The Zero Trust cybersecurity paradigm gained huge momentum during the COVID-19 pandemic with the rise of remote working. This was due to a distributed workforce with licensed and employee-owned endpoints that needed protection. But the ideas actually originated in the late 2010s; it just took a massive global catastrophe for organizations to accept and adopt it in the mainstream.

Matters have moved on considerably since then, however, with AI in the driving seat and the cybersecurity industry along for the ride. Google's security engineers, who played an instrumental role in devising the original thinking behind Zero Trust, have now outlined their thinking behind a new cybersecurity paradigm that they have labelled 'Beyond Zero' in a fresh paper.

"The enterprise landscape is now entering a new era shaped by artificial intelligence, where AI is changing the assumptions around how enterprise security works. AI agents are being deployed globally to increase operational velocity and boost productivity," wrote Google's VP of security engineering Heather Adkins and its senior director for cybersecurity and data protection, Archana Ramamoorthy, in a blog post.

"However, this increased capability also presents new potential risks, as attackers can leverage similar speed to compromise privileged credentials and execute attacks. This creates the need for a new security paradigm that is capable of defending against these new threats."

Enter Beyond Zero, which is designed to address the AI-era challenges. But what is this framework, and how can businesses adopt the principles at the heart of it?

What is Beyond Zero and how does it work?

Google's engineers define Beyond Zero as a model built on five key principles, which include the following key points:

• Resource and action-based security: Rather than granting authorization broadly for an entire application or tool, users are evaluated based on the level of individual actions or the need to tap into specific resources.
• Blended static and dynamic security: Heightened security measures are applied during high-risk or complex scenarios by pairing dynamic controls with granular static policies.
• Automatically enriched context: Decision systems can reveal insights and glean context from what users do specifically, including offering an idea of what the user should be working on, what data they're trying to access, what they're doing with the data, and which risk mitigations might be available.
• Automated in-depth investigation: Security investigations are immediately and autonomously triggered when risk signals are detected. When that happens, the system can deploy user challenges or containment measures.
• Challenges and containments: This involves triggering verification challenges or containment protocols based on containment policies, where users or agents must provide additional risk telemetry on demand.

Zero Trust introduced the "enduring principle" that legitimate access should never be confused with unlimited trust, says founder and CEO of cybersecurity workforce development firm NukuDo Dean Gefen, who has a deep background in military intelligence, cyber operations and enterprise security. What is evolving, he adds, is the level at which trust must be evaluated, with Beyond Trust representing an evolution from making trust decisions primarily around access toward making granular decisions around specific actions and resources.

"As organizations introduce more automation and AI into their environments, determining whether an identity should enter a system is only part of the security challenge," Gefen explained. "Organizations also need to understand what that identity or agent is attempting to do, what resource it is interacting with, the context surrounding the request, and the potential risk associated with the action."

Autonomous AI is where this new paradigm will become essential. While humans may perform dozens or even hundreds of actions over the course of a day, it's a fraction of the number of actions that swarms of agents could perform. Each action must be compliant with access principles – and conventional Zero Trust principles simply won't cut it.

The legacy of Zero Trust – what's changed?

While Google laid much of the foundation of the Zero Trust principles we see across organizations today, the philosophy itself can be traced back to Stephen Paul Marsh's 1994 doctoral thesis on computer security at the University of Stirling. Although he coined the term, it wasn't until 2009 and a report by Forrester's John Kindervag that the notion of "never trust, always verify" gained traction for the first time. Running in parallel, Google's BeyondCorp internal initiative removed reliance on virtual private networks (VPNs) by verifying user identity and device context for all access requests.

It's been more than a decade since the conception of Zero Trust, and things have moved along an awful lot since then, says Gefen.

"The security landscape has fundamentally changed in both complexity and velocity. Zero Trust gained prominence when organizations were primarily focused on securing human access to networks, applications, and data. Today, that environment includes cloud infrastructure, APIs, automated systems, and increasingly autonomous AI agents operating across multiple platforms.

"Its foundational principles remain extremely valuable," he continues. "The challenge is that the environment those principles must protect has expanded significantly. Organizations are no longer securing only employees accessing applications. They are securing cloud workloads, APIs, automated processes, machine identities, and AI agents that can interact with systems and data with varying degrees of autonomy. That introduces a different level of risk."

Julien Richard, vice president of information security at cybersecurity and identity management company Lastwall, agrees, telling : "If we’re giving an AI agent the same privileges as a highly privileged human simply because it’s acting on that person’s behalf, I’d first ask why we designed it that way. Least privilege, PAM, JIT access, and human approval already give us ways to constrain non-human identities. What’s different with AI is its autonomy and unpredictability, and that’s where more granular, action-level authorization becomes interesting."

How should businesses approach Beyond Zero?

"As we did with prior security publications, we will provide details on our progress, the architectural and operational / implementation considerations that went into this achievement, as well as ideas for how the industry can come together to help advance the state of technology so that everyone can benefit," wrote Google's Adkins and Ramamoorthy – indicating that it's still early and that this new security paradigm may take some time to mature.

That said, businesses would be minded to approach this as an evolution of their existing security strategy rather than a matter of rip-and-replace. Gefen advises that the fundamentals – including strong identity management, least privilege, device security, segmentation, visibility and continuous monitoring – remain essential. Then, it's about understanding where AI is entering workflows and what authority those systems have.

"Business and security leaders should know which AI agents can access sensitive information, what permissions they hold, which systems they can interact with, what actions they can execute autonomously, and how quickly the organization can identify and stop behavior that falls outside its intended purpose."

He recommends starting with the most sensitive data and highest-risk workflows, then applying greater contextual control there. Businesses must also develop the workforce capable of implementing, securing and operating the technology adopted – although this will take considerably longer. With AI becoming embedded across the business, he adds, building capabilities across various domains including cybersecurity, cloud, data and software must become intertwined with the transformation strategy itself.

As for who this affects, both Gefen and Richard indicate that any business, regardless of size or sector, could have a stake in this transition – it's less about being an absolute necessity and more related to AI's usage. In particular, Richard tells , it's about the threat model, risk appetite, and how AI is being used within an organization.

"An AI assistant summarizing public information presents a very different risk from an autonomous agent making production changes or accessing sensitive data," he says. "Not every organization needs a new security architecture because it uses AI. Start with the risk, not the technology."