AI agents are making a meaningful impact on how we use data, executing multi-step workflows, synthesizing insights — and exposing massive data policy gaps. While these digital assistants hold immense potential, they also introduce new layers of complexity.
The friction comes from the fact that data governance frameworks have always been built with human users in mind. And AI agents act like humans when they interact with data systems. But from a governance perspective, treating them like human users is a fast track to risk. If you want to tap into the potential of AI-powered data use, you need to evolve data governance to keep pace.
What exactly is an AI agent?
AI agents are autonomous software systems that can make decisions and take actions without human input. Unlike traditional software that follows a fixed script, AI agents learn, adapt, and act on their own — often in real time.
Think of a customer service chatbot that resolves issues or a personal assistant that books meetings. They perceive their environment, make decisions, and act to achieve specific goals. But while they can act without human input, AI agents don’t form their own goals.
AI agents vs. agentic AI vs. non-human identities (NHIs)
It’s helpful to distinguish AI agents from broader concepts like agentic AI and non-human identities (NHIs).
Agentic AI goes a step further in decision-making autonomy. These systems can generate, pursue, and adapt goals over time. For example, a research AI that forms hypotheses, searches for data, and adapts its approach would be considered agentic.
NHIs include not only AI agents, but also service accounts and bots — any identity interacting with data that isn’t a human being. They might be powered by basic scripts or complex AI, but their defining feature is their identity, not necessarily their intelligence or autonomy. One example is an automated rules-based recommendation engine.
AI agents and the data provisioning puzzle
Here’s the problem when it comes to data governance for AI agents: most data provisioning systems were built for humans. They rely on manual approvals, role-based access, and policies that assume predictable patterns. But AI agents request access at machine speed. They don’t work 9 to 5, and they don’t fill out forms.
This creates a massive scalability issue for data governance teams, who are already overwhelmed by human-driven requests. This dilemma is evidenced by a recent survey showing that 80% of data experts say AI is making data security more challenging.
Traditional data governance models simply can’t keep up with AI. Modern governance requires automating workflows, embedding intelligence into every layer of governance, and moving at the pace of AI.
How AI agents are impacting data usage
AI agents are fundamentally changing the way organizations interact with data. They don’t just request access — they ingest large volumes of structured and unstructured data, analyze it in real time, generate insights, and sometimes even provision access for others. This transformation creates new opportunities and risks.
The opportunities: Unlocking data at scale
- Acceleration of insights: AI agents can aggregate datasets, identify patterns, and surface insights far faster than humans. This can accelerate everything from product innovation to risk management. When access is governed properly, this leads to faster time to value.
- 24/7 operations: Unlike human users, AI agents never sleep. They can monitor, retrieve, and process data continuously, enabling always-on capabilities in areas like fraud detection, cybersecurity, and supply chain optimization.
- Consistent policy execution: When policies are encoded into AI workflows, agents don’t deviate. They enforce access rules, apply classifications, and redact sensitive fields with perfect consistency — assuming the policies are correct and up to date.
- Reduced operational burden on teams: Agents can take over high-volume, low-complexity tasks like validating access requests or routing data pulls to approved sources.
The risks: Governance gaps
AI agents in the real world
AI agents are already transforming the way enterprises operate, behind the scenes and at scale. Let’s explore what this looks like in practice.
Financial services: High-speed risk analysis
AI agents are being deployed to continuously scan structured and unstructured data in market feeds, internal risk models, news sentiment, and customer portfolios. Without the right guardrails, however, these agents might pull personally identifiable information (PII) or non-public data from sources they shouldn’t touch. A single lapse in access control could result in regulatory violations or insider trading accusations.
With a policy entitlement engine and real-time auditability, financial institutions can ensure that AI-driven insights are sourced from compliant, governed datasets — even as market conditions change minute to minute.
Healthcare and life sciences: Clinical acceleration
In healthcare and life sciences, AI agents help researchers identify patient cohorts, surface anomalies in clinical trial data, and even recommend treatment protocols based on EHR data. These agents accelerate biotech discovery, improve diagnostic precision, and personalize care.
But the stakes are high. AI agents accessing sensitive health data — especially across borders — can easily run afoul of HIPAA, GDPR, and local data sovereignty laws. Misapplied permissions could lead to unauthorized data access or biased model outputs. Governance here is mission-critical.
Dynamic, policy-driven access that adapts to patient data sensitivity, user roles, and jurisdictional rules helps maintain compliance without slowing down researchers and healthcare providers.
Marketing and sales: Personalization
AI agents in marketing use behavioral data to personalize emails, segment audiences, and A/B test content in real time. In sales, they mine CRM data to prioritize leads and surface relevant insights during live calls.
But personalization walks a fine line. If an AI agent accesses protected customer data or makes assumptions based on inferred characteristics (like health status or location), it can easily violate consent agreements or create brand backlash.
Data discovery and classification services allow marketers to use high-quality, governed data while redacting or masking sensitive fields, so AI agents can personalize without compromising privacy.
Each of these use cases hinges on rapid, secure, policy-compliant access to data. Without visibility and control, organizations face serious risks, from compliance violations to brand damage. That’s why real-time data monitoring, automated policy enforcement, and cross-platform auditability are non-negotiable.
How to safely integrate AI agents
With the right approach, you don’t need to choose between speed and safety. Here are five actionable strategies for safely integrating AI agents into your data environment:
Classify and tag data up front
Start with strong metadata. Classify data by sensitivity, domain, geography, and usage rights. This helps ensure that agents can only access what’s appropriate — and only when necessary. You can automate this step with a metadata registry that dynamically synthesizes metadata about your data, users, and applications.
Treat AI agents as first-class identities
Don’t lump agents in with humans. Assign them distinct identities with scoped permissions and audit trails to make it easier to monitor activity and revoke access when needed. Enable attribute-based access control for both human and non-human users, including AI agents, with real-time auditing across platforms.
Automate access decisions
Manual approvals can’t keep pace with machine-speed requests. Use AI-assisted workflows to dynamically approve access based on policy, context, and purpose. Automate policy authoring, provisioning decisions, and policy adaptation based on evolving usage patterns.
Implement real-time monitoring
Continuous monitoring is key to catching missteps before they escalate. Real-time logs and anomaly detection help governance teams stay in control — even as agents act autonomously. Use a platform with a unified audit layer that tracks access across all connected data platforms, for proactive intervention and compliance visibility.
Establish a feedback loop
Your governance model should evolve as your AI usage grows. Analyze agent behavior, track emerging patterns, and refine policies accordingly. This ensures your governance strategy scales alongside your innovation.
Governance that moves as fast as AI
We’re entering a new era where AI agents will drive a significant share of enterprise data activity. Organizations that cling to legacy governance models will find themselves outpaced or out of compliance. But those that adopt scalable, intelligent governance frameworks will unlock the true potential of AI — putting their data to work safely, efficiently, and confidently.
Read more about using AI for data governance.