Agentic AI: Biggest Enterprise Security Threat for 2026

Nearly half of cybersecurity professionals now consider agentic AI the single most dangerous attack vector heading into 2026. That finding, drawn from a recent Dark Reading readership poll, should stop every security leader in their tracks. Not because it’s surprising—most of us saw this coming—but because of what it reveals about just how fast the threat landscape has shifted.

Key Takeaways

1. Agentic AI Has Become the Number One Security Concern for 2026. A Dark Reading readership poll found that 48% of cybersecurity professionals identify agentic AI and autonomous systems as the top attack vector heading into 2026, outranking deepfake threats, board-level cyber recognition, and passwordless adoption. The finding reflects a growing industry consensus that AI agents—operating with elevated permissions across multiple systems—represent the fastest-expanding attack surface in enterprise security today.
2. Shadow AI and Non-Human Identities Are Compounding the Risk. Employees are importing unsanctioned AI tools into work environments without security oversight, and more than a third of data breaches now involve unmanaged shadow data. Every AI agent introduced into an organization creates a non-human identity requiring API access and machine-to-machine authentication—challenges that legacy identity management systems were never designed to handle.
3. Insecure Code and Rushed Deployments Are Building Vulnerable Infrastructure. Competitive pressure is driving developers to deploy agentic AI with minimal security review, including unvetted open-source MCP servers and code produced through rapid “vibe coding” practices. Industry analysts warn that the result is a growing volume of vulnerable infrastructure that attackers will inevitably target as agentic AI adoption scales.
4. Security Must Move to the Data Layer to Keep Pace. Legacy perimeter defenses and static access controls were not designed for a world where autonomous agents operate inside the network by design. Effective protection now requires data-layer security with zero-trust governance, context-aware authorization, and unified visibility across every interaction—whether initiated by a human or an AI agent.
5. Unified Governance Reduces Breaches and Simplifies Compliance. Organizations that consolidate sensitive content communications under a single security framework—covering file sharing, managed file transfer, email protection, and web forms—experience fewer breaches than those relying on fragmented point solutions. A unified approach also streamlines regulatory compliance with built-in support for standards like FedRAMP High, FIPS 140-3, SOC 2 Type II, and ISO 27001.

The poll asked readers to weigh in on four potential security trends for the year ahead: agentic AI attacks, advanced deepfake threats, board-level recognition of cyber risk, and the adoption of passwordless technology. Agentic AI dominated the results, with 48% of respondents placing it at the top. Passwordless adoption, by contrast, landed at the bottom—an indication that most professionals aren’t holding their breath for organizations to finally retire their outdated password practices.

These numbers tell a clear story. The rise of autonomous AI systems across enterprises isn’t just a productivity story anymore. It’s a security story—and right now, the security side isn’t keeping up.

The poll results are consistent with broader industry research. Internal surveys from firms like Omdia have found that AI adoption sits at the very top of the list of corporate security concerns, with securing agentic AI specifically identified as the number one priority for security teams trying to support their organization’s growth. The consensus is forming fast, and the message is hard to ignore: If you’re deploying agentic AI without a clear security strategy, you’re building on a foundation that’s already cracking.

Why Agentic AI Changes the Threat Equation

To understand why agentic AI has become such a lightning rod for security concerns, you need to understand what makes it different from the AI tools that came before it.

Traditional AI models sit in the background. They analyze data, generate text, or make recommendations, but they don’t do anything on their own. Agentic AI is different. These systems are designed to act autonomously—executing tasks, making decisions, accessing databases, moving files, and communicating across platforms with minimal human oversight. They carry elevated permissions because they need them to work. And that’s exactly what makes them such an appealing target.

Enterprises everywhere are deploying agentic AI to streamline operations, from predictive maintenance in manufacturing to automated workflows in software development. The productivity gains are real. As cybersecurity analysts have noted, agentic AI and autonomous systems can scale productivity by five to ten times what was previously possible. But that same scale applies to risk. Every AI agent introduced into an environment creates new access points, new authentication challenges, and new pathways for attackers to exploit.

What’s particularly concerning is the speed of adoption. Developers are under enormous pressure to ship products and hit deadlines, and the result is a growing pile of insecure code being pushed into production. Industry analysts have raised alarms about the widespread use of open-source model context protocol (MCP) servers with little to no security vetting, combined with the explosion of “vibe coding”—a trend where developers prioritize speed and experimentation over rigor. The combination is producing infrastructure that’s vulnerable by design.

