Understanding Machine Identities in Cybersecurity

In the ever-evolving landscape of cybersecurity, traditional focus on human credentials is shifting dramatically. While usernames and passwords remain a primary target for attackers through methods like phishing and brute force, the reality is that machine identities have taken center stage. Surprisingly, research indicates that for every human identity, there are 46 machine identities, including API keys, service accounts, certificates, and AI agents. These entities are increasingly tasked with authenticating themselves and engaging with crucial data and systems, making it essential to understand their implications.

The Rise of Non-Human Identities

As organizations increasingly integrate AI technology and expand their digital footprints, the terminology around these identities is evolving. The most widely used term is "non-human identity" (NHI), which encompasses a broad range of credentials that are not linked to a person, from Internet of Things (IoT) devices to large language model (LLM) agents. While this label serves a purpose in general discussions about high-level policies, its vague nature can muddy the distinctions between various types of identities.

For clarity, many cybersecurity professionals, including those at Delinea, prefer the term “machine identity.” This terminology allows for a more precise understanding of security concerns, focusing on how each machine or service proves its authenticity and accesses sensitive data. By spotlighting machine identities, businesses can effectively address key security areas such as certificate management, key rotation, and the implementation of zero trust principles.

Why Terminology Matters

The correct classification of identities is crucial for effective security management. Categorizing everything from a temperature sensor to an autonomous AI agent as NHIs may lead to oversimplified responses that overlook critical differences in security needs. Recognizing these distinctions enables organizations to tailor their security strategies appropriately, leading to better protection of their assets.

Expanding Attack Surfaces

The increase in machine identities significantly broadens the potential attack surface. Cybercriminals no longer need to compromise a human account; they can exploit poorly protected service accounts with expansive permissions instead. This shift underscores the need for robust identity management practices in the digital age.

The integration of AI technologies adds another layer of complexity. As LLM agents create new bots, fresh credentials emerge, necessitating continuous management. Without vigilant oversight, these credentials can become unnoticed gateways to sensitive information. Additionally, the longevity of credentials can pose challenges; IoT devices and similar machines are often equipped with hard-coded certificates designed for long-term use, potentially outlasting the organization that deployed them.

The Issue of Privilege Creep

Another notable challenge is privilege creep, which occurs when minimal access rights gradually expand into broader permissions due to ad-hoc adjustments or temporary fixes. Given that machines typically operate silently and consistently, their escalating privileges may go undetected until they are exploited. This further emphasizes the need for a well-defined management strategy tailored to machine identities.

Implementing Effective Solutions

At Delinea, the approach towards managing identities, whether human or machine, hinges on a zero-trust framework rooted in the principle of least privilege. The first step in this process involves comprehensive discovery: you can’t secure what you don’t know exists. A meticulous inventory of every certificate, API key, and token is essential. Each must be tagged to an owner and given a documented purpose; if it lacks an owner or justification, it represents a potential risk.

Once visibility is established, organizations must streamline the entire credential lifecycle, particularly for AI-driven workloads. Automated processes for identity creation, rotation, and retirement ensure that best practices are followed without the reliance on manual checklists. Policy-as-code can drive issuance: when an LLM agent creates a new helper bot, a corresponding credential with an expiration date should also be generated. Utilizing short-lived identities can drastically minimize risks associated with leaks.

Prioritizing Access Control

Given that AI systems often manage critical data, they deserve heightened scrutiny. Enforcement of least-privilege access controls from the outset is essential, alongside regular audits to evaluate and adjust privileges as needed, preventing privilege creep before it becomes a problem. Enterprises can also leverage AI-driven authorization methodologies to keep pace with their rapidly changing automation environments. Just-in-time access models provide context-sensitive privileges, ensuring that access is granted only when necessary while promoting productivity.

As we transition from the days of worrying about passwords hidden under keyboards to managing forgotten certificates, the significance of addressing machine identities becomes increasingly clear. Regardless of whether one adopts the term "machine identity," "non-human identity," or another classification, the ultimate goal remains the same: every identity must be properly managed and secured to prevent misuse by malicious actors.