Melissa Patenaude

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Articles by Melissa Patenaude

An exercise ended with frontier models inside a platform’s production systems, exposing a hard truth about what agents can do with credentials that systems trust.
Compare 10 identity security vendors for AI agents, including where each fits and what buyers should examine before choosing.
AI agents are workloads, but traditional workload identity alone can miss the user, task, and runtime context needed to govern dynamic agent access.
As AI agents begin calling tools and APIs, OAuth moves from background plumbing to a core access-control question.
AI agents need identity controls, scoped access, and runtime enforcement before they are trusted with production systems.
AI agents need more than working credentials. They need verifiable identity, task-scoped access, and clear attribution.
Visibility tells you what your agents are doing. Enforcement determines what they’re allowed to do. Here’s what the Aembit team saw at Identiverse that confirmed the gap.
Aembit now supports Microsoft Copilot Studio, giving security teams secure agent authentication to enterprise resources, least-privilege access at runtime, and a complete audit trail of every access event.
As AI moves from chat windows to enterprise systems, data leakage becomes an identity and access problem.
Your Azure Databricks pipelines need access to cloud and SaaS services, but they should not have to carry permanent credentials to get it.
Eliminating static API keys is real progress – but securing one credential surface is not the same as governing workload access at scale.
A working prototype can mask the harder problem: keeping every workload, agent, credential, policy, and audit trail consistent across production environments.