Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

AI-assisted development helps teams write code faster, but that speed comes with added security risk. As agents generate more code, they can introduce vulnerabilities, insecure dependencies, or exposed secrets, often before a human reviewer ever sees the change. Security teams are left reviewing more code with the same resources, which makes it harder to catch issues early.

The NIST AI Risk Management Framework is a guide that helps organizations spot and reduce risks in AI systems. This framework was released in January 2023 by the U.S. National Institute of Standards and Technology. The framework is built around four key steps, namely: Govern, Map, Measure, and Manage, and is meant to help teams responsibly use AI. It doesn’t matter which industry you work in or which AI you use; this framework works everywhere.

An intern gets admin access to production for a temporary task, but nobody remembers to revoke it. Imagine that intern works at machine speed, never sleeps, and can chain dozens of actions before you’ve read the Slack ping—and has no instinct for when they’re about to do something irreversible.

As we close out the first quarter of 2026, I find myself reflecting on a start to the year that was defined by product momentum, stronger market validation, growing trust from regulated organizations, and meaningful industry recognition. In just three months, AlgoSec introduced important platform enhancements, published fresh research on where network security is heading, strengthened its standing with government and highly regulated customers, and closed the quarter with three major awards.

At Gartner IAM Summit 2026, the strongest conversations were about machine identities, AI agents, secrets, trusted integrations, and the growing realization that credential abuse now sits much closer to the center of enterprise risk.

The hype is deafening, the booths were packed, but most of what the industry is calling "agentic AI security" is point products wearing platform clothes. Here is what the real thing requires.

AI adoption does not happen uniformly across an organization. Some employees have integrated generative AI (genAI) tools into core parts of their workflow. Others have barely opened one. Most are somewhere in between, experimenting on an ad hoc basis, without consistent visibility into what data those tools handle or where it goes. That variance is the problem. Security programs built around either universal AI adoption or zero AI adoption will miss most of the actual risk.

For every human in a healthcare organization, there are 82 machine identities—service accounts, API keys, cloud functions, medical devices.2 That's the 82:1 ratio, and it means your team is fundamentally outnumbered. The Change Healthcare breach in 2024, which started with one unprotected Citrix credential and disrupted 40% of US claims processing,1 showed exactly what happens when that ratio goes unmanaged. The numbers back this up.