Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Everyone’s racing to build copilots right now. But making an agentic AI that feels like a trusted teammate—one that understands context, acts safely, and simplifies complex workflows—is harder than it looks. While building Nyx, our agentic AI copilot for security teams, our team spent a lot of time thinking about how to make her an effective team member - skilled and trustworthy.

Let’s be honest — nobody wakes up excited to read documentation. You’ve been there. You’re configuring a tricky workflow, testing an API, troubleshooting a weird corner case. And instead of finding the answer fast, you’re 12 tabs deep, elbows in a PDF appendix, hoping for a miracle. That’s not how it should be.

Today we’re excited to announce the launch of Apono’s new AI-powered Access Assistant, now live across the Apono Cloud Access Management Platform. As AI continues to transform engineering and security workflows, this assistant brings natural language interaction to access management. Helping teams move faster while staying secure. By eliminating the guesswork from access requests, Apono’s Access Assistant gives engineers a powerful new way to get exactly the access they need.

A new era in third-party cyber risk and exposure management is underway, one that operates in real time, informed by intelligence and scaled by automation. This shift wasn’t feasible even a few years ago. The scale, speed, and complexity of today’s threat landscape—spanning thousands of vendors, assets, and attack vectors—demand more than human capacity can manage. Artificial Intelligence is the catalyst making this new model possible.

AI agents are on the rise. They can spin up, act independently, use tools, and make decisions—often without real-time human oversight. They promise incredible productivity but also introduce new risks and challenges that can’t be ignored. As these agents become more autonomous and integrated into enterprise operations, they blur the lines between human and machine responsibilities. This raises critical questions: How do we ensure they act ethically?

Over the last two years, various systems and applications have been integrated with generative artificial intelligence (gen AI) capabilities, turning regular applications into gen-AI powered applications. In addition, retrieval augmented generation (RAG)-which is the process of connecting gen-AI and large language models (LLMs) to external knowledge sources-and other agents have been incorporated into such systems, making them more effective, accurate, and updated.