The Shadow AI Problem No One Wants to Talk About

If the official deployment of agentic AI creates risk, the unofficial deployment of it creates chaos.

Shadow AI—the use of unsanctioned AI tools by employees outside the view of their security team—has become one of the most persistent and difficult-to-address threats in the modern enterprise. Workers find an open-source AI agent that helps them automate a tedious task, plug it into their workflow, and never tell IT. It sounds harmless. It isn’t.

The scale of this problem is staggering. Research has found that more than a third of data breaches now involve shadow data—unmanaged data sources that security teams don’t even know exist. When shadow data meets shadow AI, the risk doesn’t just add up. It compounds exponentially. You end up with AI agents accessing sensitive information through channels that aren’t monitored, aren’t governed, and aren’t protected by any of the controls your security team has built.

And there’s a deeper structural issue at play. Traditional identity management systems were designed for people. They authenticate humans, assign roles, and manage permissions based on who’s logging in. AI agents don’t fit neatly into that model. They operate through APIs, use machine-to-machine authentication, and often require broad permissions to function. Every agent introduced into an environment represents a non-human identity that needs to be secured—and most organizations aren’t equipped to handle that at scale.

Consider a practical scenario. A marketing team adopts an AI agent to automate campaign analytics and report generation. The agent needs access to the CRM, the email platform, customer data repositories, and third-party advertising APIs. That’s four different systems, each with its own authentication requirements, each representing a potential point of compromise. Multiply that by every team in the organization experimenting with similar tools, and you start to see how quickly the attack surface spirals out of control.

The Widening Gap Between Productivity and Protection

Here’s the tension at the heart of all of this: Businesses can’t afford to ignore agentic AI, and they can’t afford to deploy it without proper security. Right now, most are leaning heavily toward the first concern and underinvesting in the second.

The competitive pressure is real. Companies that adopt agentic AI effectively stand to gain enormous operational advantages. Those that don’t risk falling behind. That’s why internal surveys consistently show AI adoption sitting at the top of corporate priority lists. Security teams understand this. They want to support growth. But they’re watching their attack surface expand at a rate that outpaces their ability to defend it.

The problem isn’t that organizations are adopting AI. The problem is that they’re doing it without rethinking their security architecture to account for a fundamentally different kind of technology. Legacy security models—perimeter defenses, static access controls, fragmented monitoring tools—weren’t designed for a world where autonomous agents move freely across systems, make decisions in real time, and interact with sensitive data at scale.

Think about what a traditional security perimeter is actually defending. It was designed to keep unauthorized humans out of a defined network boundary. But agentic AI operates inside that boundary by design. It needs to. The entire value proposition of these systems depends on giving them wide-reaching access to internal resources. That means the threat model has fundamentally changed. The thing security teams need to worry about most isn’t someone breaking in from the outside—it’s something already inside acting in ways nobody anticipated.

Something must change. And it has to change at the data layer.

Securing the Data Layer: How Kiteworks Addresses the Agentic AI Threat

The core insight behind Kiteworks’ approach is that in a world of autonomous AI systems, security has to live where the data lives. It’s not enough to secure individual tools or endpoints when AI agents can traverse entire networks. You need a unified framework that governs every interaction with sensitive data—regardless of whether the entity requesting access is a person or a machine.

A Private Data Network Built for Zero Trust

Kiteworks’ Private Data Network applies content-defined zero-trust principles directly to sensitive data. Every interaction—whether initiated by a human employee or an AI agent—is authenticated, authorized, monitored, and encrypted before access is granted.

In practice, this means granular access controls that enforce least-privilege access for both human and non-human identities. Role-based and attribute-based policies work together to make context-aware authorization decisions. Access isn’t just determined by who’s asking—it’s determined by the sensitivity of the data, the device being used, the location of the request, and the specific action being attempted. Everything is consolidated under a single governance framework, which eliminates the fragmented visibility that makes agentic AI attacks so dangerous in the first place.

A Secure MCP Server for Controlled AI Integration

One of the specific risks highlighted in the Dark Reading report is the proliferation of insecure MCP servers being deployed by developers racing to meet deadlines. MCP is the protocol that allows AI agents to interact with external data sources and tools, and poorly secured implementations can turn it into an open door for attackers.

