In the final episode of our series on governing AI agents, Kevin Werbach speaks with Harish Peri, SVP and General Manager for AI Security at Okta. Peri frames agent governance as the natural next chapter of what Okta has done for two decades: standing in the middle of people accessing technology. The twist is that the new "software" is a non-deterministic agent with a brain, which imposes a much higher security bar. He argues that agents live at the application layer, where the real question is one of authorization: is this agent allowed to take this action or access this data, at this moment, on behalf of this user, given all available signals? Much of the conversation explores why a neutral, independent control plane separate from the frontier models and agent runtimes matters from a cybersecurity standpoint, spreading risk across multiple layers rather than concentrating it in one place.

Peri notes that while awareness of rogue AI is universal, roughly 20% of agents carry about 80% of the risk. He distinguishes security threats like prompt injection and poisoned skill files from "intent mismatch," where an under-specified instruction such as "clean this up" gets read as "delete," and explains how coarse-grained limits, fine-grained context-based authorization, and selectively applied human-in-the-loop checks each play a role in agent governance.

Harish Peri is the SVP and General Manager for AI Security at Okta, where he leads product, go-to-market, and commercial strategy for securing agentic AI. He has more than 20 years of experience across engineering, product management, marketing, and general management, spanning financial services, technology, and human capital management, with prior roles at Salesforce, ADP, and Proxyclick. He holds an MBA from the Haas School of Business at UC Berkeley.

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The Future of AI Security: The Right Architecture for Agents

Secure Your Business Against AI Agents

Logan Kelly never set out to build an AI governance solution. Waxell spun out of CallSine, an AI-native sales engagement platform, when the team realized that agents that could act on their own produced a cascade of problems: burning through tokens, accessing databases, creating data-quality issues, and generally doing things no one had explicitly approved. Unable to find existing tooling that addressed the problems effectively, the team built a control plane for agents, which became the foundation of Waxell.

In this episode in our series on governing AI agents, CEO Logan Kelly emphasizes that governance should be legible to finance, legal, and compliance teams, not just developers. As he explains, agent governance is less about exotic AI risks than about visibility and control over things companies already care about, such as cost, data access, and who's allowed to do what. Kelly makes the case that the worst outcome isn't an agent misbehaving but companies losing trust in agents altogether and missing their value—arguing that every major technology, from cloud computing onward, arrived with new risks that good governance ultimately made manageable. Looking ahead, he bets that flexible, general-purpose governance will win out over locked-in "walled garden" platforms, as the pace of change in AI keeps accelerating..

Logan Kelly is the Co-Founder and CEO of Waxell, an AI governance and agent observability company that provides a control plane for operating autonomous AI agents in production. The company entered public beta in early 2026.

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Kevin Werbach speaks with Nadav Cornberg, co-founder and CEO of Eve Security, about securing agentic AI where it counts: at the moment an agent actually does something. He recounts how customers upended his own assumptions that AI agent security should focus on visibility and after-the-fact detection. Buyers insisted on runtime enforcement first, reasoning that learning a production database was deleted after the fact helps no one. With Eve's "interrogation" approach, when an agent attempts an anomalous, high-risk action, Eve's agent-in-the-loop pauses and questions it about its intent, before approving, blocking, or escalating to a human.

Cornberg describes building a deterministic enforcement layer on top of inherently non-deterministic models, with the system minting explicit rules from observed behavior so that the large majority of everyday requests resolve deterministically. Ultimately, the consequenes are the same whether an unintended action originates in a prompt injection or a simple hallucination. On the perennial human-in-the-loop question, Cornberg argues that escalating everything would drown security teams as the agentic workforce scales, so the platform automatically handles lower-risk cases with justification and reserves human review for the genuinely critical. He closes predicting that "agentic security" will fragment into distinct segments much as endpoint, network, and cloud security once did, and that intent is fast becoming the field's organizing idea.

Nadav Cornberg is the co-founder and CEO of Eve Security, an Austin-based agentic AI observability and policy-enforcement company whose platform governs how AI agents interact with an organization's most critical systems. He brings roughly two decades in product development and engineering, including an early decade in cybersecurity at RSA and Check Point and later work in physical access security across gaming and hospitality before returning to security for the agentic era.

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Founders' blog: Why We Started Eve Security