According to Forbes, artificial intelligence is evolving from background processes to active participants in workflows with the same access and privileges as human employees. Okta’s president of product and technology Ric Smith emphasized that organizations must treat AI agents as users rather than applications, warning that current approaches often involve provisioning API keys or service accounts that persist indefinitely without proper controls. Security expert Den Jones of 909Cyber reinforced that once AI systems can log in, pull data, or take action, they become part of the identity fabric whether acknowledged or not. The article highlights that this shift mirrors historical cybersecurity patterns where adoption outpaces security, but with AI operating at machine speed, the consequences of oversight failures can scale into major breaches within seconds. This fundamental rethinking of AI’s role in organizations demands immediate attention.
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The Governance Gap We’re Creating
What makes this transition particularly dangerous isn’t the technology itself, but the governance vacuum we’re creating around it. Organizations have spent decades developing robust processes for human identity management—background checks, role-based access controls, behavioral monitoring, and termination procedures. Yet we’re deploying AI agents with similar system access using fundamentally different, often weaker, security paradigms. The assumption that AI can be managed like traditional software ignores a critical reality: these systems make autonomous decisions, interact with multiple systems, and can initiate cascading effects across an organization. The governance frameworks we apply to human users exist precisely because people can make mistakes, act maliciously, or be compromised—all risks that now apply equally to AI systems.
Consequences at Machine Speed
The velocity at which AI operates transforms what were once manageable incidents into catastrophic events. A human employee might accidentally share sensitive data with a few colleagues over several hours, but an AI agent could distribute that same data across multiple external systems in milliseconds. The probabilistic nature of large language models introduces another layer of complexity—these systems don’t follow deterministic rules but make “best guess” predictions that can lead to unexpected outcomes. When combined with system-level access, a single misinterpreted instruction could trigger automated workflows that delete critical data, modify production systems, or expose confidential information across multiple platforms simultaneously. The window for detection and intervention has shrunk from hours to seconds.
The Expanding Identity Fabric
Traditional identity and access management systems were designed for human-scale operations, but we’re entering an era where non-human identities may soon outnumber human ones by orders of magnitude. Each AI agent, automated process, and connected system represents a new identity that requires management. Okta’s observation about treating AI as users points toward a fundamental architectural shift—we need identity systems that can handle thousands or millions of machine identities with the same rigor we apply to human ones. This means extending beyond simple authentication to include behavioral analytics, access pattern monitoring, and anomaly detection specifically tuned to non-human behavior patterns. The organizations that solve this challenge first will gain significant competitive advantage in both security and operational efficiency.
Building Future-Proof Governance Frameworks
The solution lies in developing adaptive governance frameworks that recognize AI as both tool and actor. This requires rethinking several foundational security principles. First, we need dynamic access controls that can adjust permissions based on context, behavior patterns, and risk assessments in real-time. Second, we must implement comprehensive audit trails that capture not just what actions were taken, but the reasoning and context behind AI-driven decisions. Third, organizations should establish AI-specific incident response protocols that account for the unique characteristics of machine-speed operations. Finally, we need to develop new metrics for measuring AI trustworthiness and reliability that go beyond traditional system uptime or error rates to include behavioral consistency and decision transparency.
The Strategic Imperative
Treating AI as users isn’t just a technical consideration—it’s becoming a strategic business imperative. As organizations increasingly rely on AI for critical operations, the ability to securely manage machine identities will determine both innovation capacity and risk exposure. Companies that successfully implement AI identity governance will be able to deploy more sophisticated AI systems with confidence, accelerating digital transformation while maintaining security. Those that fail to adapt will face increasing operational risks, compliance challenges, and potential catastrophic failures. The transition from AI as tool to AI as colleague represents one of the most significant shifts in enterprise computing since the advent of cloud computing, and our security frameworks must evolve accordingly.
