For years, IT ticketing systems helped organizations keep a handle on data provisioning. Submit a request. Wait for review. Get access.
But that process no longer works when hundreds of users (and now, autonomous AI agents) need data in real time. A recent survey revealed that manual access provisioning is holding back innovation and creating risk for over half of today’s businesses, with 62% of data leaders saying manual governance processes are slowing down access.
IT ticketing systems were designed to manage data provisioning in a controlled, centralized way when access requests came one-at-a-time and infrequently. But legacy ticketing workflows now stretch data governance teams thin and introduce risk when data consumers turn to workarounds to meet deadlines. For example, a data analyst on a product team, blocked by a multi-day access delay, might resort to copying data from a colleague’s workspace.
And ticketing systems can’t accommodate AI agents at all. Imagine an AI agent built to retrain a fraud detection model every hour using the latest customer transaction data. If that agent has to wait in a queue for manual access approvals, the model is always out of date, and the value of the AI is lost.
If you’re scaling AI, launching new data products, or supporting your business teams with governed self-service access, ticketing systems will hold you back. It’s time for a new alternative that’s built for the speed and scale of our AI-driven ecosystems.
How IT ticketing systems handle data access requests
In a manual system, users request access by submitting a ticket — usually via an internal help desk tool. That request is then routed to a data owner or steward who reviews it individually, approves or denies access, and manually grants permissions.
This model is:
- Slow: Users may wait hours or days for access.
- Labor-intensive: Data teams must spend time reviewing and fulfilling requests.
- Inconsistent: Approvals depend on individual judgment, not standard policy.
- Opaque: There’s limited visibility into who accessed what, when, or why.
Today’s data ecosystems are dynamic, decentralized, and always on. Requests come from hundreds or thousands of users across business domains, cloud platforms, and geographies — and increasingly from AI agents that act on data in real time. Access permutations balloon faster than governance teams can manage. Manual reviews simply can’t scale to meet the never-ending demand.
And bottlenecks have consequences. Nearly two-thirds of data leaders say access challenges have impacted the ROI of their data platforms, while 55% admit their current strategies can’t keep up with AI-driven access demands.
What automated provisioning does differently
Automated provisioning replaces manual approvals with policy-based access workflows. Policies are pre-defined based on user identity, usage purpose, geography, or other attributes. When users or systems request access, the platform checks those conditions against the policy in real time and grants access automatically if the criteria are met.
Automated provisioning aligns with modern data environments by providing consistent, scalable, and compliant access. It also makes governance more proactive by minimizing the risk of workarounds and unauthorized access.
What’s possible with automated provisioning
Unlike ticketing systems, automated provisioning transforms the data access process from reactive to proactive. That enables teams to scale data access without scaling risk.
With automation:
- Data consumers get faster access: Whether it’s a human user or an AI system, requests are evaluated against defined policies and dynamically approved if criteria are met.
- Data governors reduce manual workload: Instead of reviewing every request, governors and stewards manage policies that apply consistently across datasets and platforms.
- Governance is embedded, not bolted on: Policies are enforced in real time, ensuring compliant, context-aware access without slowing down innovation.
- Auditability improves: Every request, approval, and access decision is logged, providing transparency and simplifying compliance reporting.
The result is scalable access that supports both AI and analytics — without delays or compliance risks.
What to look for in an automated provisioning solution
As organizations support more users, more platforms, and autonomous AI systems, the right data access governance and provisioning model should simplify access without compromising control. Here are five capabilities to prioritize when evaluating an automated data provisioning solution:
Workflow-driven automation
Automate the entire data access lifecycle, from request to approval to provisioning. This reduces manual overhead, speeds access, and ensures that AI agents and human users alike get the data they need, fast.
Dynamic, context-aware access controls
Move beyond static roles and permissions. Look for a solution that uses real-time context — including user attributes, usage purpose, data sensitivity, and activity patterns — to make access decisions dynamically. This enables dialed-in access and prevents unnecessary exposure.
Cross-platform policy enforcement
With data distributed across clouds and platforms, consistent policy enforcement is critical. A strong provisioning solution should provide unified visibility and control, whether data is accessed by a person, application, or AI model.
Federated governance
Enable domain-level decision-makers to provision access within their scope, without sacrificing central oversight. This accelerates experimentation and analytics, reduces bottlenecks, and improves governance coverage across the business.
Metadata-driven policy engines
The ability to tag, classify, and organize data automatically is essential — especially for supporting real-time AI workflows like inference, retraining, or RAG-based applications. Policies should evolve with the data, not lag behind it.
The Immuta Platform brings together everything needed for modern data provisioning — from dynamic, attribute-based access controls to federated governance and real-time policy enforcement. It’s built to scale securely across cloud platforms, business domains, and AI use cases.
Out of the queue and into the future
As AI accelerates and data usage expands, provisioning systems must evolve. Ticketing workflows that depend on human review simply can no longer meet the speed or scale of modern data needs.
Automated provisioning helps organizations move faster without compromising control. It reduces friction, increases transparency, and lays the foundation for scalable data governance in the age of AI.
To learn more about ticketing systems, automation, and identity governance administration (IGA), check out this blog.