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Shaikh Muskan
onNovember 5, 2025

Next-Gen Identity Architecture for AI Workflows: Dynamic Authorization & Zero-Trust Security: 2025

Business News Technology Trends

Next-Gen Identity Architecture for AI Workflows: Dynamic Authorization & Zero-Trust Security:

According to the World Economic Forum, by the end of 2025, there will be more than 40 billion AI agents and non-human type identities, which is 12x more than the world’s workforce. This will create a massive shift for Identity & Access Management (IAM).

If you consider the logic of the current model:
Human identity is the basis of security. Every employee has an account, role assignment, and static permissions. But in the age of AI, multiple AI agents are dynamically created and run for a single employee. A traditional IAM system will not be able to handle this scale and speed. The entire old security identity architecture will break down.

Transforming Traditional Identity into Adaptive:

The traditional approach to making applications predictable has become completely deterministic. They have a fixed flow, such as pulling data through HubSpot every Monday, managing reports, and sending emails. “This model is easy to build and simple to secure as well.”

But AI agents are completely different. When given autonomy, inputs can produce completely different outputs. If an agent is asked to summarize an inbound lead, they can write polished insights. The next day, additional data can be fetched from Google Drive and sometimes anomalies can be escalated to the executive system.
This non-deterministic unpredictability of a static role weakens the foundation, when its nature is changing minute by minute. Therefore, it is not possible to assign permission in advance. Manually approving every action is not practical, but giving AI permanent access to boards is simply inviting disaster.

Machine-Speed Access Control for the AI Era:

The AI agent is failing so fast that permissions must be implemented in a dynamic and just-in-time model to handle its growth. The static credentials security system must be designed to issue ultra-short-lived tokens to the agent, valid only for the operation. These tokens should expire in seconds, not hours or days.

The aim is simple: enforce “latest privilege” at machine speed. Agents only get access to what they need, no more, no less, and only for a specific action. So that they complete specific actions and if an agent is compromised, the impact of the damage is limited to seconds, not weeks.

The Rising Problem of Dual Identity Dynamics:

There will come a time when an AI agent will play two roles. One will be its own identity and the other will be on behalf of a human, for example: an AI analyst delegating research to their AI assistant. This should represent both the analyst’s identity and the agent’s independent execution context. This is what creates the dual-person model.

Dual identities are not designed to handle traditional identity systems; they should represent, in every request, who the human is and what the agent can do, the intent of the request, and the context in which the action is taking place.

If nuance isn’t captured, AI agents can cross the wrong boundaries, execute trades, and even access sensitive data. They can modify records to model false identities, which can be very risky.

Expanding Zero Trust Security for AI Agents:

The next challenge for AI could be agent-to-agent trust. In the future, companies will be dealing with not just one AI assistant, but multiple agents that collaborate on the infrastructure. For example, one agent will monitor workloads, another will scale the cluster, and a third will do development.

If the design is not correct, AI agents can bypass traditional identity controls and change instructions without an audit trail. Therefore, all agent-to-agent interactions must be authenticated, authorized, and logged. Without a hidden communication channel, trust issues can lead to agents disappearing at any second. Some will be short-lived, while others will be persistent. This ephemerality can break traditional identity systems.

Most importantly, every action must be auditable. If non-repudiable logs occur, incident response and compliance response can occur. AI agents must be designed to track identity, logging, and traceability globally, as is the case for enterprises. Identify what actions have been performed, identify how, and in what context.

Agent identity playbook:

AI agents don’t wait for legacy IAM. They easily overpower human-based static models with their speed, scale, and unpredictability. To stay ahead of this shift, security leaders will need a machine-ready playbook that is dynamic, automated, and uncompromising.

Agent identity lifecycle: Creative, monitor, and retire agents in real-time—no manual steps.
Dynamically enforce the newly privileged: Grant permissions and limit them to single operations with an ultra-short-lived token lifetime.
Extend null trust to agents: Treat agent-to-agent communication as strict as human logins—authenticate, authorize, and audit every call.
Monitor with full auditability: Every action must have a clear, verifiable trail—even if the agent only exists for a few seconds.

Enterprises will make massive use of AI agents because their business impact will be strong. The identity scale challenge is inevitable. Security leaders must ensure that AI agents operate within guardrails. This means static IAM and adopting practices at machine speed.

The real challenge is translating these principles into execution—automating credential issuance/revocation, embedding identity context into every workflow, and simultaneously observing and controlling agent-to-agent traffic.

And as much as possible — build feedback loops that use telemetry and audit data to continuously improve policies. Security teams will make these capabilities part of their daily routine — not only will they adapt to the era of AI agents, but they will also gain a scale advantage in speed, resilience, and trust.