Companies are shipping AI systems into products faster than they are testing them for abuse. The model gets most of the attention, but the risk is bigger than the model. Prompts, data, app logic, APIs, and the user-facing code are all targets, too.
A model that writes code, answers customers, or approves transactions can be tricked, steered, copied, poisoned, or made to expose things it should not. You cannot patch a large language model the way you patch a server. You have to probe it, constrain it, and watch it.
The main attack classes are no longer theoretical. A jailbreak coaxes a model past its own guardrails into doing what it was told not to. Prompt injection slips hostile instructions into the content a model reads, turning the model against its operator.
Training data can be poisoned, so the model learns a behavior that the attacker wanted there. Attackers can copy a model by asking it enough questions to reproduce how it behaves, or push it off course with inputs built to confuse it. None of this looks like the traffic a firewall was built to judge.
AI red teaming means testing an AI system the way an attacker would: trying to break its rules, abuse its prompts, leak its data, or force unsafe behavior. That work is moving from research exercise toward production practice.
The 2026 Cybersecurity Stars Awards reflect the shift. Some winners focus on finding AI weaknesses before release. Others focus on catching abuse while the system is already running.
Straiker won in AI Security Testing for a continuous AI red-teaming platform built to find a model's weak points before attackers do. It was also named among the Best Cybersecurity Startup winners. The pairing is telling: testing AI systems for abuse is now its own category, not a feature buried inside general application security.
Testing finds the weak points. Runtime defense tries to stop them from becoming incidents.
DeepKeep won in Artificial Intelligence Security for a platform that secures the model itself and the data it runs on, the layers that a tool built for ordinary software would miss.
Impart Security took AI Runtime Defense, a category focused on controls that sit in front of a live AI system and watch for abusive prompts, jailbreak attempts, and other hostile requests as they happen.
Jscrambler was recognized in the same category for client-side security. That matters because the browser is where the AI app meets the user, and an exposed client gives attackers another place to tamper with the system.
Not every AI security problem is solved by another platform. Jeremy London of Keeper Security was named AI Security Innovator of the Year for his work on AI and threat analytics. Tools matter. So do the people who know how models break and how attackers think.
The rule for production AI is blunt: assume attackers will try to make the system ignore your rules. Test it before you ship. Watch it after it goes live. A model that has never been red-teamed is one whose weaknesses attackers will find first.
The complete list of 2026 Cybersecurity Stars Awards winners is live at awards.thehackernews.com/winners/2026.