Coworkers With Superhuman Context: Why Access Control Isn’t Enough

Over the next year, AI agents will increasingly operate across enterprise systems, pulling signals from CRMs, ERPs, HR tools, and communication platforms to draw conclusions and take actions without step-by-step human oversight. That ability to assemble context is not only what makes agents powerful, but is also what creates a new baseline of governance concerns.

Traditional access control asks “Which users are allowed into which systems?” But agents don’t just log in; they synthesize information across boundaries, infer new knowledge, and act in ways no permission model anticipated.

Recent surveys make this risk clear: 80 percent of organizations report that their AI agents have carried out unintended actions such as accessing unauthorized systems or resources, accessing or sharing sensitive or inappropriate data, and downloading sensitive content. 

These incidents are reminders that once agents can engineer context and act autonomously, traditional access controls are no longer enough. What’s missing is a way to observe, audit, and govern their reasoning process. Here’s why.

Beyond access: Governing how agents reason

Traditional role-based access controls were designed for people: define roles, grant permissions, and monitor who enters which systems. That model works when the main risk is unauthorized access. With agents, the challenge is different. What matters is not only where they go but how they assemble information once inside.

Take a finance agent with legitimate access to both payroll and expense data. By combining those datasets, it could surface insights the enterprise never explicitly authorized. If compromised, the same breadth of access could also allow the agent to move laterally into tax filings, vendor accounts, and compliance logs. Microsoft has already reported cases where compromised copilots became stepping stones into wider system infiltration.

The answer isn’t to strip agents of access, but to extend the same governance principles applied to employees, clear roles, scoped responsibilities, and accountability for outcomes, into agent oversight. 

The difference is that with agents, governance can’t stop at permissions. It also has to extend into how they reason: what data they combine, which inferences they draw, which ones they report to other agents and humans, and whether those conclusions remain within acceptable boundaries. That requires new forms of monitoring and auditing designed not only to track activity, but to make the reasoning process itself visible and reviewable.

How reasoning risks take shape

When agents assemble context across systems, they can transform ordinary, non-sensitive inputs into outcomes the enterprise never authorized. This happens in two ways:

1. “Safe” data can turn sensitive when aggregated. When agents combine siloed or anonymized datasets, they can surface information that was previously protected. For instance, HR records plus performance data might inadvertently reveal salary disparities. Governance here requires aggregation guardrails: explicit rules on what data types may be combined, and audits that flag when sensitive categories emerge from otherwise safe inputs.
   
   
2. Unanticipated insights through novel connections. Sometimes the risk isn’t that data becomes sensitive, but that agents generate inferences no one planned for. Budgets are a clear example. By linking signals across procurement, project timelines, and expense systems, an agent might forecast overruns or even reallocate spend automatically. What looks like proactive insight can actually create risk: premature escalations to leadership or resource misallocations if those inferences are acted on without review. Governance here requires emergent reasoning oversight: review mechanisms and decision logs that surface when agents generate classes of inference (like financial forecasting) that weren’t explicitly authorized. This may extend to having specific oversight roles for the new era of hybrid human-agent workforces.

Together, these protocols ensure that context remains a source of value without drifting into disclosures or inferences that enterprises cannot control or defend.

From gap to opportunity

Enterprises don’t just need powerful agents, they need agents that can carry equal weight to human coworkers. A new economy is already forming around this shift: companies that can trust agents with meaningful responsibilities will move faster, scale more efficiently, and unlock opportunities their competitors can’t.

That requires more than access; it requires explainability, accountability, and trust in how those agents operate. Those that establish observability, reasoning logs, and enforceable guardrails now will be positioned to lead in this new economy. Those that delay will find themselves reacting to incidents, struggling with compliance gaps, and losing ground to competitors.

Wand is laying the foundation that makes agent governance possible. Our operating system for the hybrid workforce — where humans and AI agents work side by side — is designed to give enterprises visibility, accountability, and control over how agents operate. It does this through four core pillars:

• Agent Government: The oversight layer that enforces rules, budgets, and outcomes, ensuring safe, compliant agent behavior.
• Agent Network: A coordinated system where agents work together and alongside human counterparts in accountable ways.
• Agent University: A continuous learning engine that trains, retrains, and evolves agents to meet changing enterprise standards.
• Agent Economy: An integrated marketplace for the tools, data, and services that expand agent capability within governed boundaries.

Together, these layers make agent governance tangible: giving companies the means to treat agents as teammates, not tools, and ensuring every action is explainable, verifiable, and aligned with enterprise priorities.

If you’d like to see how Wand can give you visibility and control over your agents, book a demo.

Book a Demo