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Understanding Agentic AI and Its Identity Challenges<\/h2>\nWhat Makes Agentic AI Different from Humans and Machines<\/h3>\n
Agentic AI operates at machine speed and scale but lacks human judgment, creating a unique identity challenge.<\/p>\n
Unlike humans, agentic AI doesn\u2019t undergo background checks or onboarding. Unlike machines, it has broad access to resources and operates at scale. As Cisco\u2019s Matt Caulfield<\/em> noted, agents are a \u201cthird kind of identity\u201d \u2014 neither human nor machine.<\/p>\n They can execute thousands of API calls in seconds, bypassing traditional access controls. This speed and scale make them both powerful and dangerous.<\/p>\n Cisco is building an identity stack designed specifically for agentic AI, with a six-stage maturity model to govern them effectively.<\/p>\n The model helps enterprises move from agent pilots to production by addressing identity, access, and action-level control. It\u2019s a necessary evolution for enterprises running agent pilots at scale.<\/p>\n Traditional IAM systems were built for human-scale access, not for agents that operate at machine speed and consume far more permissions.<\/p>\n According to Cisco President Jeetu Patel<\/em>, 85% of enterprises are running agent pilots, but only 5% have reached production \u2014 an 80-point gap.<\/p>\n The root issue is that existing IAM tools weren\u2019t built for agentic AI. Agents lack judgment and operate at a scale that traditional systems can\u2019t handle.<\/p>\n Without a maturity model and new identity controls, enterprises risk catastrophic outcomes \u2014 like the Fortune 50 security policy rewritten by an AI agent with valid credentials and authorized access.<\/p>\n Traditional access control verifies credentials but doesn\u2019t monitor actions. This creates a dangerous gap where agents can act freely once authorized.<\/p>\n Current systems assume that a valid credential equals safe behavior. In reality, an agent with access can execute thousands of API calls in seconds \u2014 far beyond human capacity.<\/p>\n Zero trust works for humans, but not for agents. As Cisco\u2019s Matt Caulfield<\/em> explained, agents operate at machine speed and scale, making traditional policies ineffective.<\/p>\n Human employees go through onboarding, but agents bypass all checks. This lack of judgment means they can rewrite policies or access data without oversight.<\/p>\n Enterprises need tools that monitor AI actions in real time. Access control alone isn\u2019t enough \u2014 enforcement must shift from identity to behavior.<\/p>\n Without action-level enforcement, even the most secure credential can lead to catastrophic outcomes, as seen in the Fortune 50 security policy rewrite<\/em> incident.<\/p>\n Traditional IAM systems are designed for human users, not AI agents. As Cisco\u2019s Matt Caulfield<\/em> explained, agents operate at machine scale and speed, making human-like access control ineffective. Attempting to apply the same rules to agents is like using a wrench to tighten a bolt \u2014 it works, but not well. Machine-scale enforcement, on the other hand, tracks actions in real time and scales with the number of agents.<\/p>\n Cisco\u2019s six-stage identity maturity model is a blueprint for securing agentic AI. It moves beyond simple access to include continuous monitoring and enforcement. Enterprises that skip this step risk exposing themselves to the same issues that CrowdStrike\u2019s George Kurtz<\/em> described \u2014 agents rewriting security policies without oversight.<\/p>\n Action-level controls ensure that an AI agent\u2019s behavior aligns with organizational policies. As Caulfield noted, \u201cWe really need to shift our thinking to more action-level control.\u201d<\/em> This means tracking not just who has access, but what the agent is doing in real time. It\u2019s the difference between detecting a breach and preventing it before it happens.<\/p>\n Agents are not humans, nor are they machines \u2014 they are a new type of identity. Define policies that reflect this uniqueness. Avoid cloning human user accounts, as agents consume far more permissions due to their scale and speed. Cisco\u2019s Matt Caulfield emphasizes that agents operate at machine scale but have human-like access, making traditional IAM models inadequate.<\/p>\n Access control is not enough. Monitor actions taken by agents in real time. Human employees won\u2019t execute 500 API calls in three seconds \u2014 agents will. Shift from verifying credentials to watching what agents do. Zero trust must extend beyond access to include action-level enforcement, as Caulfield argues.<\/p>\n Cisco has developed a six-stage identity maturity model to govern agentic AI. Use it to assess your current state and plan your path to production. With 85% of enterprises running agent pilots and only 5% in production, aligning with maturity models is critical to closing the gap and securing your AI infrastructure.<\/p>\nThe Identity Maturity Model for AI Agents<\/h3>\n
Why 85% of Enterprises Are Struggling with Agent Pilots<\/h3>\n
\nThe Limitations of Current Access Control Models<\/h2>\n
Access Control vs. Action-Level Enforcement<\/h3>\n
Why Zero Trust Fails at the Agent Level<\/h3>\n
The Need for Real-Time Monitoring of AI Actions<\/h3>\n
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\nHow to Govern AI Agents: A Practical Comparison of Approaches<\/h2>\n
Human-Like Access Control vs. Machine-Scale Enforcement<\/h3>\n
Why Identity Maturity Models Are Critical<\/h3>\n
The Role of Action-Level Controls in AI Governance<\/h3>\n
\nImplementing AI Agent Governance: Practical Steps for Executives<\/h2>\n
Step 1: Define Clear Agent Identity and Access Policies<\/h3>\n
Step 2: Implement Action-Level Monitoring and Controls<\/h3>\n
Step 3: Integrate with Identity Maturity Models<\/h3>\n
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