Artificial intelligence is more than just a buzzword; it’s a force that’s changing the way we work, learn, and live. In every industry — from healthcare to entertainment — AI is sparking new conversations, debates, and initiatives as organizations race to harness its potential. Case in point: A survey of 700+ data professionals found that 75% plan to implement AI applications in the next year.
Yet, the same survey found that 46% of respondents face challenges integrating AI into their ecosystems. The technology is outpacing existing infrastructure and processes, creating unanticipated consequences that threaten to derail, or at least delay, AI-driven objectives.
One such consequence is a breakdown in data provisioning as more users, including human, agents, and nonhuman identities — devices, software, legal entities, etc., as defined by Gartner (login required) — demand access to exponentially more data. For resource-strapped data governance teams that are responsible for the high-stakes task of making sure the right access policies are in place, it’s a losing battle.
That’s why we’ve spent the past year developing Immuta AI, a new suite of AI capabilities within the Immuta Platform, built on top of our Data Marketplace solution. We developed Immuta AI with data governance teams in mind. By using AI to simplify data product discovery, assist in policy authoring, and automate data access decision-making, Immuta AI will eliminate manual workflows, allowing data governance teams to scale their role with ease and confidence — at the speed of AI.
Take an in-depth look at Immuta AI here.
Traditional data access provisioning simply can’t scale alongside AI.
AI works fast. Tasks that take the average human minutes, hours, or even days can be done by AI in mere seconds. And as today’s applications become more sophisticated, they are ingesting both structured and unstructured data at unprecedented speed and scale. These apps also aren’t just providing more access to non-human identities; they’re making it easier for far more human users — including non-technical users — to access it, too.
The scale of data access provisioning therefore isn’t just increasing incrementally at a rate of 10x — it’s skyrocketing at a rate of 100x or more. As more data consumers access data products via data marketplaces, and AI agents become more ubiquitous and autonomous, they will exacerbate this growth rate by demanding real-time data decisions at machine speed. Traditional human-driven governance models are just not prepared for that kind of growth, especially when you consider that more than half of organizations handle data governance processes manually.
To put it into perspective, the top 25% of Immuta’s customers have between 40,000 and 250,000 data sources, and up to 50,000 data users. To manage all of the requests from all of those users on all of that data, there are just a handful of people on the data governance team who may be relying on manual approval processes. With AI agents, the volume of access requests will grow even more, increasing the strain and pressure on data governance teams, creating bottlenecks, security gaps, and blockers to innovation.
Still, AI is here to stay. So is the need for secure, real-time access to data across platforms, formats, and domains. The question now becomes, how can you efficiently scale data governance to meet — and stay ahead of — the demands of AI-powered analytics?
AI is triggering massive data access demands that manual data governance approaches can’t handle. But, AI also offers a solution to that dilemma.
As with any technological advancement, from the printing press to the internet, AI starts by replacing repetitive tasks and becomes more advanced with training, time, and data. In the case of data governance, AI can first help to streamline data access request approvals, and become progressively advanced by recommending policies and automating decision-making. But before any of that can happen, you need a strong foundation: proactive data governance and provisioning processes built for scale.
To keep up with the pace of AI, it’s time to move beyond traditional role-based access control (RBAC), platform-native controls, or DIY approaches to governing data. These methods are often reactive, and they create complexity and redundancy in cloud environments. They are already difficult to manage at scale, let alone as exponentially more users and data are introduced.
The Immuta Data Marketplace solution already serves as a central hub where organizations can manage governed access to data products. Now, with Immuta AI, we’re enhancing that foundation by embedding intelligence into policy enforcement.
In practice, integrating a robust policy engine that centralizes and automatically enforces dynamic controls sidesteps the challenges of legacy systems, so that you can scale governance and accelerate access, even as data demands grow. With this type of foundation for data provisioning in place, you’re able to add AI tools on top that further enhance and scale governed data access. That’s where Immuta AI comes in.
Over the past 10 years, we have developed and fine-tuned a powerful policy engine that’s built to scale. We’ve incorporated attribute-based access control, dynamic data masking, and continuous data monitoring to ensure that security doesn’t need to come at the expense of speed. Last fall, we launched a data marketplace solution to expedite data product delivery and collaboration. With Immuta AI, we’re bolstering those capabilities and the power of the Immuta Data Marketplace solution to meet the needs of the 100x, AI-powered future of data and analytics.
Immuta AI layers on top of Immuta’s existing workflows, enhancing their functionality and accessibility. This means you can create policies faster, discover data more easily, and streamline processes that require cross-functional collaboration.
Take an in-depth look at Immuta AI here.
It all starts with Immuta Copilot. For data governance teams, Immuta Copilot eliminates the high-stakes process of creating and enforcing the right policies on the right data at massive scale. Users simply enter a plain-language text prompt describing the rules, restrictions, and desired policy outcomes, and Immuta Copilot handles the rest.
Immuta Copilot interprets the text prompt, and uses existing system and user metadata to generate the appropriate policy. By automatically authoring an accurate, consistent policy based on natural language, you not only accelerate the policy creation process, but also open it up to non-technical users.
With Immuta Copilot, organizations that leverage federated data architectures are better able to delegate policy management, vastly reduces the burden on data governance teams, and eliminates uncertainty about whether a policy has accounted for all data, tags, and users. As a result, governance teams can do their jobs faster and more confidently, even as AI fuels greater data demands.
There’s no question that AI will drastically increase the volume of data access requests coming to data governance teams. Immuta Copilot will help alleviate some of their burden, but the scale of the shift necessitates additional support. Enter Immuta AI’s intelligent policy recommendations.
Using data from historical data access decisions and existing policies, Immuta AI will analyze past patterns and the context of incoming requests in order to advise governance teams on whether to approve or deny access. This not only accelerates the process of individually reviewing each access request, but also adds critical assurances for consistency and compliance. As a result, data governance teams vastly reduce time to data, risk of errors, and uncertainty about how to handle one-off requests.
Finally, Immuta AI simplifies the data product search and delivery process with semantic discovery capabilities. Through analysis of data product metadata, schemas, and user profiles, Immuta AI quickly delivers the most relevant data marketplace search results based on semantic text prompts, such as business use or desired outcome.
This eliminates the need for data consumers to use exact search terms in order to find the right data products, accelerating time-to-insight and empowering them to find the data they need, when they need it.
Metadata is the brains behind agentic automation, and Immuta AI puts that metadata to work in order to ease the burden of recertifying access.
When done manually, access recertification requires data governance teams to assess whether specific users or user groups should still have access to datasets after a certain time period, and revoke it if not. The process can easily take hours, days, or even weeks to complete, and takes governance teams away from new data access requests, not to mention other higher priority tasks.
Immuta can aggregate user and data metadata in order to see the frequency with which specific users query data, and whether their queries align with the intended usage purpose. Based on that knowledge, Immuta automatically recommends that users either be recertified, or have their access revoked. Leveraging this approach vastly reduces the time spent on recertifications, which in turn makes the process highly scalable.
The future of governance — that which requires 100x greater scale and speed — is here. And it’s powered by AI.
We developed Immuta AI because we see what’s coming for data governance teams and want to empower them not just to keep up with the pace of AI, but to be ahead of it so they can fully harness its potential. With the power of AI-driven governance at their fingertips, governance teams will accelerate speed to data so that companies can innovate faster, without sacrificing security or compliance.
Stay in-the-know about Immuta AI capabilities and see how they work for yourself. Check out this blog from Immuta CTO Steve Touw, and get in touch with our team to learn more.
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