Research, benchmarks, and threat intelligence

Enterprise AI security playbooks consistently identify the same hard layers: runtime detection, telemetry, audit, and adversarial testing. These are where most programs fail.

AI agents are no longer chatbots. They execute tool calls, manage databases, transfer funds, and communicate with other agents often without human oversight. When an agent is compromised, the blast radius isn't a bad response.

Imagine an attacker sends five perfectly innocent messages to your AI system. Then, on message six, they ask something dangerous — phrased so naturally that it looks like a normal follow-up question.

The average cost of a data breach reached $4.88 million in 2024. Now, enterprises are connecting their most sensitive data directly to large language models. Every API call is a potential exfiltration vector.

Every AI firewall on the market today has the same fundamental problem: their detection layers don't talk to each other. A heuristic engine flags something suspicious. An ML classifier says it's safe.

Every AI security product on the market today falls into the same trap: they depend on a large language model to classify threats. Rebuff, Vigil, Guardrails AI, NeMo Guardrails. This works until it doesn't.

The security tools most companies rely on were designed for a world where humans use software. That world is disappearing fast. AI agents now retrieve documents, make decisions, and take actions across your systems.

A new standard called AIUC-1 launched, the first certification specifically designed to prove that AI agents are safe enough for enterprise use. It was built by security leaders from Anthropic, MITRE, Stanford, and MIT.

As enterprises deploy LLMs in production, a new security category has emerged: the AI firewall. Traditional security tools were not designed to detect threats that arrive as natural language.

An AI firewall is a security layer purpose-built to sit between users (or systems) and AI models. It intercepts every prompt and response in real time, analyzing them for threats that traditional security tools cannot detect.

The user's prompt is the least interesting attack vector in a production AI application. We call it the front door fallacy: prompt scanners protect one surface, direct user input, and leave everything else wide open.

In 2026, AI-driven attacks are costing enterprises billions of dollars annually. As organizations embed LLMs, chatbots, RAG pipelines, and agents into production, the attack surface has expanded far beyond traditional cybersecurity boundaries.