicious code injection, or unpredictable emergent behavior, creating vulnerabilities in applications ranging from financial transactions to critical infrastructure. This necessitates a re-evaluation of how identity is defined and managed in a world increasingly populated by intelligent, self-governing machines, demanding novel approaches to authentication, authorization, and ongoing monitoring.

Existing identity systems are fundamentally designed around the attributes of human beings – biometrics, legal frameworks built upon personhood, and established patterns of behavior tied to social context. These systems falter when applied to AI Agents, which lack biological markers, operate outside traditional legal definitions, and exhibit autonomy that complicates accountability. An AI Agent’s ‘identity’, if it can be called that, is constructed from code and data, presenting a fluid and mutable profile vastly different from a fixed human identity. This mismatch creates significant challenges in verifying an agent’s origins, intent, and ongoing trustworthiness, as conventional methods relying on human-centric assumptions prove inadequate for assessing non-biological entities capable of independent action and learning. Consequently, the very foundations of trust, built upon recognizing and verifying who is performing an action, become unstable when the actor is an AI Agent operating outside established identity paradigms.

A significant trust deficit currently impedes the widespread adoption of AI Agents, particularly within critical applications demanding reliability and security. This isn’t simply a matter of technological refinement, but a fundamental inadequacy in existing systems designed around human identity. A recent report details five core structural gaps contributing to this issue: the inability to reliably verify the semantic intent behind an agent’s actions, a lack of accountability in recursive delegation – where agents delegate tasks to other agents – compromised agent identity integrity leaving them vulnerable to manipulation, governance opacity obscuring decision-making processes, and concerns surrounding long-term operational sustainability. These deficiencies collectively demonstrate that current technologies and standards are insufficient for establishing the necessary confidence in autonomous AI entities, hindering their potential benefits across diverse fields.

Constructing Verifiable Agency

Authentication is the fundamental process of verifying the claimed identity of an agent within a system. This involves presenting evidence, often credentials, that substantiate the agent’s assertions. Successful authentication establishes trust by confirming that the agent possesses the necessary permissions or attributes to perform a specific action or access resources. Common authentication methods include password-based systems, multi-factor authentication, and increasingly, cryptographic methods utilizing public and private key pairs. The strength of authentication relies on the robustness of the presented evidence and the security of the verification process against spoofing or compromise. Without reliable authentication, systems cannot reliably determine who or what is initiating a request, creating significant security vulnerabilities.

Decentralized Identifiers (DIDs) represent a new approach to digital identity, enabling individuals and entities to control their own identifiers without reliance on centralized authorities. Unlike traditional identifiers which are often managed by a single provider, DIDs are cryptographically verifiable and globally unique, resolving to a DID Document containing public keys and service endpoints. These identifiers are designed to be persistent, meaning they are not tied to any single organization or system. DID methods define how DIDs are created, updated, and resolved, and can leverage various underlying technologies, including blockchains, distributed hash tables, and peer-to-peer networks. This self-sovereign nature allows agents to selectively disclose verifiable credentials, enhancing privacy and interoperability across different systems and applications.

Zero Knowledge Proofs (ZKPs) are a cryptographic method enabling one party (the prover) to demonstrate the truth of a statement to another party (the verifier) without conveying any information beyond the validity of the statement itself. This is achieved through a challenge-response protocol where the prover demonstrates knowledge of a secret without revealing the secret. In agent identity systems, ZKPs facilitate selective disclosure of attributes; for example, an agent can prove they are over 18 without revealing their exact date of birth or other identifying details. This minimizes data exposure, enhancing both privacy and security by reducing the attack surface and limiting potential data breaches. ZKPs rely on complex mathematical problems to guarantee proof validity and prevent forgery, with implementations like zk-SNARKs and zk-STARKs offering varying trade-offs between proof size and verification speed.

Beyond Permission: The Architecture of Continuous Trust

Traditional authorization systems rely on static permissions granted to an agent, which are often insufficient for modern, dynamic environments where access requirements change rapidly. Continuous Access Evaluation (CAE) addresses this limitation by providing real-time verification of an agent’s access rights before granting access to a resource. Unlike static checks performed at login or initial connection, CAE continuously monitors contextual factors – such as user behavior, device posture, threat intelligence, and data sensitivity – to dynamically adjust access privileges. This ongoing assessment allows organizations to revoke access immediately if conditions change, mitigating the risk of unauthorized access and data breaches. Implementation typically involves integrating authorization policies with real-time monitoring and analytics engines, enabling automated and adaptive access control decisions.

Semantic Intent Verification operates by analyzing the meaning and context of an agent’s actions, rather than solely relying on permitted access rights. This process involves comparing the observed behavior against the originally defined objectives and constraints of the agent’s task. By establishing a link between the intent behind a request and the actual execution, systems can detect and potentially block actions that, while technically permissible, deviate from the expected purpose. This is particularly relevant in complex, multi-step workflows where an agent’s intermediate actions could inadvertently lead to unintended outcomes, even if each individual step doesn’t violate pre-defined permissions.

Audit logging systems capture a detailed, time-stamped record of all agent actions, including data accessed, commands executed, and decisions made, which is essential for post-incident forensic analysis and demonstrating compliance with regulatory requirements. Complementing this, Trusted Execution Environments (TEEs) – such as Intel SGX or ARM TrustZone – provide a secure enclave for agent code execution, isolating it from the broader system and protecting code integrity against modification or unauthorized access. This combination ensures both a record of what an agent did and verification that the agent operated using authorized, unmodified code, bolstering accountability and trust in automated processes.

