Last November, C1 decided to go all in on AI internally — marketing, HR, every team. It didn't go the way anyone expected. CISO Kevin Paige tells the story of the company's director of marketing trying to follow a blog post to connect an AI workflow to Google's SEO tools. She needed an MCP server, which meant a GitHub account, which meant downloading a repository and installing Python, which she'd never heard of. Once the software was running, she still needed a Google service account, an enabled API, OAuth credentials, and her own human credentials just to let the whole chain talk to itself.
That cascade is the reason C1 rebuilt its core product around vaulting for both human and non-human identities, rather than treating agents as an afterthought bolted onto systems designed for people. "Every non-human identity needs to be treated like a human identity," Paige said. "It needs accountability, and somebody needs to understand why it exists."
The numbers back up the urgency. Early in 2026, C1's own Future of Identity survey put the human-to-agent ratio at roughly 44 to 1. Paige now expects that to flip within six to twelve months, not because AI adoption is accelerating gradually, but because a single employee spinning up 50 agents turns the ratio upside down overnight. And those agents aren't static: they spawn their own sub-agents to get work done, which pushes the real ratio toward something closer to 1 human for every 100 to 150 non-human identities.
Traditional password vaults, Paige points out, were built for a human to check out a credential once a week. Agents need access constantly, and giving them standing credentials just because that's how vaults have always worked creates the exact sprawl problem C1 is trying to solve. The fix, in Paige's view, is just-in-time access: an agent pulls a credential only when it needs one, only for as long as the task takes, built on cryptography designed with post-quantum migration already in mind.
Static policy isn't enough to manage that at agent speed, either. Paige argues that managing AI now genuinely requires AI: runtime monitoring, not just access rules checked periodically. C1's answer is what Paige calls an intent-aware proxy that sits inline between agents and the systems they touch, watching what both the agent and the human behind it are doing. If an agent tries something it shouldn't, the system can block it, fire a Slack alert, or escalate to a manager, all while preserving a clear line back to the human ultimately responsible.
That traceability matters for breach detection, too. C1 connects to a customer's identity provider and directly to the applications themselves, so it can compare what someone is authorized to do against what they're actually doing. A developer account suddenly acting with admin privileges, or an application accessed by bypassing the IDP entirely, gets caught and flagged in real time.
Paige's biggest frustration with how teams handle machine credentials today comes down to plain hygiene: developers create a credential for testing, mean to revoke it before production, and never do. "In my 30 years in security, I've never seen somebody go back," he said, "because it's moving too fast." His advice mirrors how security teams have always talked about passwords, just applied to a category of identity that didn't exist a few years ago: don't create a credential if you can avoid it, and if you do, give it a lifecycle from day one.
C1, formerly known as Conductor One, changed its name late last year once it became clear that securing agents was a fundamentally different problem than securing people. For developers and architects building agentic workflows now, Paige's advice is to get the governance building blocks in place early. "The governed path should be the fast path," he said. "You spend a little time up front getting it right, and then you accelerate down the road."
C1 customer Instacart offers a concrete look at what that governed path can look like in practice. The grocery delivery company's infrastructure security team used C1 to move 95% of privileged access to automated just-in-time provisioning, then went further: they built Gadjit, an open-source LLM-powered bot that reads C1's identity data to evaluate the remaining sensitive access requests that once required manual manager approval. Gadjit pulls context on an entitlement's risk level and who else holds it, scores whether a request looks legitimate, and approves or escalates accordingly, all logged in C1's audit trail. In testing, the bot's accuracy ran close to 100%, and the team points out it's already outperforming the industry norm of managers rubber-stamping requests they don't have time to evaluate. It's a small-scale, working version of the same principle Paige described: managing identity at agent speed increasingly requires AI managing AI, with humans reviewing the policy rather than approving every request by hand.