Kiteworks has built a secure MCP server that keeps AI interactions within the boundaries of the private data network. Sensitive data never leaves the trusted environment. Every AI operation is secured with OAuth 2.0 authentication, governed by the same role-based and attribute-based controls that protect human access, and logged with comprehensive audit trails for forensic analysis and regulatory compliance. The existing security policies an organization has already built don’t need to be rebuilt for AI—they extend automatically. That last point matters more than it might seem. One of the biggest operational burdens security teams face with new technology is the need to create and maintain entirely separate policy frameworks. By extending existing controls to AI interactions, Kiteworks eliminates that duplication and keeps governance manageable even as AI adoption accelerates.

Tackling Shadow AI Before It Becomes a Breach

Addressing shadow AI requires visibility above all else. You can’t secure what you can’t see. Kiteworks provides centralized audit logs that track every data interaction—including those driven by AI—across the entire organization. Embedded anomaly detection powered by machine learning identifies unusual data transfers and flags potential exfiltration attempts in real time.

On top of that, automated data classification and tagging identifies sensitive content based on keywords, patterns, and contextual analysis. Data loss prevention policies then enforce the appropriate response automatically: blocking, quarantining, or encrypting data based on its sensitivity and the context of the access request. The result is that even when employees bring unauthorized AI tools into the environment, the data itself remains protected.

Securing Non-Human Identities at Scale

The explosion of AI agents across the enterprise means a corresponding explosion in non-human identities—each requiring API access, each creating a machine-to-machine authentication challenge. Kiteworks addresses this with a secure API framework built on REST protocols with stringent authentication, authorization, and encryption at every layer.

Real-time monitoring uses machine learning to detect anomalies in API traffic, catching threats before they have a chance to escalate. Automated vulnerability scanning keeps APIs resilient against new attack techniques, and JWT-based authentication provides a secure foundation for machine-to-machine communication across custom API clients.

A Hardened Architecture Against Supply Chain Risk

Rushed deployments and insecure code are a recurring theme in the current AI adoption cycle, and they create real supply chain risk. Kiteworks’ hardened virtual appliance is built to mitigate this through sandboxing for third-party libraries, which isolates open-source components and prevents zero-day exploits from reaching sensitive data. An embedded firewall and web application firewall add multiple layers of intrusion protection. And a zero-trust internal architecture ensures that all service communications—even within the appliance itself—are treated as untrusted, requiring authentication tokens and encryption at every step.

The Bigger Picture: Unified Governance for the AI Era

What sets this approach apart from point solutions that try to secure individual AI tools or specific endpoints is the scope. Kiteworks consolidates file sharing, managed file transfer, email protection, and web forms under a single security framework. That means fewer gaps in visibility, fewer inconsistencies in policy enforcement, and a smaller overall attack surface for adversaries to exploit.

The data backs this up: Organizations that rely on fewer, more unified communication tools experience fewer breaches. When every channel runs through the same governance engine, there’s no weak link for attackers to find. And critically, this consolidation doesn’t require organizations to rip and replace their existing infrastructure. It works alongside the tools teams already use, providing a consistent security layer that travels with the data rather than sitting at arbitrary boundaries.

For organizations in regulated industries, Kiteworks meets the standards that matter: FedRAMP High readiness, FIPS 140-3 certification, SOC 2 Type II, and ISO 27001. Compliance isn’t an add-on. It’s built into the architecture.

Looking Ahead: The Window to Act Is Closing

The Dark Reading poll captures a moment of collective recognition across the security industry. The people on the front lines understand that agentic AI isn’t just another technology to manage—it’s a fundamental reshaping of the attack surface. And 48% of them believe it will be the dominant vector for cybercrime by the end of this year.

That belief isn’t unfounded. The ingredients are all in place: rapid adoption driven by competitive pressure, developers deploying without adequate security review, shadow AI spreading through organizations unchecked, and identity management systems that weren’t designed for machines. It’s a recipe for exactly the kind of large-scale breaches that make headlines.

But it doesn’t have to play out that way. Organizations that move now to secure their data layer—with unified governance, zero-trust access controls, and real-time visibility into both human and AI-driven interactions—will be positioned to safely scale their AI initiatives. They’ll capture the productivity benefits without becoming the next cautionary tale.

The path forward isn’t about slowing down AI adoption. That ship has sailed, and frankly, the organizations trying to hit the brakes will find themselves at a competitive disadvantage that’s just as dangerous as any security vulnerability. The path forward is about building security into the foundation of AI deployment from the start—treating it not as a checkpoint at the end of the process, but as the infrastructure on which everything else runs.

Those that wait will find themselves playing catch-up in an environment where the attackers have already adapted. The clock is ticking, and 2026 will separate the organizations that took the threat seriously from those that became its proof of concept.