The Model Context Protocol (MCP) is an emerging standard designed to normalize interactions between agents and external resources, specifically tools and data. This standardization aims to improve both interoperability – allowing different agents to seamlessly utilize the same resources – and security by establishing a consistent framework for access control and data exchange. However, current organizational visibility into agent communications remains limited, with only 47.1% of organizations reporting full visibility. This lack of transparency presents a significant risk, hindering effective monitoring, auditing, and incident response capabilities related to agent activity.

Towards a Resilient Ecosystem: The Dynamics of Verifiable Relationships

Operational sustainability for AI agents isn’t achieved through a one-time authentication, but rather through a ‘Continuous Relationship Model’. This framework views agent identity as a dynamic process, beginning with an initial declaration of capabilities and characteristics. Crucially, this declaration is then subject to ongoing observation – monitoring agent behavior for consistency and adherence to stated intentions. This observational data feeds into a continuous confidence assessment, allowing systems to adjust trust levels and permissions over time. By treating identity as a fluid, verifiable attribute shaped by performance and interaction, organizations can move beyond static credentials and build truly resilient AI ecosystems capable of adapting to evolving threats and maintaining reliable operation.

Verifiable Credentials represent a pivotal shift in establishing trust between AI agents and the systems they interact with. These digitally signed assertions allow an agent to present claims about itself – its provenance, capabilities, or adherence to specific standards – in a manner that can be cryptographically verified by relying parties. Unlike traditional trust models reliant on centralized authorities, VCs distribute trust, enabling agents to prove their attributes without revealing underlying data. This is particularly crucial in complex AI ecosystems where agents may be deployed by diverse entities and operate autonomously. By leveraging established standards and cryptographic techniques, VCs facilitate interoperability and reduce the risks associated with relying on unknown or unverified AI entities, ultimately fostering a more secure and reliable operational environment.

The integrity of artificial intelligence models is increasingly reliant on robust supply chain security, and SLSA – Supply-chain Levels for Software Artifacts – offers a framework to address this critical need. This methodology establishes a graduated set of security requirements for each stage of the software build process, from source code management to artifact delivery. By progressively increasing these requirements – levels ranging from 0, offering minimal assurance, to 4, providing strong cryptographic guarantees – organizations can demonstrably verify the provenance and unaltered state of their AI models. This is crucial because malicious actors may attempt to compromise models through the injection of vulnerabilities or backdoors during the build or distribution phases. Implementing SLSA helps mitigate these risks, ensuring that deployed AI systems operate as intended and haven’t been tampered with, thereby fostering greater trust and reliability in increasingly complex AI applications.

The escalating complexity of AI agent networks demands a fundamental shift in governance approaches, moving beyond traditional oversight to encompass transparent audit trails and verifiable accountability. Current enterprise environments are characterized by a striking imbalance – for every human identity, there are 144 non-human, or AI, identities operating with varying degrees of autonomy. This necessitates mechanisms for tracing ‘Recursive Delegation Accountability’, ensuring that actions taken by these agents can be reliably attributed and understood, even as responsibility is delegated across multiple layers of interaction. Without such transparency, identifying and mitigating risks within these complex systems becomes increasingly difficult, hindering the responsible deployment and operation of AI at scale. Establishing clear lines of accountability is no longer simply a matter of compliance, but a critical prerequisite for building trust and ensuring the long-term sustainability of AI-driven operations.

The pursuit of AI identity, as detailed in this report, highlights the inherent ephemerality of any complex system. It’s a process of continuous negotiation between stated purpose and demonstrable action, a dynamic far removed from the static identities assigned to humans. This resonates with Barbara Liskov’s observation: “Programs must be right first before they are fast.” The emphasis isn’t merely on achieving functionality, but on building systems – in this case, AI agents – whose behavior is predictable and accountable over time. A robust AI identity framework, much like well-designed software, prioritizes correctness and trustworthiness as foundational elements, acknowledging that improvements and adaptations will inevitably occur, but within a controlled and verifiable context. The study correctly identifies that current systems aren’t equipped to manage this continuous lifecycle of AI agency.

What Lies Ahead?

The pursuit of ‘AI identity’ reveals less a technical challenge and more a fundamental misapplication of established frameworks. Existing systems, built for human-centric authentication, attempt to force a linear concept of self onto entities operating within entirely different temporal and logical spaces. This is akin to charting river currents with a surveyor’s level – the instruments are not inherently flawed, simply ill-suited to the medium. The core issue isn’t verifying who an agent is, but continuously assessing the congruence between its declared intent and observed behavior – a dynamic relationship, not a static attribute.

Future work must move beyond the notion of ‘identity’ as a fixed credential. Instead, research should focus on robust mechanisms for evaluating drift – the inevitable divergence between initial programming and emergent action. Technical debt, in this context, is not a matter of code complexity, but of eroding semantic integrity. Uptime, then, is not a state of reliable function, but a rare phase of temporal harmony before inevitable decay.

The promise of decentralized identifiers and verifiable credentials remains, but only if repurposed. These technologies should serve not as badges of static authenticity, but as components of a continuous monitoring system, charting the evolving profile of each agent and quantifying the cost of its continued operation. The long-term challenge lies in accepting that all systems degrade, and in building infrastructure that ages gracefully, rather than attempting the illusion of perpetual stability.

Original article: https://arxiv.org/pdf/2604.23280.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-04-28 21:02